Confidence Intervals for Two Proportions

Size: px
Start display at page:

Download "Confidence Intervals for Two Proportions"

Transcription

1 PASS Samle Size Software Chater 6 Cofidece Itervals for Two Proortios Itroductio This routie calculates the grou samle sizes ecessary to achieve a secified iterval width of the differece, ratio, or odds ratio of two ideedet roortios. Cautio: These rocedures assume that the roortios obtaied from future samles will be the same as the roortios that are secified. If the samle roortios are differet from those secified whe ruig these rocedures, the iterval width may be arrower or wider tha secified. Four Procedures Documeted Here There are four rocedures i the meus described i this chater. These rocedures are very similar excet for the tye of arameterizatio. The arameterizatio ca be i terms of roortios, differeces i roortios, ratios of roortios, ad odds ratios. Techical Details A bacgroud of the comariso of two roortios is give, followed by details of the cofidece iterval methods available i this rocedure. Comarig Two Proortios Suose you have two oulatios from which dichotomous (biary) resoses will be recorded. The robability (or ris) of obtaiig the evet of iterest i oulatio (the treatmet grou) is ad i oulatio (the cotrol grou) is. The corresodig failure roortios are give by q ad q. The assumtio is made that the resoses from each grou follow a biomial distributio. This meas that the evet robability i is the same for all subjects withi a oulatio ad that the resoses from oe subject to the ext are ideedet of oe aother. Radom samles of m ad idividuals are obtaied from these two oulatios. The data from these samles ca be dislayed i a -by- cotigecy table as follows Success Failure Total Poulatio a c m Poulatio b d Totals s f N 6-

2 PASS Samle Size Software Cofidece Itervals for Two Proortios The followig alterative otatio is sometimes used: Success Failure Total Poulatio x x Poulatio x x Totals m m N The biomial roortios ad are estimated from these data usig the formulae a x m ad b x Whe aalyzig studies such as these, you usually wat to comare the two biomial robabilities ad. The most direct methods of comarig these quatities are to calculate their differece or their ratio. If the biomial robability is exressed i terms of odds rather tha robability, aother measure is the odds ratio. Mathematically, these comariso arameters are Parameter Comutatio Differece δ Ris Ratio φ / Odds Ratio ψ / q / q q q The choice of which of these measures is used might at seem arbitrary, but it is imortat. Not oly is their iterretatio differet, but, for small samle sizes, the coverage robabilities may be differet. Differece The (ris) differece δ is erhas the most direct method of comariso betwee the two evet robabilities. This arameter is easy to iterret ad commuicate. It gives the absolute imact of the treatmet. However, there are subtle difficulties that ca arise with its iterretatio. Oe iterretatio difficulty occurs whe the evet of iterest is rare. If a differece of 0.00 were reorted for a evet with a baselie robability of 0.40, we would robability dismiss this as beig of little imortace. That is, there usually little iterest i a treatmet that decreases the robability from to However, if the baselie robably of a disease was 0.00 ad 0.00 was the decrease i the disease robability, this would rereset a reductio of 50%. Thus we see that iterretatio deeds o the baselie robability of the evet. A similar situatio occurs whe the amout of ossible differece is cosidered. Cosider two evets, oe with a baselie evet rate of 0.40 ad the other with a rate of 0.0. What is the maximum decrease that ca occur? Obviously, the first evet rate ca be decreased by a absolute amout of 0.40 which the secod ca oly be decreased by a maximum of 0.0. So, although creatig the simle differece is a useful method of comariso, care must be tae that it fits the situatio. Ratio The (ris) ratio φ / gives the relative chage i the disease ris due to the alicatio of the treatmet. This arameter is also direct ad easy to iterret. To comare this with the differece, cosider a treatmet that reduces the ris of disease for to Which sigle umber is most elighteig, the fact that the 6-

3 PASS Samle Size Software Cofidece Itervals for Two Proortios absolute ris of disease has bee decreased by , or the fact that ris of disease i the treatmet grou is oly 55.8% of that i the cotrol grou? I may cases, the ercetage (ris ratio) commuicates the imact of the treatmet better tha the absolute chage. Perhas the biggest drawbac to this arameter is that it caot be calculated i oe of the most commo exerimetal desigs: the case-cotrol study. Odds Ratio Chaces are usually commuicated as log-term roortios or robabilities. I bettig, chaces are ofte give as odds. For examle, the odds of a horse wiig a race might be set at 0-to- or 3-to-. How do you traslate from odds to robability? A odds of 3-to- meas that the evet will occur three out of five times. That is, a odds of 3-to- (.5) traslates to a robability of wiig of The odds of a evet are calculated by dividig the evet ris by the o-evet ris. Thus, i our case of two oulatios, the odds are o ad o For examle, if is 0.60, the odds are 0.60/ Rather tha rereset the odds as a decimal amout, it is re-scaled ito whole umbers. Thus, istead of sayig the odds are.5-to-, we say they are 3-to-. Aother way to comare roortios is to comute the ratio of their odds. The odds ratio of two evets is ψ o o Although the odds ratio is more comlicated to iterret tha the ris ratio, it is ofte the arameter of choice. Reasos for this iclude the fact that the odds ratio ca be accurately estimated from case-cotrol studies, while the ris ratio caot. Also, the odds ratio is the basis of logistic regressio (used to study the ifluece of ris factors). Furthermore, the odds ratio is the atural arameter i the coditioal lielihood of the two-grou, biomial-resose desig. Fially, whe the baselie evet-rates are rare, the odds ratio rovides a close aroximatio to the ris ratio sice, i this case,, so that ψ φ Cofidece Itervals for the Differece May methods have bee devised for comutig cofidece itervals for the differece betwee two roortios δ. Seve of these methods are available i the Cofidece Itervals for Two Proortios [Proortios] usig Proortios ad Cofidece Itervals for Two Proortios [Differeces] rocedures. The seve cofidece iterval methods are. Score (Farrigto ad Maig). Score (Miettie ad Nurmie) 6-3

4 PASS Samle Size Software Cofidece Itervals for Two Proortios 3. Score with Correctio for Sewess (Gart ad Nam) 4. Score (Wilso) 5. Score with Cotiuity Correctio (Wilso) 6. Chi-Square with Cotiuity Correctio (Yates) 7. Chi-Square (Pearso) Newcombe (998b) coducted a comarative evaluatio of eleve cofidece iterval methods. He recommeded that the modified Wilso score method be used istead of the Pearso Chi-Square or the Yate s Corrected Chi- Square. Beal (987) foud that the Score methods erformed very well. The lower L ad uer U limits of these itervals are comuted as follows. Note that, uless otherwise stated, z z α / is the aroriate ercetile from the stadard ormal distributio. C.I. for Differece: Farrigto ad Maig s Score Farrigto ad Maig (990) roosed a test statistic for testig whether the differece is equal to a secified valueδ 0. The regular MLE s ad are used i the umerator of the score statistic while MLE s ad costraied so that δ 0 are used i the deomiator. The sigificace level of the test statistic is based o the asymtotic ormality of the score statistic. The test statistic formula is z FMD δ 0 q q where the estimates ad are comuted as i the corresodig test of Miettie ad Nurmie (985) give as δ 0 B cos ( A) A π cos 3 L 3L C B 3 3 B sig ( C) 3 L 9L L 3L L3 L L L L0 C 7 6 L L L3 ( ) [ N δ 0 N x] 0 ( N N ) 0 N 0 xδ 0 δ 0 L δ M L δ M L 3 N 3 6-4

5 PASS Samle Size Software Cofidece Itervals for Two Proortios Farrigto ad Maig (990) roosed ivertig their score test to fid the cofidece iterval. The lower limit is foud by solvig ad the uer limit is the solutio of z FMD z α / z FMD z α / C.I. for Differece: Miettie ad Nurmie s Score Miettie ad Nurmie (985) roosed a test statistic for testig whether the differece is equal to a secified valueδ 0. The regular MLE s ad are used i the umerator of the score statistic while MLE s ad costraied so that δ0 are used i the deomiator. A correctio factor of N/(N-) is alied to mae the variace estimate less biased. The sigificace level of the test statistic is based o the asymtotic ormality of the score statistic. The formula for comutig this test statistic is where δ 0 B cos ( A) A π cos 3 L 3L C B 3 3 z MND δ 0 q q N N B sig ( C) 3 L 9L L 3L L3 L L L L0 C 7 6 L L L3 ( ) [ N δ 0 N x] 0 ( N N ) 0 N 0 xδ 0 δ 0 L δ M L M L 3 δ N 3 Miettie ad Nurmie (985) roosed ivertig their score test to fid the cofidece iterval. The lower limit is foud by solvig ad the uer limit is the solutio of z z MND α / z MND z α / 6-5

6 PASS Samle Size Software Cofidece Itervals for Two Proortios C.I. for Differece: Gart ad Nam s Score Gart ad Nam (990) age 638 roosed a modificatio to the Farrigto ad Maig (990) differece test that δ stad for the Farrigto ad Maig differece test statistic described z FM corrected for sewess. Let ( ) above. The sewess corrected test statistic z GN is the aroriate solutio to the quadratic equatio where / V γ 6 ( δ ) q ( q ) q ( q ) 3 ( ) γ δ γ 0 ( ) zgnd ( ) zgnd zfmd( ) Gart ad Nam (988) roosed ivertig their score test to fid the cofidece iterval. The lower limit is foud by solvig ad the uer limit is the solutio of z GND z α / z GND z α / C.I. for Differece: Wilso s Score as Modified by Newcombe (with ad without Cotiuity Correctio) For details, see Newcombe (998b), age 876. where ( l ) u ( u ) l B z m ( u ) l ( l ) u C z m ad l ad u are the roots of ( ) z m ad l ad u are the roots of ( ) z 0 0 L B U C 6-6

7 PASS Samle Size Software Cofidece Itervals for Two Proortios 6-7 C.I. for Differece: Yate s Chi-Square with Cotiuity Correctio For details, see Newcombe (998b), age 875. m m z L ) ( ) ( m m z U ) ( ) ( C.I. for Differece: Pearso s Chi-Square For details, see Newcombe (998b), age 875. m z L ) ( ) ( m z U ) ( ) ( For each of the seve methods, oe-sided itervals may be obtaied by relacig α/ by α. For two-sided itervals, the distace from the differece i samle roortios to each of the limits may be differet. Thus, istead of secifyig the distace to the limits we secify the width of the iterval, W. The basic equatio for determiig samle size for a two-sided iterval whe W has bee secified is L U W For oe-sided itervals, the distace from the variace ratio to limit, D, is secified. The basic equatio for determiig samle size for a oe-sided uer limit whe D has bee secified is ( ) U D The basic equatio for determiig samle size for a oe-sided lower limit whe D has bee secified is ( ) L D Each of these equatios ca be solved for ay of the uow quatities i terms of the others. Cofidece Itervals for the Ratio (Relative Ris) May methods have bee devised for comutig cofidece itervals for the ratio (relative ris) of two roortios / φ. Six of these methods are available i the Cofidece Itervals for Two Proortios [Ratios] rocedure. The six cofidece iterval methods are. Score (Farrigto ad Maig). Score (Miettie ad Nurmie) 3. Score with Correctio for Sewess (Gart ad Nam) 4. Logarithm (Katz) 5. Logarithm / (Walter) 6. Fleiss

8 PASS Samle Size Software Cofidece Itervals for Two Proortios C.I. for Ratio: Farrigto ad Maig s Score Farrigto ad Maig (990) roosed a test statistic for testig whether the ratio is equal to a secified value φ 0. The regular MLE s ad are used i the umerator of the score statistic while MLE s ad costraied so that / φ0 are used i the deomiator. A correctio factor of N/(N-) is alied to icrease the variace estimate. The sigificace level of the test statistic is based o the asymtotic ormality of the score statistic. Here is the formula for comutig the test where φ 0 B A Nφ 0 B 4AC A z FMR [ N φ x N x ] B C M 0 φ0 as i the test of Miettie ad Nurmie (985). / φ0 q q φ0 Farrigto ad Maig (990) roosed ivertig their score test to fid the cofidece iterval. The lower limit is foud by solvig ad the uer limit is the solutio of z FMR z α / z FMR z α / C.I. for Ratio: Miettie ad Nurmie s Score Miettie ad Nurmie (985) roosed a test statistic for testig whether the ratio is equal to a secified value φ 0. The regular MLE s ad are used i the umerator of the score statistic while MLE s ad costraied so that / φ0 are used i the deomiator. A correctio factor of N/(N-) is alied to mae the variace estimate less biased. The sigificace level of the test statistic is based o the asymtotic ormality of the score statistic. Here is the formula for comutig the test z MNR / φ0 q q N φ0 N 6-8

9 PASS Samle Size Software where φ 0 Cofidece Itervals for Two Proortios B A Nφ 0 B 4AC A [ N φ x N x ] B C M 0 φ0 Miettie ad Nurmie (985) roosed ivertig their score test to fid the cofidece iterval. The lower limit is foud by solvig ad the uer limit is the solutio of z z MNR α / z MNR z α / C.I. for Ratio: Gart ad Nam s Score Gart ad Nam (988) age 39 roosed a modificatio to the Farrigto ad Maig (988) ratio test that corrected for sewess. Let z FM ( φ ) stad for the Farrigto ad Maig ratio test statistic described above. The sewess corrected test statistic z GN is the aroriate solutio to the quadratic equatio ϕ z z z φ ϕ 0 where ( q ) q ( q ) q ϕ 3 / 6 u q q u ( ) ( ) GNR ( ) GNR FMR( ) Gart ad Nam (988) roosed ivertig their score test to fid the cofidece iterval. The lower limit is foud by solvig ad the uer limit is the solutio of z z GNR α / zgnr z α / 6-9

10 PASS Samle Size Software Cofidece Itervals for Two Proortios C.I. for Ratio: Logarithm (Katz) This was oe of the first methods roosed for comutig cofidece itervals for ris ratios. For details, see Gart ad Nam (988), age 34. L φ ex z q q U φ ex z q q where φ C.I. for Ratio: Logarithm (Walters) For details, see Gart ad Nam (988), age 34. where a φ ex l m b l u a m b q q V φ m φ q q q ( q ) ( m ) ( q ) ( ) 3/ q q µ 3 v v m q q L φ ex U φ ex ( z u ) ( z u ) 6-0

11 PASS Samle Size Software Cofidece Itervals for Two Proortios C.I. for Odds Ratio ad Relative Ris: Iterated Method of Fleiss Fleiss (98) resets a imroved cofidece iterval for the odds ratio ad relative ris. This method forms the cofidece iterval as all those value of the odds ratio which would ot be rejected by a chi-square hyothesis test. Fleiss gives the followig details about how to costruct this cofidece iterval. To comute the lower limit, do the followig.. For a trial value of ψ, comute the quatities X, Y, W, F, U, ad V usig the formulas X ψ ( m s) ( s) Y X 4msψ ψ X Y A ψ ( ) B s A C m A D f m A ( ) W A B C D F ( a A ) W ( ψ ) z α / ψ T Y ψ Y U B C A D [( a A ) ( )] U W a V T A [ X ( m s) ms( )] Fially, use the udatig equatio below to calculate a ew value for the odds ratio usig the udatig equatio ( ) ( ) F ψ V ψ. Cotiue iteratig util the value of F is arbitrarily close to zero. The uer limit is foud by substitutig for i the formulas for F ad V. Cofidece limits for the relative ris ca be calculated usig the exected couts A, B, C, ad D from the last iteratio of the above rocedure. The lower limit of the relative ris φ lower φ uer A B A B lower lower uer uer m m 6-

12 PASS Samle Size Software Cofidece Itervals for Two Proortios 6- Cofidece Itervals for the Odds Ratio May methods have bee devised for comutig cofidece itervals for the odds ratio of two roortios ψ Eight of these methods are available i the Cofidece Itervals for Two Proortios [Odds Ratios] rocedure. The eight cofidece iterval methods are. Exact (Coditioal). Score (Farrigto ad Maig) 3. Score (Miettie ad Nurmie) 4. Fleiss 5. Logarithm 6. Matel-Haeszel 7. Simle 8. Simle / C.I. for Odds Ratio: Coditioal Exact The coditioal exact cofidece iterval of the odds ratio is calculated usig the ocetral hyergeometric distributio as give i Sahai ad Khurshid (995). That is, a ( ) 00 α % cofidece iterval is foud by searchig for ψ L ad ψ U such that ( ) ( ) α ψ ψ L x L m m ad ( ) ( ) α ψ ψ U x U m m where ( ), 0 max m ad ( ), mi m

13 PASS Samle Size Software Cofidece Itervals for Two Proortios 6-3 Farrigto ad Maig s Test of the Odds Ratio Farrigto ad Maig (990) develoed a test statistic similar to that of Miettie ad Nurmie but with the factor N/(N-) removed. The formula for comutig this test statistic is ( ) ( ) q N q N q q z FMO where the estimates ad are comuted as i the corresodig test of Miettie ad Nurmie (985) as ( ) 0 0 ψ ψ A AC B B 4 ( ) 0 ψ N A ( ) 0 0 ψ ψ M N N B C M Farrigto ad Maig (990) roosed ivertig their score test to fid the cofidece iterval. The lower limit is foud by solvig z α / z FMO ad the uer limit is the solutio of z FMO z α / C.I. for Odds Ratio: Miettie ad Nurmie Miettie ad Nurmie (985) roosed a test statistic for testig whether the odds ratio is equal to a secified valueψ 0. Because the aroach they used with the differece ad ratio does ot easily exted to the odds ratio, they used a score statistic aroach for the odds ratio. The regular MLE s are ad. The costraied MLE s are ad, These estimates are costraied so that ψ ψ 0. A correctio factor of N/(N-) is alied to mae the variace estimate less biased. The sigificace level of the test statistic is based o the asymtotic ormality of the score statistic. The formula for comutig the test statistic is ( ) ( ) N N q N q N q q z MNO where ( ) 0 0 ψ ψ

14 PASS Samle Size Software Cofidece Itervals for Two Proortios B A N ψ ( ) 0 B 4AC A B N ψ C M ( ) ψ 0 N M 0 Miettie ad Nurmie (985) roosed ivertig their score test to fid the cofidece iterval. The lower limit is foud by solvig ad the uer limit is the solutio of z MNO z α / z MNO z α / C.I. for Odds Ratio: Iterated Method of Fleiss Fleiss (98) resets a imrove cofidece iterval for the odds ratio. This method forms the cofidece iterval as all those value of the odds ratio which would ot be rejected by a chi-square hyothesis test. Fleiss gives the followig details about how to costruct this cofidece iterval. To comute the lower limit, do the followig.. For a trial value of ψ, comute the quatities X, Y, W, F, U, ad V usig the formulas X ψ ( m s) ( s) Y X 4msψ ψ X Y A ψ ( ) B s A C m A D f m A ( ) W A B C D F ( a A ) W ( ψ ) z α / ψ T Y.. ψ Y U B C A D [( a A ) ( )] U W a V T A [ X ( m s) ms( )] 6-4

15 PASS Samle Size Software Cofidece Itervals for Two Proortios Fially, use the udatig equatio below to calculate a ew value for the odds ratio usig the udatig equatio ( ) ( ) F ψ V ψ. Cotiue iteratig util the value of F is arbitrarily close to zero. The uer limit is foud by substitutig for i the formulas for F ad V. Cofidece limits for the relative ris ca be calculated usig the exected couts A, B, C, ad D from the last iteratio of the above rocedure. The lower limit of the relative ris φ lower φ uer A B A B lower lower uer uer m m C.I. for Odds Ratio: Matel-Haeszel The commo estimate of the logarithm of the odds ratio is used to create this estimator. That is ad bc ( ) l l ψ The stadard error of this estimator is estimated usig the Robis, Breslow, Greelad (986) estimator which erforms well i most situatios. The stadard error is give by where a d A N b c B N ad C N bc D N The cofidece limits are calculated as A AD BC se ( l( ψ )) C CD B D ( l( ψ ) z se( l( ψ ))) lower / ψ ex α ( ( ) α / ( ( ))) ψ ex l ψ l ψ uer z se 6-5

16 PASS Samle Size Software Cofidece Itervals for Two Proortios C.I. for Odds Ratio: Simle, Simle ½, ad Logarithm The simle estimate of the odds ratio uses the formula The stadard error of this estimator is estimated by q ψ q ad bc se( ψ ) ψ a b c d Problems occur if ay oe of the quatities a, b, c, or d are zero. To correct this roblem, may authors recommed addig oe-half to each cell cout so that a zero caot occur. Now, the formulas become ad se ( ψ ) ψ ψ ( a 0.5)( d 0.5) ( b 0.5)( c 0.5) a 0.5 b 0.5 c 0.5 d 0.5 The distributio of these direct estimates of the odds ratio do ot coverge to ormality as fast as does their logarithm, so the logarithm of the odds ratio is used to form cofidece itervals. The formula for the stadard error of the log odds ratio is ad se ( L ) a 0.5 ( ψ ) L l b 0.5 c 0.5 d 0.5 A 00( α )% cofidece iterval for the log odds ratio is formed usig the stadard ormal distributio as follows See Fleiss et al (003) for more details. ψ ψ ( L z α / se( L )) ( L z se( L )) lower ex uer ex α / Cofidece Level The cofidece level, α, has the followig iterretatio. If thousads of radom samles of size ad are draw from oulatios ad, resectively, ad a cofidece iterval for the true differece/ratio/odds ratio of roortios is calculated for each air of samles, the roortio of those itervals that will iclude the true differece/ratio/odds ratio of roortios is α. 6-6

17 PASS Samle Size Software Procedure Otios Cofidece Itervals for Two Proortios This sectio describes the otios that are secific to this rocedure. These are located o the Desig tab. For more iformatio about the otios of other tabs, go to the Procedure Widow chater. Desig Tab (Commo Otios) This chater covers four rocedures, each of which has differet otios. This sectio documets otios that are commo to all four rocedures. Followig this sectio, the uique otios for each rocedure (roortios, differeces, ratios, ad odds ratios) will be documeted. Solve For Solve For This otio secifies the arameter to be solved for from the other arameters. Oe-Sided or Two-Sided Iterval Iterval Tye Secify whether the iterval to be used will be a two-sided cofidece iterval, a iterval that has oly a uer limit, or a iterval that has oly a lower limit. Cofidece Cofidece Level The cofidece level, α, has the followig iterretatio. If thousads of radom samles of size ad are draw from oulatios ad, resectively, ad a cofidece iterval for the true differece/ratio/odds ratio of roortios is calculated for each air of samles, the roortio of those itervals that will iclude the true differece/ratio/odds ratio of roortios is α. Ofte, the values 0.95 or 0.99 are used. You ca eter sigle values or a rage of values such as 0.90, 0.95 or 0.90 to 0.99 by 0.0. Samle Size (Whe Solvig for Samle Size) Grou Allocatio Select the otio that describes the costraits o N or N or both. The otios are Equal (N N) This selectio is used whe you wish to have equal samle sizes i each grou. Sice you are solvig for both samle sizes at oce, o additioal samle size arameters eed to be etered. Eter N, solve for N Select this otio whe you wish to fix N at some value (or values), ad the solve oly for N. Please ote that for some values of N, there may ot be a value of N that is large eough to obtai the desired ower. Eter N, solve for N Select this otio whe you wish to fix N at some value (or values), ad the solve oly for N. Please ote that for some values of N, there may ot be a value of N that is large eough to obtai the desired ower. 6-7

18 PASS Samle Size Software Cofidece Itervals for Two Proortios Eter R N/N, solve for N ad N For this choice, you set a value for the ratio of N to N, ad the PASS determies the eeded N ad N, with this ratio, to obtai the desired ower. A equivalet reresetatio of the ratio, R, is N R * N. Eter ercetage i Grou, solve for N ad N For this choice, you set a value for the ercetage of the total samle size that is i Grou, ad the PASS determies the eeded N ad N with this ercetage to obtai the desired ower. N (Samle Size, Grou ) This otio is dislayed if Grou Allocatio Eter N, solve for N N is the umber of items or idividuals samled from the Grou oulatio. N must be. You ca eter a sigle value or a series of values. N (Samle Size, Grou ) This otio is dislayed if Grou Allocatio Eter N, solve for N N is the umber of items or idividuals samled from the Grou oulatio. N must be. You ca eter a sigle value or a series of values. R (Grou Samle Size Ratio) This otio is dislayed oly if Grou Allocatio Eter R N/N, solve for N ad N. R is the ratio of N to N. That is, R N / N. Use this value to fix the ratio of N to N while solvig for N ad N. Oly samle size combiatios with this ratio are cosidered. N is related to N by the formula: where the value [Y] is the ext iteger Y. N [R N], For examle, settig R.0 results i a Grou samle size that is double the samle size i Grou (e.g., N 0 ad N 0, or N 50 ad N 00). R must be greater tha 0. If R <, the N will be less tha N; if R >, the N will be greater tha N. You ca eter a sigle or a series of values. Percet i Grou This otio is dislayed oly if Grou Allocatio Eter ercetage i Grou, solve for N ad N. Use this value to fix the ercetage of the total samle size allocated to Grou while solvig for N ad N. Oly samle size combiatios with this Grou ercetage are cosidered. Small variatios from the secified ercetage may occur due to the discrete ature of samle sizes. The Percet i Grou must be greater tha 0 ad less tha 00. You ca eter a sigle or a series of values. 6-8

19 PASS Samle Size Software Cofidece Itervals for Two Proortios Samle Size (Whe Not Solvig for Samle Size) Grou Allocatio Select the otio that describes how idividuals i the study will be allocated to Grou ad to Grou. The otios are Equal (N N) This selectio is used whe you wish to have equal samle sizes i each grou. A sigle er grou samle size will be etered. Eter N ad N idividually This choice ermits you to eter differet values for N ad N. Eter N ad R, where N R * N Choose this otio to secify a value (or values) for N, ad obtai N as a ratio (multile) of N. Eter total samle size ad ercetage i Grou Choose this otio to secify a value (or values) for the total samle size (N), obtai N as a ercetage of N, ad the N as N - N. Samle Size Per Grou This otio is dislayed oly if Grou Allocatio Equal (N N). The Samle Size Per Grou is the umber of items or idividuals samled from each of the Grou ad Grou oulatios. Sice the samle sizes are the same i each grou, this value is the value for N, ad also the value for N. The Samle Size Per Grou must be. You ca eter a sigle value or a series of values. N (Samle Size, Grou ) This otio is dislayed if Grou Allocatio Eter N ad N idividually or Eter N ad R, where N R * N. N is the umber of items or idividuals samled from the Grou oulatio. N must be. You ca eter a sigle value or a series of values. N (Samle Size, Grou ) This otio is dislayed oly if Grou Allocatio Eter N ad N idividually. N is the umber of items or idividuals samled from the Grou oulatio. N must be. You ca eter a sigle value or a series of values. R (Grou Samle Size Ratio) This otio is dislayed oly if Grou Allocatio Eter N ad R, where N R * N. R is the ratio of N to N. That is, R N/N Use this value to obtai N as a multile (or roortio) of N. N is calculated from N usig the formula: where the value [Y] is the ext iteger Y. N[R x N], 6-9

20 PASS Samle Size Software Cofidece Itervals for Two Proortios For examle, settig R.0 results i a Grou samle size that is double the samle size i Grou. R must be greater tha 0. If R <, the N will be less tha N; if R >, the N will be greater tha N. You ca eter a sigle value or a series of values. Total Samle Size (N) This otio is dislayed oly if Grou Allocatio Eter total samle size ad ercetage i Grou. This is the total samle size, or the sum of the two grou samle sizes. This value, alog with the ercetage of the total samle size i Grou, imlicitly defies N ad N. The total samle size must be greater tha oe, but ractically, must be greater tha 3, sice each grou samle size eeds to be at least. You ca eter a sigle value or a series of values. Percet i Grou This otio is dislayed oly if Grou Allocatio Eter total samle size ad ercetage i Grou. This value fixes the ercetage of the total samle size allocated to Grou. Small variatios from the secified ercetage may occur due to the discrete ature of samle sizes. The Percet i Grou must be greater tha 0 ad less tha 00. You ca eter a sigle value or a series of values. Desig Tab (Proortios) This sectio documets otios that are used whe the arameterizatio is i terms of the values of the two samle roortios, P ad P. The corresodig rocedure is Cofidece Itervals for the Differece betwee Two Proortios usig Proortios. Cofidece Iterval Method Cofidece Iterval Formula Secify the formula to be i used i calculatio of cofidece itervals. Score (Farrigto & Maig) This formula is based o ivertig Farrigto ad Maig's score test. Score (Miettie & Nurmie) This formula is based o ivertig Miettie ad Nurmie's score test. Score w/ Sewess (Gart & Nam) This formula is based o ivertig Gart ad Nam's score test, with a correctio for sewess. Score (Wilso) This formula is based o the Wilso score method for a sigle roortio, without cotiuity correctio. Score (Wilso C.C.) This formula is based o the Wilso score method for a sigle roortio, with cotiuity correctio. Chi-Square C.C. (Yates) This is the commoly used simle asymtotic method, with cotiuity correctio. 6-0

21 PASS Samle Size Software Cofidece Itervals for Two Proortios Chi-Square (Pearso) This is the commoly used simle asymtotic method, without cotiuity correctio. Precisio Cofidece Iterval Width (Two-Sided) This is the distace from the lower cofidece limit to the uer cofidece limit. You ca eter a sigle value or a list of values. The value(s) must be greater tha zero. Distace from Diff to Limit (Oe-Sided) This is the distace from the differece i samle roortios to the lower or uer limit of the cofidece iterval, deedig o whether the Iterval Tye is set to Lower Limit or Uer Limit. You ca eter a sigle value or a list of values. The value(s) must be greater tha zero. Proortios (Differece P P) P (Proortio Grou ) Eter a estimate of the roortio for grou. The samle size ad width calculatios assume that the value etered here is the roortio estimate that is obtaied from the samle. If the samle roortio is differet from the oe secified here, the width may be arrower or wider tha secified. The value(s) must be betwee ad You ca eter a rage of values such as...3 or. to.5 by.. P (Proortio Grou ) Eter a estimate of the roortio for grou. The samle size ad width calculatios assume that the value etered here is the roortio estimate that is obtaied from the samle. If the samle roortio is differet from the oe secified here, the width may be arrower or wider tha secified. The value(s) must be betwee ad You ca eter a rage of values such as...3 or. to.5 by.. Desig Tab (Differeces) This sectio documets otios that are used whe the arameterizatio is i terms of the differece i samle roortios ad the value of the secod samle roortio, P. The corresodig rocedure is Cofidece Itervals for the Differece betwee Two Proortios usig Differeces. Cofidece Iterval Method Cofidece Iterval Formula Secify the formula to be i used i calculatio of cofidece itervals. Score (Farrigto & Maig) This formula is based o ivertig Farrigto ad Maig's score test. Score (Miettie & Nurmie) This formula is based o ivertig Miettie ad Nurmie's score test. 6-

22 PASS Samle Size Software Cofidece Itervals for Two Proortios Score w/ Sewess (Gart & Nam) This formula is based o ivertig Gart ad Nam's score test, with a correctio for sewess. Score (Wilso) This formula is based o the Wilso score method for a sigle roortio, without cotiuity correctio. Score (Wilso C.C.) This formula is based o the Wilso score method for a sigle roortio, with cotiuity correctio. Chi-Square C.C. (Yates) This is the commoly used simle asymtotic method, with cotiuity correctio. Chi-Square (Pearso) This is the commoly used simle asymtotic method, without cotiuity correctio. Precisio Cofidece Iterval Width (Two-Sided) This is the distace from the lower cofidece limit to the uer cofidece limit. You ca eter a sigle value or a list of values. The value(s) must be greater tha zero. Distace from Diff to Limit (Oe-Sided) This is the distace from the differece i samle roortios to the lower or uer limit of the cofidece iterval, deedig o whether the Iterval Tye is set to Lower Limit or Uer Limit. You ca eter a sigle value or a list of values. The value(s) must be greater tha zero. Proortios (Differece P P) Differece i Samle Proortios Eter a estimate of the differece betwee samle roortio ad samle roortio. The samle size ad width calculatios assume that the value etered here is the differece estimate that is obtaied from the samle. If the samle differece is differet from the oe secified here, the width may be arrower or wider tha secified. The value(s) must be betwee - ad, ad such that P Differece P is betwee ad You ca eter a rage of values such as...3 or. to.5 by.. P (Proortio Grou ) Eter a estimate of the roortio for grou. The samle size ad width calculatios assume that the value etered here is the roortio estimate that is obtaied from the samle. If the samle roortio is differet from the oe secified here, the width may be arrower or wider tha secified. The value(s) must be betwee ad You ca eter a rage of values such as...3 or. to.5 by.. Desig Tab (Ratios) This sectio documets otios that are used whe the arameterizatio is i terms of the ratio of samle roortios ad the value of the secod samle roortio, P. The corresodig rocedure is Cofidece Itervals for the Differece betwee Two Proortios usig Ratios. 6-

23 PASS Samle Size Software Cofidece Itervals for Two Proortios Cofidece Iterval Method Cofidece Iterval Formula Secify the formula to be i used i calculatio of cofidece itervals. Score (Farrigto & Maig) This formula is based o ivertig Farrigto ad Maig's score test. Score (Miettie & Nurmie) This formula is based o ivertig Miettie ad Nurmie's score test. Score w/ Sewess (Gart & Nam) This formula is based o ivertig Gart ad Nam's score test, with a correctio for sewess. Logarithm (Katz) This formula is based o the asymtotic ormality of log(p/p). Logarithm / (Walter) This formula is based o the asymtotic ormality of log(p/p), but / is used as a adjustmet. Fleiss This is a iterative method that was develoed for the odds ratio ad adated to the roortio ratio. Precisio Cofidece Iterval Width (Two-Sided) This is the distace from the lower cofidece limit to the uer cofidece limit. You ca eter a sigle value or a list of values. The value(s) must be greater tha zero. Distace from Ratio to Limit (Oe-Sided) This is the distace from the ratio of samle roortios to the lower or uer limit of the cofidece iterval, deedig o whether the Iterval Tye is set to Lower Limit or Uer Limit. You ca eter a sigle value or a list of values. The value(s) must be greater tha zero. Proortios (Ratio P/P) Ratio of Samle Proortios Eter a estimate of the ratio of samle roortio to samle roortio. The samle size ad width calculatios assume that the value etered here is the ratio estimate that is obtaied from the samles. If the samle ratio is differet from the oe secified here, the width may be arrower or wider tha secified. The value(s) must be greater tha 0, ad such that P Ratio * P is betwee ad You ca eter a rage of values such as or.5 to.9 by.. P (Proortio Grou ) Eter a estimate of the roortio for grou. The samle size ad width calculatios assume that the value etered here is the roortio estimate that is obtaied from the samle. If the samle roortio is differet from the oe secified here, the width may be arrower or wider tha secified. The value(s) must be betwee ad

24 PASS Samle Size Software Cofidece Itervals for Two Proortios You ca eter a rage of values such as...3 or. to.5 by.. Desig Tab (Odds Ratios) This sectio documets otios that are used whe the arameterizatio is i terms of the odds ratio ad the value of the secod samle roortio, P. The corresodig rocedure is Cofidece Itervals for the Differece betwee Two Proortios usig Odds Ratios. Cofidece Iterval Method Cofidece Iterval Formula Secify the formula to be i used i calculatio of cofidece itervals. Exact (Coditioal) This coditioal exact cofidece iterval formula is calculated usig the o-cetral hyergeometric distributio. Score (Farrigto & Maig) This formula is based o ivertig Farrigto ad Maig's score test. Score (Miettie & Nurmie) This formula is based o ivertig Miettie ad Nurmie's score test. Fleiss This iterative method forms the cofidece iterval as all those value of the odds ratio which would ot be rejected by a chi-square hyothesis test. Logarithm This formula is similar to SIMPLE /, but with the logarithm of the odds ratio. Matel- Haeszel This formula is based o the Matel-Haeszel formula for the odds ratio. Simle This uses the simle odds ratio formula ad large samle stadard error estimate. Simle / This uses the simle odds ratio formula ad large samle stadard error estimate, but with / added to frequecies as a bias reductio device. Precisio Cofidece Iterval Width (Two-Sided) This is the distace from the lower cofidece limit to the uer cofidece limit. You ca eter a sigle value or a list of values. The value(s) must be greater tha zero. Distace from OR to Limit (Oe-Sided) This is the distace from the odds ratio to the lower or uer limit of the cofidece iterval, deedig o whether the Iterval Tye is set to Lower Limit or Uer Limit. You ca eter a sigle value or a list of values. The value(s) must be greater tha zero. 6-4

25 PASS Samle Size Software Proortios (OR O/O) Cofidece Itervals for Two Proortios Odds Ratio Eter a estimate of the samle odds ratio (O/O). The samle size ad width calculatios assume that the value etered here is the odds ratio estimate that is obtaied from the samles. If the samle odds ratio is differet from the oe secified here, the width may be arrower or wider tha secified. The value(s) must be greater tha 0. You ca eter a rage of values such as or.5 to.9 by.. P (Proortio Grou ) Eter a estimate of the roortio for grou. The samle size ad width calculatios assume that the value etered here is the roortio estimate that is obtaied from the samle. If the samle roortio is differet from the oe secified here, the width may be arrower or wider tha secified. The value(s) must be betwee ad You ca eter a rage of values such as...3 or. to.5 by.. Examle Calculatig Samle Size usig Proortios Suose a study is laed i which the researcher wishes to costruct a two-sided 95% cofidece iterval for the differece i roortios such that the width of the iterval is o wider tha 0.. The cofidece iterval method to be used is the Yates chi-square simle asymtotic method with cotiuity correctio. The cofidece level is set at 0.95, but 0.99 is icluded for comarative uroses. The roortio estimates to be used are 0.6 for Grou, ad 0.4 for Grou. Istead of examiig oly the iterval width of 0., a series of widths from 0.05 to 0.3 will also be cosidered. The goal is to determie the ecessary samle size. Setu This sectio resets the values of each of the arameters eeded to ru this examle. First, from the PASS Home widow, load the Cofidece Itervals for Two Proortios usig Proortios rocedure widow by exadig Proortios, the Two Ideedet Proortios, the clicig o Cofidece Iterval, ad the clicig o Cofidece Itervals for Two Proortios usig Proortios. You may the mae the aroriate etries as listed below, or oe Examle by goig to the File meu ad choosig Oe Examle Temlate. Otio Value Desig Tab Solve For... Samle Size Cofidece Iterval Formula... Chi-Square C.C. (Yates) Iterval Tye... Two-Sided Cofidece Level Grou Allocatio... Equal (N N) Cofidece Iterval Width (Two-Sided) to 0.30 by 0.05 P P

26 PASS Samle Size Software Cofidece Itervals for Two Proortios Aotated Outut Clic the Calculate butto to erform the calculatios ad geerate the followig outut. Numeric Results Numeric Results for Two-Sided Cofidece Itervals for the Differece i Proortios Cofidece Iterval Method: Chi-Square - Simle Asymtotic with Cotiuity Correctio (Yates) Cofidece Target Actual Lower Uer Level N N N Width Width P P P - P Limit Limit Refereces Newcombe, R. G 'Iterval Estimatio for the Differece Betwee Ideedet Proortios: Comariso of Eleve Methods.' Statistics i Medicie, 7, Fleiss, J. L., Levi, B., Pai, M.C Statistical Methods for Rates ad Proortios. Third Editio. Joh Wiley & Sos. New Yor. Reort Defiitios Cofidece level is the roortio of cofidece itervals (costructed with this same cofidece level, samle size, etc.) that would cotai the true differece i oulatio roortios. N ad N are the umber of items samled from each oulatio. N is the total samle size, N N. Target Width is the value of the width that is etered ito the rocedure. Actual Width is the value of the width that is obtaied from the rocedure. P ad P are the assumed samle roortios for samle size calculatios. P - P is the differece betwee samle roortios at which samle size calculatios are made. Lower Limit ad Uer Limit are the lower ad uer limits of the cofidece iterval for the true differece i roortios (Poulatio Proortio - Poulatio Proortio ). Summary Statemets Grou samle sizes of 3030 ad 3030 roduce a two-sided 95% cofidece iterval for the differece i oulatio roortios with a width that is equal to whe the estimated samle roortio is 0.60 ad the estimated samle roortio is This reort shows the calculated samle sizes for each of the scearios. 6-6

27 PASS Samle Size Software Plots Sectio Cofidece Itervals for Two Proortios These lots show the grou samle size versus the cofidece iterval width for the two cofidece levels. 6-7

28 PASS Samle Size Software Cofidece Itervals for Two Proortios Examle Validatio (Proortios ad Differeces) usig Newcombe Newcombe (998b) age 877 gives a examle of a calculatio for a cofidece iterval for the differece i roortios whe the cofidece level is 95%, the samle roortios are 0.9 ad 0.3, ad the iterval width is for the Chi-Square (Pearso) method, for the Chi-Square C.C. (Yates) method, for the Score (Miettie ad Nurmie) method, for the Score (Wilso) method, ad for the Score C.C. (Wilso) method. The ecessary samle size i each case is 0 er grou. Setu This sectio resets the values of each of the arameters eeded to ru this examle. First, from the PASS Home widow, load the Cofidece Itervals for Two Proortios usig Proortios rocedure widow by exadig Proortios, the Two Ideedet Proortios, the clicig o Cofidece Iterval, ad the clicig o Cofidece Itervals for Two Proortios usig Proortios. You may the mae the aroriate etries as listed below, or oe Examle by goig to the File meu ad choosig Oe Examle Temlate. Otio Value Desig Tab Solve For... Samle Size Cofidece Iterval Formula... Varies [Chi-Square (Pearso), Chi-Square C.C. (Yates), Score (Miettie & Nurmie), Score (Wilso), Score C.C. (Wilso)] Iterval Tye... Two-Sided Cofidece Level Grou Allocatio... Equal (N N) Cofidece Iterval Width (Two-Sided).. Varies (0.6790, , , , ) P P Outut Clic the Calculate butto to erform the calculatios ad geerate the followig outut. Chi-Square (Pearso) Cofidece Target Actual Lower Uer Level N N N Width Width P P P - P Limit Limit PASS also calculates the ecessary samle size to be 0 er grou. Chi-Square C.C. (Yates) Cofidece Target Actual Lower Uer Level N N N Width Width P P P - P Limit Limit PASS also calculates the ecessary samle size to be 0 er grou. 6-8

29 PASS Samle Size Software Cofidece Itervals for Two Proortios Score (Miettie & Nurmie) Cofidece Target Actual Lower Uer Level N N N Width Width P P P - P Limit Limit PASS also calculates the ecessary samle size to be 0 er grou. Score (Wilso) Cofidece Target Actual Lower Uer Level N N N Width Width P P P - P Limit Limit PASS also calculates the ecessary samle size to be 0 er grou. Score C.C. (Wilso) Cofidece Target Actual Lower Uer Level N N N Width Width P P P - P Limit Limit PASS also calculates the ecessary samle size to be 0 er grou. 6-9

30 PASS Samle Size Software Cofidece Itervals for Two Proortios Examle 3 Validatio (Proortios ad Differeces) usig Gart ad Nam Gart ad Nam (990) age 640 give a examle of a calculatio for a cofidece iterval for the differece i roortios whe the cofidece level is 95%, the samle roortios are 0.8 ad 0.08, ad the iterval width is 0.48 for the Score (Gart ad Nam) method. The ecessary samle size i each case is 5 er grou. Setu This sectio resets the values of each of the arameters eeded to ru this examle. First, from the PASS Home widow, load the Cofidece Itervals for Two Proortios usig Proortios rocedure widow by exadig Proortios, the Two Ideedet Proortios, the clicig o Cofidece Iterval, ad the clicig o Cofidece Itervals for Two Proortios usig Proortios. You may the mae the aroriate etries as listed below, or oe Examle 3 by goig to the File meu ad choosig Oe Examle Temlate. Otio Value Desig Tab Solve For... Samle Size Cofidece Iterval Formula... Score w/sewess (Gart & Nam) Iterval Tye... Two-Sided Cofidece Level Grou Allocatio... Equal (N N) Cofidece Iterval Width (Two-Sided) P P Outut Clic the Calculate butto to erform the calculatios ad geerate the followig outut. Numeric Results Cofidece Target Actual Lower Uer Level N N N Width Width P P P - P Limit Limit PASS also calculates the ecessary samle size to be 5 er grou. 6-30

31 PASS Samle Size Software Cofidece Itervals for Two Proortios Examle 4 Calculatig Samle Size usig Differeces Suose a study is laed i which the researcher wishes to costruct a two-sided 95% cofidece iterval for the differece i roortios such that the width of the iterval is o wider tha 0.. The cofidece iterval method to be used is the Yates chi-square simle asymtotic method with cotiuity correctio. The cofidece level is set at 0.95, but 0.99 is icluded for comarative uroses. The differece estimate to be used is 0.05, ad the estimate for roortio is 0.3. Istead of examiig oly the iterval width of 0., a series of widths from 0.05 to 0.3 will also be cosidered. The goal is to determie the ecessary samle size. Setu This sectio resets the values of each of the arameters eeded to ru this examle. First, from the PASS Home widow, load the Cofidece Itervals for Two Proortios usig Differeces rocedure widow by exadig Proortios, the Two Ideedet Proortios, the clicig o Cofidece Iterval, ad the clicig o Cofidece Itervals for Two Proortios usig Differeces. You may the mae the aroriate etries as listed below, or oe Examle 4 by goig to the File meu ad choosig Oe Examle Temlate. Otio Value Desig Tab Solve For... Samle Size Cofidece Iterval Formula... Chi-Square C.C. (Yates) Iterval Tye... Two-Sided Cofidece Level Grou Allocatio... Equal (N N) Cofidece Iterval Width (Two-Sided) to 0.30 by 0.05 Differece i Samle Proortios P Aotated Outut Clic the Calculate butto to erform the calculatios ad geerate the followig outut. Numeric Results Numeric Results for Two-Sided Cofidece Itervals for the Differece i Proortios Cofidece Iterval Method: Chi-Square - Simle Asymtotic with Cotiuity Correctio (Yates) Cofidece Target Actual Lower Uer Level N N N Width Width P P P - P Limit Limit

32 PASS Samle Size Software Cofidece Itervals for Two Proortios Refereces Newcombe, R. G 'Iterval Estimatio for the Differece Betwee Ideedet Proortios: Comariso of Eleve Methods.' Statistics i Medicie, 7, Fleiss, J. L., Levi, B., Pai, M.C Statistical Methods for Rates ad Proortios. Third Editio. Joh Wiley & Sos. New Yor. Reort Defiitios Cofidece level is the roortio of cofidece itervals (costructed with this same cofidece level, samle size, etc.) that would cotai the true differece i oulatio roortios. N ad N are the umber of items samled from each oulatio. N is the total samle size, N N. Target Width is the value of the width that is etered ito the rocedure. Actual Width is the value of the width that is obtaied from the rocedure. P ad P are the assumed samle roortios for samle size calculatios. P - P is the differece betwee samle roortios at which samle size calculatios are made. Lower Limit ad Uer Limit are the lower ad uer limits of the cofidece iterval for the true differece i roortios (Poulatio Proortio - Poulatio Proortio ). Summary Statemets Grou samle sizes of 769 ad 769 roduce a two-sided 95% cofidece iterval for the differece i oulatio roortios with a width that is equal to whe the estimated samle roortio is 0.35, the estimated samle roortio is 0.30, ad the differece i samle roortios is This reort shows the calculated samle sizes for each of the scearios. Plots Sectio These lots show the grou samle size versus the cofidece iterval width for the two cofidece levels. Validatio usig Differeces The validatio for the rocedure Cofidece Itervals for the Differece betwee Two Proortios usig Differeces is show i Examles ad 3, which is the validatio for the roortio secificatio. 6-3

33 PASS Samle Size Software Cofidece Itervals for Two Proortios Examle 5 Calculatig Samle Size usig Ratios Suose a study is laed i which the researcher wishes to costruct a two-sided 95% cofidece iterval for the ratio of roortios such that the width of the iterval is o wider tha 0.. The cofidece iterval method to be used is the Logarithm (Katz) method. The cofidece level is set at 0.95, but 0.99 is icluded for comarative uroses. The ratio estimate to be used is., ad the estimate for roortio is 0.6. Istead of examiig oly the iterval width of 0., a series of widths from 0. to 0.3 will also be cosidered. The goal is to determie the ecessary samle size. Setu This sectio resets the values of each of the arameters eeded to ru this examle. First, from the PASS Home widow, load the Cofidece Itervals for Two Proortios usig Ratios rocedure widow by exadig Proortios, the Two Ideedet Proortios, the clicig o Cofidece Iterval, ad the clicig o Cofidece Itervals for Two Proortios usig Ratios. You may the mae the aroriate etries as listed below, or oe Examle 5 by goig to the File meu ad choosig Oe Examle Temlate. Otio Value Desig Tab Solve For... Samle Size Cofidece Iterval Formula... Logarithm (Katz) Iterval Tye... Two-Sided Cofidece Level Grou Allocatio... Equal (N N) Cofidece Iterval Width (Two-Sided) to 0.30 by 0.05 Ratio of Samle Proortios.... P Aotated Outut Clic the Calculate butto to erform the calculatios ad geerate the followig outut. Numeric Results Numeric Results for Two-Sided Cofidece Itervals for the Ratio of Proortios Cofidece Iterval Method: Logarithm (Katz) Cofidece Target Actual Lower Uer Level N N N Width Width P P P/P Limit Limit

34 PASS Samle Size Software Cofidece Itervals for Two Proortios Refereces Gart, Joh J. ad Nam, Ju-mo 'Aroximate Iterval Estimatio of the Ratio of Biomial Parameters: A Review ad Correctios for Sewess.' Biometrics, Volume 44, Kooma, P. A. R 'Cofidece Itervals for the Ratio of Two Biomial Proortios.' Biometrics, Volume 40, Issue, Katz, D., Batista, J., Aze, S. P., ad Pie, M. C 'Obtaiig Cofidece Itervals for the Ris Ratio i Cohort Studies.' Biometrics, Volume 34, Reort Defiitios Cofidece level is the roortio of cofidece itervals (costructed with this same cofidece level, samle size, etc.) that would cotai the true ratio of oulatio roortios. N ad N are the umber of items samled from each oulatio. N is the total samle size, N N. Target Width is the value of the width that is etered ito the rocedure. Actual Width is the value of the width that is obtaied from the rocedure. P ad P are the assumed samle roortios for samle size calculatios. P/P is the ratio of samle roortios at which samle size calculatios are made. Lower Limit ad Uer Limit are the lower ad uer limits of the cofidece iterval for the true ratio of roortios (Poulatio Proortio / Poulatio Proortio ). Summary Statemets Grou samle sizes of 337 ad 337 roduce a two-sided 95% cofidece iterval for the ratio of oulatio roortios with a width that is equal to 0.00 whe the estimated samle roortio is 0.7, the estimated samle roortio is 0.60, ad the ratio of the samle roortios is.0. This reort shows the calculated samle sizes for each of the scearios. Plots Sectio These lots show the grou samle size versus the cofidece iterval width for the two cofidece levels. 6-34

35 PASS Samle Size Software Cofidece Itervals for Two Proortios Examle 6 Validatio (Ratios) usig Gart ad Nam Gart ad Nam (988) age 33 give a examle (Examle ) of a calculatio for a cofidece iterval for the ratio of roortios whe the cofidece level is 95%, the samle roortio ratio is ad the samle roortio is 0.3, the samle size for grou is 0, ad the iterval width is for the Logarithm / (Walter) method, 3.75 for the Score (Farrigto ad Maig) method, ad 4.33 for the Score w/sewess (Gart ad Nam) method. The ecessary samle size for grou i each case is 0. Setu This sectio resets the values of each of the arameters eeded to ru this examle. First, from the PASS Home widow, load the Cofidece Itervals for Two Proortios usig Ratios rocedure widow by exadig Proortios, the Two Ideedet Proortios, the clicig o Cofidece Iterval, ad the clicig o Cofidece Itervals for Two Proortios usig Ratios. You may the mae the aroriate etries as listed below, or oe Examle 6 by goig to the File meu ad choosig Oe Examle Temlate. Otio Value Desig Tab Solve For... Samle Size Cofidece Iterval Formula... Varies [Logarithm / (Walter), Score (Farrigto ad Maig), Score w/sewess (Gart ad Nam)] Iterval Tye... Two-Sided Cofidece Level Grou Allocatio... Eter N, solve for N N... 0 Cofidece Iterval Width (Two-Sided).. Varies (3.437, 3.75, 4.33) Ratio of Samle Proortios... P Outut Clic the Calculate butto to erform the calculatios ad geerate the followig outut. Logarithm / (Walter) Cofidece Target Actual Lower Uer Level N N N Width Width P P P/P Limit Limit PASS also calculates the ecessary samle size for Grou to be 0. Score (Farrigto ad Maig) Cofidece Target Actual Lower Uer Level N N N Width Width P P P/P Limit Limit PASS also calculates the ecessary samle size for Grou to be

36 PASS Samle Size Software Cofidece Itervals for Two Proortios Score w/sewess (Gart ad Nam) Cofidece Target Actual Lower Uer Level N N N Width Width P P P/P Limit Limit PASS also calculates the ecessary samle size for Grou to be 0. Examle 7 Validatio (Ratios) usig Katz et al Katz et al (978) ages give a examle of a calculatio for a lower limit cofidece iterval for the ratio of roortios whe the cofidece level is 97.5%, the samle roortio ratio is ad the samle roortio is , the samle size for grou is, ad the distace from the ratio to the limit is 0.63 for the Logarithm (Katz) method. The ecessary samle size for grou is 5. Setu This sectio resets the values of each of the arameters eeded to ru this examle. First, from the PASS Home widow, load the Cofidece Itervals for Two Proortios usig Ratios rocedure widow by exadig Proortios, the Two Ideedet Proortios, the clicig o Cofidece Iterval, ad the clicig o Cofidece Itervals for Two Proortios usig Ratios. You may the mae the aroriate etries as listed below, or oe Examle 7 by goig to the File meu ad choosig Oe Examle Temlate. Otio Value Desig Tab Solve For... Samle Size Cofidece Iterval Formula... Logarithm (Katz) Iterval Tye... Lower Limit Cofidece Level Grou Allocatio... Eter N, solve for N N... Distace to from Ratio to Limit Ratio of Samle Proortios P Outut Clic the Calculate butto to erform the calculatios ad geerate the followig outut. Logarithm (Katz) Target Actual Dist from Dist from Cofidece Ratio Ratio Lower Uer Level N N N to Limit to Limit P P P/P Limit Limit If PASS also calculates the ecessary samle size for grou to be

37 PASS Samle Size Software Cofidece Itervals for Two Proortios Examle 8 Calculatig Samle Size usig Odds Ratios Suose a study is laed i which the researcher wishes to costruct a two-sided 95% cofidece iterval for the odds ratio such that the width of the iterval is o wider tha 0.5. The cofidece iterval method to be used is the Logarithm method. The cofidece level is set at 0.95, but 0.99 is icluded for comarative uroses. The odds ratio estimate to be used is.5, ad the estimate for roortio is 0.4. Istead of examiig oly the iterval width of 0.5, a series of widths from 0. to.0 will also be cosidered. The goal is to determie the ecessary samle size. Setu This sectio resets the values of each of the arameters eeded to ru this examle. First, from the PASS Home widow, load the Cofidece Itervals for Two Proortios usig Odds Ratios rocedure widow by exadig Proortios, the Two Ideedet Proortios, the clicig o Cofidece Iterval, ad the clicig o Cofidece Itervals for Two Proortios usig Odds Ratios. You may the mae the aroriate etries as listed below, or oe Examle 8 by goig to the File meu ad choosig Oe Examle Temlate. Otio Value Desig Tab Solve For... Samle Size Cofidece Iterval Formula... Logarithm Iterval Tye... Two-Sided Cofidece Level Grou Allocatio... Equal (N N) Cofidece Iterval Width (Two-Sided).. 0. to.0 by 0. Odds Ratio....5 P Aotated Outut Clic the Calculate butto to erform the calculatios ad geerate the followig outut. Numeric Results Numeric Results for Two-Sided Cofidece Itervals for the Odds Ratio Cofidece Iterval Method: Logarithm Odds Cofidece Target Actual Ratio Lower Uer Level N N N Width Width P P O/O Limit Limit

38 PASS Samle Size Software Cofidece Itervals for Two Proortios Refereces Fleiss, J. L., Levi, B., Pai, M.C Statistical Methods for Rates ad Proortios. Third Editio. Joh Wiley & Sos. New Yor. Reort Defiitios Cofidece level is the roortio of cofidece itervals (costructed with this same cofidece level, samle size, etc.) that would cotai the true odds ratio of oulatio roortios. N ad N are the umber of items samled from each oulatio. N is the total samle size, N N. Target Width is the value of the width that is etered ito the rocedure. Actual Width is the value of the width that is obtaied from the rocedure. P ad P are the assumed samle roortios for samle size calculatios. Odds Ratio O/O is the samle odds ratio at which samle size calculatios are made. Lower Limit ad Uer Limit are the lower ad uer limits of the cofidece iterval for the true odds ratio of roortios (Poulatio Odds / Poulatio Odds ). Summary Statemets Grou samle sizes of 844 ad 844 roduce a two-sided 95% cofidece iterval for the oulatio odds ratio with a width that is equal to 0.00 whe the estimated samle roortio is 0.50, the estimated samle roortio is 0.40, ad the samle odds ratio is.50. This reort shows the calculated samle sizes for each of the scearios. Plots Sectio These lots show the grou samle size versus the cofidece iterval width for the two cofidece levels. 6-38

39 PASS Samle Size Software Cofidece Itervals for Two Proortios Examle 9 Validatio (Odds Ratios) usig Fleiss et al Fleiss et al (003) ages 7, 9 give a examle of a calculatio for a cofidece iterval for the odds ratio whe the cofidece level is 95%, the samle odds ratio is.5 ad the samle roortio is 0., the samle size for grou is 50, ad the iterval width is for the Logarithm method, ad for the Fleiss method. The ecessary samle size for grou i each case is 50. Setu This sectio resets the values of each of the arameters eeded to ru this examle. First, from the PASS Home widow, load the Cofidece Itervals for Two Proortios usig Odds Ratios rocedure widow by exadig Proortios, the Two Ideedet Proortios, the clicig o Cofidece Iterval, ad the clicig o Cofidece Itervals for Two Proortios usig Odds Ratios. You may the mae the aroriate etries as listed below, or oe Examle 9 by goig to the File meu ad choosig Oe Examle Temlate. Otio Value Desig Tab Solve For... Samle Size Cofidece Iterval Formula... Varies [Logarithm, Fleiss] Iterval Tye... Two-Sided Cofidece Level Grou Allocatio... Eter N, solve for N N Cofidece Iterval Width (Two-Sided).. Varies (4.387, 4.980) Odds Ratio....5 P Outut Clic the Calculate butto to erform the calculatios ad geerate the followig outut. Logarithm Odds Cofidece Target Actual Ratio Lower Uer Level N N N Width Width P P O/O Limit Limit PASS also calculates the ecessary samle size for Grou to be 50. Fleiss Odds Cofidece Target Actual Ratio Lower Uer Level N N N Width Width P P O/O Limit Limit PASS also calculates the ecessary samle size for Grou to be

Confidence Intervals for One Mean

Confidence Intervals for One Mean Chapter 420 Cofidece Itervals for Oe Mea Itroductio This routie calculates the sample size ecessary to achieve a specified distace from the mea to the cofidece limit(s) at a stated cofidece level for a

More information

Unit 8: Inference for Proportions. Chapters 8 & 9 in IPS

Unit 8: Inference for Proportions. Chapters 8 & 9 in IPS Uit 8: Iferece for Proortios Chaters 8 & 9 i IPS Lecture Outlie Iferece for a Proortio (oe samle) Iferece for Two Proortios (two samles) Cotigecy Tables ad the χ test Iferece for Proortios IPS, Chater

More information

Properties of MLE: consistency, asymptotic normality. Fisher information.

Properties of MLE: consistency, asymptotic normality. Fisher information. Lecture 3 Properties of MLE: cosistecy, asymptotic ormality. Fisher iformatio. I this sectio we will try to uderstad why MLEs are good. Let us recall two facts from probability that we be used ofte throughout

More information

Confidence Intervals for Linear Regression Slope

Confidence Intervals for Linear Regression Slope Chapter 856 Cofidece Iterval for Liear Regreio Slope Itroductio Thi routie calculate the ample ize eceary to achieve a pecified ditace from the lope to the cofidece limit at a tated cofidece level for

More information

Lesson 15 ANOVA (analysis of variance)

Lesson 15 ANOVA (analysis of variance) Outlie Variability -betwee group variability -withi group variability -total variability -F-ratio Computatio -sums of squares (betwee/withi/total -degrees of freedom (betwee/withi/total -mea square (betwee/withi

More information

Methods in Sample Surveys 140.640 3rd Quarter, 2009

Methods in Sample Surveys 140.640 3rd Quarter, 2009 This work is licesed uder a Creative Commos Attributio-NoCommercial-ShareAlike Licese. Your use of this material costitutes accetace of that licese ad the coditios of use of materials o this site. Coyright

More information

I. Chi-squared Distributions

I. Chi-squared Distributions 1 M 358K Supplemet to Chapter 23: CHI-SQUARED DISTRIBUTIONS, T-DISTRIBUTIONS, AND DEGREES OF FREEDOM To uderstad t-distributios, we first eed to look at aother family of distributios, the chi-squared distributios.

More information

Z-TEST / Z-STATISTIC: used to test hypotheses about. µ when the population standard deviation is unknown

Z-TEST / Z-STATISTIC: used to test hypotheses about. µ when the population standard deviation is unknown Z-TEST / Z-STATISTIC: used to test hypotheses about µ whe the populatio stadard deviatio is kow ad populatio distributio is ormal or sample size is large T-TEST / T-STATISTIC: used to test hypotheses about

More information

Definition. A variable X that takes on values X 1, X 2, X 3,...X k with respective frequencies f 1, f 2, f 3,...f k has mean

Definition. A variable X that takes on values X 1, X 2, X 3,...X k with respective frequencies f 1, f 2, f 3,...f k has mean 1 Social Studies 201 October 13, 2004 Note: The examples i these otes may be differet tha used i class. However, the examples are similar ad the methods used are idetical to what was preseted i class.

More information

1. C. The formula for the confidence interval for a population mean is: x t, which was

1. C. The formula for the confidence interval for a population mean is: x t, which was s 1. C. The formula for the cofidece iterval for a populatio mea is: x t, which was based o the sample Mea. So, x is guarateed to be i the iterval you form.. D. Use the rule : p-value

More information

Confidence Intervals. CI for a population mean (σ is known and n > 30 or the variable is normally distributed in the.

Confidence Intervals. CI for a population mean (σ is known and n > 30 or the variable is normally distributed in the. Cofidece Itervals A cofidece iterval is a iterval whose purpose is to estimate a parameter (a umber that could, i theory, be calculated from the populatio, if measuremets were available for the whole populatio).

More information

Chapter 7: Confidence Interval and Sample Size

Chapter 7: Confidence Interval and Sample Size Chapter 7: Cofidece Iterval ad Sample Size Learig Objectives Upo successful completio of Chapter 7, you will be able to: Fid the cofidece iterval for the mea, proportio, ad variace. Determie the miimum

More information

Hypothesis testing. Null and alternative hypotheses

Hypothesis testing. Null and alternative hypotheses Hypothesis testig Aother importat use of samplig distributios is to test hypotheses about populatio parameters, e.g. mea, proportio, regressio coefficiets, etc. For example, it is possible to stipulate

More information

Incremental calculation of weighted mean and variance

Incremental calculation of weighted mean and variance Icremetal calculatio of weighted mea ad variace Toy Fich [email protected] [email protected] Uiversity of Cambridge Computig Service February 009 Abstract I these otes I eplai how to derive formulae for umerically

More information

5: Introduction to Estimation

5: Introduction to Estimation 5: Itroductio to Estimatio Cotets Acroyms ad symbols... 1 Statistical iferece... Estimatig µ with cofidece... 3 Samplig distributio of the mea... 3 Cofidece Iterval for μ whe σ is kow before had... 4 Sample

More information

One-sample test of proportions

One-sample test of proportions Oe-sample test of proportios The Settig: Idividuals i some populatio ca be classified ito oe of two categories. You wat to make iferece about the proportio i each category, so you draw a sample. Examples:

More information

Determining the sample size

Determining the sample size Determiig the sample size Oe of the most commo questios ay statisticia gets asked is How large a sample size do I eed? Researchers are ofte surprised to fid out that the aswer depeds o a umber of factors

More information

Hypergeometric Distributions

Hypergeometric Distributions 7.4 Hypergeometric Distributios Whe choosig the startig lie-up for a game, a coach obviously has to choose a differet player for each positio. Similarly, whe a uio elects delegates for a covetio or you

More information

Lesson 17 Pearson s Correlation Coefficient

Lesson 17 Pearson s Correlation Coefficient Outlie Measures of Relatioships Pearso s Correlatio Coefficiet (r) -types of data -scatter plots -measure of directio -measure of stregth Computatio -covariatio of X ad Y -uique variatio i X ad Y -measurig

More information

Case Study. Normal and t Distributions. Density Plot. Normal Distributions

Case Study. Normal and t Distributions. Density Plot. Normal Distributions Case Study Normal ad t Distributios Bret Halo ad Bret Larget Departmet of Statistics Uiversity of Wiscosi Madiso October 11 13, 2011 Case Study Body temperature varies withi idividuals over time (it ca

More information

Regression with a Binary Dependent Variable (SW Ch. 11)

Regression with a Binary Dependent Variable (SW Ch. 11) Regressio with a Biary Deedet Variable (SW Ch. 11) So far the deedet variable (Y) has bee cotiuous: district-wide average test score traffic fatality rate But we might wat to uderstad the effect of X o

More information

A probabilistic proof of a binomial identity

A probabilistic proof of a binomial identity A probabilistic proof of a biomial idetity Joatho Peterso Abstract We give a elemetary probabilistic proof of a biomial idetity. The proof is obtaied by computig the probability of a certai evet i two

More information

Measures of Spread and Boxplots Discrete Math, Section 9.4

Measures of Spread and Boxplots Discrete Math, Section 9.4 Measures of Spread ad Boxplots Discrete Math, Sectio 9.4 We start with a example: Example 1: Comparig Mea ad Media Compute the mea ad media of each data set: S 1 = {4, 6, 8, 10, 1, 14, 16} S = {4, 7, 9,

More information

Output Analysis (2, Chapters 10 &11 Law)

Output Analysis (2, Chapters 10 &11 Law) B. Maddah ENMG 6 Simulatio 05/0/07 Output Aalysis (, Chapters 10 &11 Law) Comparig alterative system cofiguratio Sice the output of a simulatio is radom, the comparig differet systems via simulatio should

More information

Normal Distribution.

Normal Distribution. Normal Distributio www.icrf.l Normal distributio I probability theory, the ormal or Gaussia distributio, is a cotiuous probability distributio that is ofte used as a first approimatio to describe realvalued

More information

Chapter 7 Methods of Finding Estimators

Chapter 7 Methods of Finding Estimators Chapter 7 for BST 695: Special Topics i Statistical Theory. Kui Zhag, 011 Chapter 7 Methods of Fidig Estimators Sectio 7.1 Itroductio Defiitio 7.1.1 A poit estimator is ay fuctio W( X) W( X1, X,, X ) of

More information

Overview. Learning Objectives. Point Estimate. Estimation. Estimating the Value of a Parameter Using Confidence Intervals

Overview. Learning Objectives. Point Estimate. Estimation. Estimating the Value of a Parameter Using Confidence Intervals Overview Estimatig the Value of a Parameter Usig Cofidece Itervals We apply the results about the sample mea the problem of estimatio Estimatio is the process of usig sample data estimate the value of

More information

1 Computing the Standard Deviation of Sample Means

1 Computing the Standard Deviation of Sample Means Computig the Stadard Deviatio of Sample Meas Quality cotrol charts are based o sample meas ot o idividual values withi a sample. A sample is a group of items, which are cosidered all together for our aalysis.

More information

SECTION 1.5 : SUMMATION NOTATION + WORK WITH SEQUENCES

SECTION 1.5 : SUMMATION NOTATION + WORK WITH SEQUENCES SECTION 1.5 : SUMMATION NOTATION + WORK WITH SEQUENCES Read Sectio 1.5 (pages 5 9) Overview I Sectio 1.5 we lear to work with summatio otatio ad formulas. We will also itroduce a brief overview of sequeces,

More information

PSYCHOLOGICAL STATISTICS

PSYCHOLOGICAL STATISTICS UNIVERSITY OF CALICUT SCHOOL OF DISTANCE EDUCATION B Sc. Cousellig Psychology (0 Adm.) IV SEMESTER COMPLEMENTARY COURSE PSYCHOLOGICAL STATISTICS QUESTION BANK. Iferetial statistics is the brach of statistics

More information

Solving Logarithms and Exponential Equations

Solving Logarithms and Exponential Equations Solvig Logarithms ad Epoetial Equatios Logarithmic Equatios There are two major ideas required whe solvig Logarithmic Equatios. The first is the Defiitio of a Logarithm. You may recall from a earlier topic:

More information

CHAPTER 3 THE TIME VALUE OF MONEY

CHAPTER 3 THE TIME VALUE OF MONEY CHAPTER 3 THE TIME VALUE OF MONEY OVERVIEW A dollar i the had today is worth more tha a dollar to be received i the future because, if you had it ow, you could ivest that dollar ad ear iterest. Of all

More information

How To Solve The Homewor Problem Beautifully

How To Solve The Homewor Problem Beautifully Egieerig 33 eautiful Homewor et 3 of 7 Kuszmar roblem.5.5 large departmet store sells sport shirts i three sizes small, medium, ad large, three patters plaid, prit, ad stripe, ad two sleeve legths log

More information

1 Correlation and Regression Analysis

1 Correlation and Regression Analysis 1 Correlatio ad Regressio Aalysis I this sectio we will be ivestigatig the relatioship betwee two cotiuous variable, such as height ad weight, the cocetratio of a ijected drug ad heart rate, or the cosumptio

More information

Soving Recurrence Relations

Soving Recurrence Relations Sovig Recurrece Relatios Part 1. Homogeeous liear 2d degree relatios with costat coefficiets. Cosider the recurrece relatio ( ) T () + at ( 1) + bt ( 2) = 0 This is called a homogeeous liear 2d degree

More information

Example 2 Find the square root of 0. The only square root of 0 is 0 (since 0 is not positive or negative, so those choices don t exist here).

Example 2 Find the square root of 0. The only square root of 0 is 0 (since 0 is not positive or negative, so those choices don t exist here). BEGINNING ALGEBRA Roots ad Radicals (revised summer, 00 Olso) Packet to Supplemet the Curret Textbook - Part Review of Square Roots & Irratioals (This portio ca be ay time before Part ad should mostly

More information

Taking DCOP to the Real World: Efficient Complete Solutions for Distributed Multi-Event Scheduling

Taking DCOP to the Real World: Efficient Complete Solutions for Distributed Multi-Event Scheduling Taig DCOP to the Real World: Efficiet Complete Solutios for Distributed Multi-Evet Schedulig Rajiv T. Maheswara, Milid Tambe, Emma Bowrig, Joatha P. Pearce, ad Pradeep araatham Uiversity of Souther Califoria

More information

GCSE STATISTICS. 4) How to calculate the range: The difference between the biggest number and the smallest number.

GCSE STATISTICS. 4) How to calculate the range: The difference between the biggest number and the smallest number. GCSE STATISTICS You should kow: 1) How to draw a frequecy diagram: e.g. NUMBER TALLY FREQUENCY 1 3 5 ) How to draw a bar chart, a pictogram, ad a pie chart. 3) How to use averages: a) Mea - add up all

More information

Chapter 7 - Sampling Distributions. 1 Introduction. What is statistics? It consist of three major areas:

Chapter 7 - Sampling Distributions. 1 Introduction. What is statistics? It consist of three major areas: Chapter 7 - Samplig Distributios 1 Itroductio What is statistics? It cosist of three major areas: Data Collectio: samplig plas ad experimetal desigs Descriptive Statistics: umerical ad graphical summaries

More information

Practice Problems for Test 3

Practice Problems for Test 3 Practice Problems for Test 3 Note: these problems oly cover CIs ad hypothesis testig You are also resposible for kowig the samplig distributio of the sample meas, ad the Cetral Limit Theorem Review all

More information

CS103X: Discrete Structures Homework 4 Solutions

CS103X: Discrete Structures Homework 4 Solutions CS103X: Discrete Structures Homewor 4 Solutios Due February 22, 2008 Exercise 1 10 poits. Silico Valley questios: a How may possible six-figure salaries i whole dollar amouts are there that cotai at least

More information

Quadrat Sampling in Population Ecology

Quadrat Sampling in Population Ecology Quadrat Samplig i Populatio Ecology Backgroud Estimatig the abudace of orgaisms. Ecology is ofte referred to as the "study of distributio ad abudace". This beig true, we would ofte like to kow how may

More information

THE ARITHMETIC OF INTEGERS. - multiplication, exponentiation, division, addition, and subtraction

THE ARITHMETIC OF INTEGERS. - multiplication, exponentiation, division, addition, and subtraction THE ARITHMETIC OF INTEGERS - multiplicatio, expoetiatio, divisio, additio, ad subtractio What to do ad what ot to do. THE INTEGERS Recall that a iteger is oe of the whole umbers, which may be either positive,

More information

Now here is the important step

Now here is the important step LINEST i Excel The Excel spreadsheet fuctio "liest" is a complete liear least squares curve fittig routie that produces ucertaity estimates for the fit values. There are two ways to access the "liest"

More information

3 Energy. 3.3. Non-Flow Energy Equation (NFEE) Internal Energy. MECH 225 Engineering Science 2

3 Energy. 3.3. Non-Flow Energy Equation (NFEE) Internal Energy. MECH 225 Engineering Science 2 MECH 5 Egieerig Sciece 3 Eergy 3.3. No-Flow Eergy Equatio (NFEE) You may have oticed that the term system kees croig u. It is ecessary, therefore, that before we start ay aalysis we defie the system that

More information

Chapter 6: Variance, the law of large numbers and the Monte-Carlo method

Chapter 6: Variance, the law of large numbers and the Monte-Carlo method Chapter 6: Variace, the law of large umbers ad the Mote-Carlo method Expected value, variace, ad Chebyshev iequality. If X is a radom variable recall that the expected value of X, E[X] is the average value

More information

0.7 0.6 0.2 0 0 96 96.5 97 97.5 98 98.5 99 99.5 100 100.5 96.5 97 97.5 98 98.5 99 99.5 100 100.5

0.7 0.6 0.2 0 0 96 96.5 97 97.5 98 98.5 99 99.5 100 100.5 96.5 97 97.5 98 98.5 99 99.5 100 100.5 Sectio 13 Kolmogorov-Smirov test. Suppose that we have a i.i.d. sample X 1,..., X with some ukow distributio P ad we would like to test the hypothesis that P is equal to a particular distributio P 0, i.e.

More information

Non-Inferiority Tests for Two Proportions

Non-Inferiority Tests for Two Proportions Chapter 0 Non-Inferiority Tests for Two Proportions Introduction This module provides power analysis and sample size calculation for non-inferiority and superiority tests in twosample designs in which

More information

THE REGRESSION MODEL IN MATRIX FORM. For simple linear regression, meaning one predictor, the model is. for i = 1, 2, 3,, n

THE REGRESSION MODEL IN MATRIX FORM. For simple linear regression, meaning one predictor, the model is. for i = 1, 2, 3,, n We will cosider the liear regressio model i matrix form. For simple liear regressio, meaig oe predictor, the model is i = + x i + ε i for i =,,,, This model icludes the assumptio that the ε i s are a sample

More information

Trigonometric Form of a Complex Number. The Complex Plane. axis. ( 2, 1) or 2 i FIGURE 6.44. The absolute value of the complex number z a bi is

Trigonometric Form of a Complex Number. The Complex Plane. axis. ( 2, 1) or 2 i FIGURE 6.44. The absolute value of the complex number z a bi is 0_0605.qxd /5/05 0:45 AM Page 470 470 Chapter 6 Additioal Topics i Trigoometry 6.5 Trigoometric Form of a Complex Number What you should lear Plot complex umbers i the complex plae ad fid absolute values

More information

Maximum Likelihood Estimators.

Maximum Likelihood Estimators. Lecture 2 Maximum Likelihood Estimators. Matlab example. As a motivatio, let us look at oe Matlab example. Let us geerate a radom sample of size 00 from beta distributio Beta(5, 2). We will lear the defiitio

More information

Overview of some probability distributions.

Overview of some probability distributions. Lecture Overview of some probability distributios. I this lecture we will review several commo distributios that will be used ofte throughtout the class. Each distributio is usually described by its probability

More information

Inference on Proportion. Chapter 8 Tests of Statistical Hypotheses. Sampling Distribution of Sample Proportion. Confidence Interval

Inference on Proportion. Chapter 8 Tests of Statistical Hypotheses. Sampling Distribution of Sample Proportion. Confidence Interval Chapter 8 Tests of Statistical Hypotheses 8. Tests about Proportios HT - Iferece o Proportio Parameter: Populatio Proportio p (or π) (Percetage of people has o health isurace) x Statistic: Sample Proportio

More information

MEI Structured Mathematics. Module Summary Sheets. Statistics 2 (Version B: reference to new book)

MEI Structured Mathematics. Module Summary Sheets. Statistics 2 (Version B: reference to new book) MEI Mathematics i Educatio ad Idustry MEI Structured Mathematics Module Summary Sheets Statistics (Versio B: referece to ew book) Topic : The Poisso Distributio Topic : The Normal Distributio Topic 3:

More information

Discrete Mathematics and Probability Theory Spring 2014 Anant Sahai Note 13

Discrete Mathematics and Probability Theory Spring 2014 Anant Sahai Note 13 EECS 70 Discrete Mathematics ad Probability Theory Sprig 2014 Aat Sahai Note 13 Itroductio At this poit, we have see eough examples that it is worth just takig stock of our model of probability ad may

More information

Non-life insurance mathematics. Nils F. Haavardsson, University of Oslo and DNB Skadeforsikring

Non-life insurance mathematics. Nils F. Haavardsson, University of Oslo and DNB Skadeforsikring No-life isurace mathematics Nils F. Haavardsso, Uiversity of Oslo ad DNB Skadeforsikrig Mai issues so far Why does isurace work? How is risk premium defied ad why is it importat? How ca claim frequecy

More information

The following example will help us understand The Sampling Distribution of the Mean. C1 C2 C3 C4 C5 50 miles 84 miles 38 miles 120 miles 48 miles

The following example will help us understand The Sampling Distribution of the Mean. C1 C2 C3 C4 C5 50 miles 84 miles 38 miles 120 miles 48 miles The followig eample will help us uderstad The Samplig Distributio of the Mea Review: The populatio is the etire collectio of all idividuals or objects of iterest The sample is the portio of the populatio

More information

In nite Sequences. Dr. Philippe B. Laval Kennesaw State University. October 9, 2008

In nite Sequences. Dr. Philippe B. Laval Kennesaw State University. October 9, 2008 I ite Sequeces Dr. Philippe B. Laval Keesaw State Uiversity October 9, 2008 Abstract This had out is a itroductio to i ite sequeces. mai de itios ad presets some elemetary results. It gives the I ite Sequeces

More information

Statistical inference: example 1. Inferential Statistics

Statistical inference: example 1. Inferential Statistics Statistical iferece: example 1 Iferetial Statistics POPULATION SAMPLE A clothig store chai regularly buys from a supplier large quatities of a certai piece of clothig. Each item ca be classified either

More information

Section 11.3: The Integral Test

Section 11.3: The Integral Test Sectio.3: The Itegral Test Most of the series we have looked at have either diverged or have coverged ad we have bee able to fid what they coverge to. I geeral however, the problem is much more difficult

More information

CHAPTER 7: Central Limit Theorem: CLT for Averages (Means)

CHAPTER 7: Central Limit Theorem: CLT for Averages (Means) CHAPTER 7: Cetral Limit Theorem: CLT for Averages (Meas) X = the umber obtaied whe rollig oe six sided die oce. If we roll a six sided die oce, the mea of the probability distributio is X P(X = x) Simulatio:

More information

Week 3 Conditional probabilities, Bayes formula, WEEK 3 page 1 Expected value of a random variable

Week 3 Conditional probabilities, Bayes formula, WEEK 3 page 1 Expected value of a random variable Week 3 Coditioal probabilities, Bayes formula, WEEK 3 page 1 Expected value of a radom variable We recall our discussio of 5 card poker hads. Example 13 : a) What is the probability of evet A that a 5

More information

The Stable Marriage Problem

The Stable Marriage Problem The Stable Marriage Problem William Hut Lae Departmet of Computer Sciece ad Electrical Egieerig, West Virgiia Uiversity, Morgatow, WV [email protected] 1 Itroductio Imagie you are a matchmaker,

More information

Approximating Area under a curve with rectangles. To find the area under a curve we approximate the area using rectangles and then use limits to find

Approximating Area under a curve with rectangles. To find the area under a curve we approximate the area using rectangles and then use limits to find 1.8 Approximatig Area uder a curve with rectagles 1.6 To fid the area uder a curve we approximate the area usig rectagles ad the use limits to fid 1.4 the area. Example 1 Suppose we wat to estimate 1.

More information

.04. This means $1000 is multiplied by 1.02 five times, once for each of the remaining sixmonth

.04. This means $1000 is multiplied by 1.02 five times, once for each of the remaining sixmonth Questio 1: What is a ordiary auity? Let s look at a ordiary auity that is certai ad simple. By this, we mea a auity over a fixed term whose paymet period matches the iterest coversio period. Additioally,

More information

3. Greatest Common Divisor - Least Common Multiple

3. Greatest Common Divisor - Least Common Multiple 3 Greatest Commo Divisor - Least Commo Multiple Defiitio 31: The greatest commo divisor of two atural umbers a ad b is the largest atural umber c which divides both a ad b We deote the greatest commo gcd

More information

Modified Line Search Method for Global Optimization

Modified Line Search Method for Global Optimization Modified Lie Search Method for Global Optimizatio Cria Grosa ad Ajith Abraham Ceter of Excellece for Quatifiable Quality of Service Norwegia Uiversity of Sciece ad Techology Trodheim, Norway {cria, ajith}@q2s.tu.o

More information

5.4 Amortization. Question 1: How do you find the present value of an annuity? Question 2: How is a loan amortized?

5.4 Amortization. Question 1: How do you find the present value of an annuity? Question 2: How is a loan amortized? 5.4 Amortizatio Questio 1: How do you fid the preset value of a auity? Questio 2: How is a loa amortized? Questio 3: How do you make a amortizatio table? Oe of the most commo fiacial istrumets a perso

More information

ODBC. Getting Started With Sage Timberline Office ODBC

ODBC. Getting Started With Sage Timberline Office ODBC ODBC Gettig Started With Sage Timberlie Office ODBC NOTICE This documet ad the Sage Timberlie Office software may be used oly i accordace with the accompayig Sage Timberlie Office Ed User Licese Agreemet.

More information

NATIONAL SENIOR CERTIFICATE GRADE 12

NATIONAL SENIOR CERTIFICATE GRADE 12 NATIONAL SENIOR CERTIFICATE GRADE MATHEMATICS P EXEMPLAR 04 MARKS: 50 TIME: 3 hours This questio paper cosists of 8 pages ad iformatio sheet. Please tur over Mathematics/P DBE/04 NSC Grade Eemplar INSTRUCTIONS

More information

Estimating Probability Distributions by Observing Betting Practices

Estimating Probability Distributions by Observing Betting Practices 5th Iteratioal Symposium o Imprecise Probability: Theories ad Applicatios, Prague, Czech Republic, 007 Estimatig Probability Distributios by Observig Bettig Practices Dr C Lych Natioal Uiversity of Irelad,

More information

Analyzing Longitudinal Data from Complex Surveys Using SUDAAN

Analyzing Longitudinal Data from Complex Surveys Using SUDAAN Aalyzig Logitudial Data from Complex Surveys Usig SUDAAN Darryl Creel Statistics ad Epidemiology, RTI Iteratioal, 312 Trotter Farm Drive, Rockville, MD, 20850 Abstract SUDAAN: Software for the Statistical

More information

Center, Spread, and Shape in Inference: Claims, Caveats, and Insights

Center, Spread, and Shape in Inference: Claims, Caveats, and Insights Ceter, Spread, ad Shape i Iferece: Claims, Caveats, ad Isights Dr. Nacy Pfeig (Uiversity of Pittsburgh) AMATYC November 2008 Prelimiary Activities 1. I would like to produce a iterval estimate for the

More information

15.075 Exam 3. Instructor: Cynthia Rudin TA: Dimitrios Bisias. November 22, 2011

15.075 Exam 3. Instructor: Cynthia Rudin TA: Dimitrios Bisias. November 22, 2011 15.075 Exam 3 Istructor: Cythia Rudi TA: Dimitrios Bisias November 22, 2011 Gradig is based o demostratio of coceptual uderstadig, so you eed to show all of your work. Problem 1 A compay makes high-defiitio

More information

1. MATHEMATICAL INDUCTION

1. MATHEMATICAL INDUCTION 1. MATHEMATICAL INDUCTION EXAMPLE 1: Prove that for ay iteger 1. Proof: 1 + 2 + 3 +... + ( + 1 2 (1.1 STEP 1: For 1 (1.1 is true, sice 1 1(1 + 1. 2 STEP 2: Suppose (1.1 is true for some k 1, that is 1

More information

CS103A Handout 23 Winter 2002 February 22, 2002 Solving Recurrence Relations

CS103A Handout 23 Winter 2002 February 22, 2002 Solving Recurrence Relations CS3A Hadout 3 Witer 00 February, 00 Solvig Recurrece Relatios Itroductio A wide variety of recurrece problems occur i models. Some of these recurrece relatios ca be solved usig iteratio or some other ad

More information

Chapter 5: Inner Product Spaces

Chapter 5: Inner Product Spaces Chapter 5: Ier Product Spaces Chapter 5: Ier Product Spaces SECION A Itroductio to Ier Product Spaces By the ed of this sectio you will be able to uderstad what is meat by a ier product space give examples

More information

Mann-Whitney U 2 Sample Test (a.k.a. Wilcoxon Rank Sum Test)

Mann-Whitney U 2 Sample Test (a.k.a. Wilcoxon Rank Sum Test) No-Parametric ivariate Statistics: Wilcoxo-Ma-Whitey 2 Sample Test 1 Ma-Whitey 2 Sample Test (a.k.a. Wilcoxo Rak Sum Test) The (Wilcoxo-) Ma-Whitey (WMW) test is the o-parametric equivalet of a pooled

More information

where: T = number of years of cash flow in investment's life n = the year in which the cash flow X n i = IRR = the internal rate of return

where: T = number of years of cash flow in investment's life n = the year in which the cash flow X n i = IRR = the internal rate of return EVALUATING ALTERNATIVE CAPITAL INVESTMENT PROGRAMS By Ke D. Duft, Extesio Ecoomist I the March 98 issue of this publicatio we reviewed the procedure by which a capital ivestmet project was assessed. The

More information

INVESTMENT PERFORMANCE COUNCIL (IPC)

INVESTMENT PERFORMANCE COUNCIL (IPC) INVESTMENT PEFOMANCE COUNCIL (IPC) INVITATION TO COMMENT: Global Ivestmet Performace Stadards (GIPS ) Guidace Statemet o Calculatio Methodology The Associatio for Ivestmet Maagemet ad esearch (AIM) seeks

More information

Your organization has a Class B IP address of 166.144.0.0 Before you implement subnetting, the Network ID and Host ID are divided as follows:

Your organization has a Class B IP address of 166.144.0.0 Before you implement subnetting, the Network ID and Host ID are divided as follows: Subettig Subettig is used to subdivide a sigle class of etwork i to multiple smaller etworks. Example: Your orgaizatio has a Class B IP address of 166.144.0.0 Before you implemet subettig, the Network

More information

SAMPLE QUESTIONS FOR FINAL EXAM. (1) (2) (3) (4) Find the following using the definition of the Riemann integral: (2x + 1)dx

SAMPLE QUESTIONS FOR FINAL EXAM. (1) (2) (3) (4) Find the following using the definition of the Riemann integral: (2x + 1)dx SAMPLE QUESTIONS FOR FINAL EXAM REAL ANALYSIS I FALL 006 3 4 Fid the followig usig the defiitio of the Riema itegral: a 0 x + dx 3 Cosider the partitio P x 0 3, x 3 +, x 3 +,......, x 3 3 + 3 of the iterval

More information

Sequences and Series

Sequences and Series CHAPTER 9 Sequeces ad Series 9.. Covergece: Defiitio ad Examples Sequeces The purpose of this chapter is to itroduce a particular way of geeratig algorithms for fidig the values of fuctios defied by their

More information

Theorems About Power Series

Theorems About Power Series Physics 6A Witer 20 Theorems About Power Series Cosider a power series, f(x) = a x, () where the a are real coefficiets ad x is a real variable. There exists a real o-egative umber R, called the radius

More information

Research Method (I) --Knowledge on Sampling (Simple Random Sampling)

Research Method (I) --Knowledge on Sampling (Simple Random Sampling) Research Method (I) --Kowledge o Samplig (Simple Radom Samplig) 1. Itroductio to samplig 1.1 Defiitio of samplig Samplig ca be defied as selectig part of the elemets i a populatio. It results i the fact

More information

Simple Annuities Present Value.

Simple Annuities Present Value. Simple Auities Preset Value. OBJECTIVES (i) To uderstad the uderlyig priciple of a preset value auity. (ii) To use a CASIO CFX-9850GB PLUS to efficietly compute values associated with preset value auities.

More information

5 Boolean Decision Trees (February 11)

5 Boolean Decision Trees (February 11) 5 Boolea Decisio Trees (February 11) 5.1 Graph Coectivity Suppose we are give a udirected graph G, represeted as a boolea adjacecy matrix = (a ij ), where a ij = 1 if ad oly if vertices i ad j are coected

More information

Two Correlated Proportions (McNemar Test)

Two Correlated Proportions (McNemar Test) Chapter 50 Two Correlated Proportions (Mcemar Test) Introduction This procedure computes confidence intervals and hypothesis tests for the comparison of the marginal frequencies of two factors (each with

More information

Confidence intervals and hypothesis tests

Confidence intervals and hypothesis tests Chapter 2 Cofidece itervals ad hypothesis tests This chapter focuses o how to draw coclusios about populatios from sample data. We ll start by lookig at biary data (e.g., pollig), ad lear how to estimate

More information

WHEN IS THE (CO)SINE OF A RATIONAL ANGLE EQUAL TO A RATIONAL NUMBER?

WHEN IS THE (CO)SINE OF A RATIONAL ANGLE EQUAL TO A RATIONAL NUMBER? WHEN IS THE (CO)SINE OF A RATIONAL ANGLE EQUAL TO A RATIONAL NUMBER? JÖRG JAHNEL 1. My Motivatio Some Sort of a Itroductio Last term I tought Topological Groups at the Göttige Georg August Uiversity. This

More information

University of California, Los Angeles Department of Statistics. Distributions related to the normal distribution

University of California, Los Angeles Department of Statistics. Distributions related to the normal distribution Uiversity of Califoria, Los Ageles Departmet of Statistics Statistics 100B Istructor: Nicolas Christou Three importat distributios: Distributios related to the ormal distributio Chi-square (χ ) distributio.

More information

CHAPTER 11 Financial mathematics

CHAPTER 11 Financial mathematics CHAPTER 11 Fiacial mathematics I this chapter you will: Calculate iterest usig the simple iterest formula ( ) Use the simple iterest formula to calculate the pricipal (P) Use the simple iterest formula

More information

UC Berkeley Department of Electrical Engineering and Computer Science. EE 126: Probablity and Random Processes. Solutions 9 Spring 2006

UC Berkeley Department of Electrical Engineering and Computer Science. EE 126: Probablity and Random Processes. Solutions 9 Spring 2006 Exam format UC Bereley Departmet of Electrical Egieerig ad Computer Sciece EE 6: Probablity ad Radom Processes Solutios 9 Sprig 006 The secod midterm will be held o Wedesday May 7; CHECK the fial exam

More information

A GUIDE TO LEVEL 3 VALUE ADDED IN 2013 SCHOOL AND COLLEGE PERFORMANCE TABLES

A GUIDE TO LEVEL 3 VALUE ADDED IN 2013 SCHOOL AND COLLEGE PERFORMANCE TABLES A GUIDE TO LEVEL 3 VALUE ADDED IN 2013 SCHOOL AND COLLEGE PERFORMANCE TABLES Cotets Page No. Summary Iterpretig School ad College Value Added Scores 2 What is Value Added? 3 The Learer Achievemet Tracker

More information

Baan Service Master Data Management

Baan Service Master Data Management Baa Service Master Data Maagemet Module Procedure UP069A US Documetiformatio Documet Documet code : UP069A US Documet group : User Documetatio Documet title : Master Data Maagemet Applicatio/Package :

More information

A Combined Continuous/Binary Genetic Algorithm for Microstrip Antenna Design

A Combined Continuous/Binary Genetic Algorithm for Microstrip Antenna Design A Combied Cotiuous/Biary Geetic Algorithm for Microstrip Atea Desig Rady L. Haupt The Pesylvaia State Uiversity Applied Research Laboratory P. O. Box 30 State College, PA 16804-0030 [email protected] Abstract:

More information

INVESTMENT PERFORMANCE COUNCIL (IPC) Guidance Statement on Calculation Methodology

INVESTMENT PERFORMANCE COUNCIL (IPC) Guidance Statement on Calculation Methodology Adoptio Date: 4 March 2004 Effective Date: 1 Jue 2004 Retroactive Applicatio: No Public Commet Period: Aug Nov 2002 INVESTMENT PERFORMANCE COUNCIL (IPC) Preface Guidace Statemet o Calculatio Methodology

More information

The analysis of the Cournot oligopoly model considering the subjective motive in the strategy selection

The analysis of the Cournot oligopoly model considering the subjective motive in the strategy selection The aalysis of the Courot oligopoly model cosiderig the subjective motive i the strategy selectio Shigehito Furuyama Teruhisa Nakai Departmet of Systems Maagemet Egieerig Faculty of Egieerig Kasai Uiversity

More information

5.3. Generalized Permutations and Combinations

5.3. Generalized Permutations and Combinations 53 GENERALIZED PERMUTATIONS AND COMBINATIONS 73 53 Geeralized Permutatios ad Combiatios 53 Permutatios with Repeated Elemets Assume that we have a alphabet with letters ad we wat to write all possible

More information