TI-89, TI-92 Plus or Voyage 200 for Non-Business Statistics

Size: px
Start display at page:

Download "TI-89, TI-92 Plus or Voyage 200 for Non-Business Statistics"

Transcription

1 Chapter 3 TI-89, TI-9 Plu or Voyage 00 for No-Buie Statitic Eterig Data Pre [APPS], elect FlahApp the pre [ENTER]. Highlight Stat/Lit Editor the pre [ENTER]. Pre [ENTER] agai to elect the mai folder. (Note: If you do ot have FlahApp or Stat/Lit Editor the you ca dowload it from Rachel Webb i NH354B durig office hour. Make ure the curor i i the lit, ot o the lit ame ad type the deired value preig [ENTER] after each oe. For x-y data pair, eter all x-value i oe lit. Eter all correpodig y-value i a ecod lit. Pre [Home] to retur to the home cree. To clear a previouly tored lit of data value, arrow up to the lit ame you wat to clear, pre [CLEAR], the pre eter. Oe Variable Statitic Eter the data i lit. Select F4 for the Calc meu. Ue curor key to highlight :-Var Stat. Type i the ame of your lit without pace, for example lit. Pre eter twice ad the tatitic will appear i a ew widow. Ue the curor key to arrow up ad dow to ee all of the value. Note: Sx i the ample tadard deviatio. The quartile calculated by the TI calculator differ omewhat from thoe foud uig the procedure i the text for thi cla. Make ure that you do the quartile by had. Sortig Data After the data i etered i a lit, make ure your curor i o the lit you wat to ort. Select F3 Lit, the elect :Op, the elect :Sort Lit. Make ure the lit umber i correct ad that Acedig i elected the hi Eter. Chapter 4 Factorial O the home cree, eter the umber of which you would like to fid the factorial. Pre [ d ] [Math] > 7:Probability > :!. Pre [ENTER] to calculate. Combiatio/Permutatio Pre [ d ] Math > 7:Probability > Pre for permutatio (: P r ), 3 for combiatio (3: C r ). Eter the ample ize o the home cree, the a comma, the eter the umber of uccee the ed the parethei. Pre [ENTER] to calculate. Chapter 5 Mea, Variace ad Stadard Deviatio of a Dicrete Probability Ditributio Table Go to the [App] Stat/Lit Editor, ad type the X value ito Lit ad P(X) value ito Lit. Select F4 for the Calc meu. Ue curor key to highlight :-Var Stat. Type i the ame of your X lit without pace, for example lit where it ay Lit. Type i the ame of your P(X) lit without pace, for example lit where

2 it ay Freq. Pre eter twice ad the tatitic will appear i a ew widow. Ue the curor key to arrow up ad dow to ee all of the value. Note: x thi i µ the populatio mea ad σx i the populatio tadard deviatio; quare thi value to get the variace. Biomial Ditributio Go to the [App] Stat/Lit Editor, the elect F5 [DISTR]. Thi will get you a meu of probability ditributio. Arrow dow to biomial Pdf ad pre [ENTER]. Eter the value for, p ad x ito each cell. Pre [ENTER]. Thi i the probability deity fuctio ad will retur you the probability of exactly x uccee. If you leave off the x value ad jut eter ad p, you will get all the probabilitie for each x from 0 to. Arrow dow to biomial Cdf ad pre [ENTER]. Eter the value for, p ad lower ad upper value of x ito each cell. Pre [ENTER]. Thi i the cumulative ditributio fuctio ad will retur you the probability betwee the lower ad upper x-value, icluive. Poio Ditributio Go to the [App] Stat/Lit Editor, the elect F5 [DISTR]. Thi will get you a meu of probability ditributio. Arrow dow to Poio Pdf ad pre [ENTER]. Eter the value for ad x ito each cell. Pre [ENTER]. Thi i the probability deity fuctio ad will retur you the probability of exactly x uccee. Arrow dow to Poio Cdf ad pre [ENTER]. Eter the value for ad the lower ad upper value of x ito each cell. Pre [ENTER]. Thi i the cumulative ditributio fuctio ad will retur you the probability betwee the lower ad upper x-value, icluive. Note: the calculator doe ot have the hypergeometric or multiomial ditributio. Chapter 6 Normal Ditributio Go to the [App] Stat/Lit Editor, the elect F5 [DISTR]. Thi will get you a meu of probability ditributio. Arrow dow to Normal Cdf ad pre [ENTER]. Eter the value for the lower x value (x ), upper x value (x ),, ad ito each cell. Pre [ENTER]. Thi i the cumulative ditributio fuctio ad will retur P(x <x<x ). For example to fid P(80< X < 0) whe the mea i 00 ad the tadard deviatio i 0, you hould have i the followig order 80, 0, 00, 0. If you have a z-core, ue = 0 ad =, the you will get tadard ormal ditributio. For a left tail area ue a lower boud of egative ifiity (-), ad for a right tail are ue a upper boud ifiity (). Ivere Normal Ditributio Go to the [App] Stat/Lit Editor, the elect F5 [DISTR]. Thi will get you a meu of probability ditributio. Arrow dow to Ivere Normal ad pre [ENTER]. Eter the area to the left of the x value,, ad ito each cell. Pre [ENTER]. Thi will retur the percetile for the x value. For example to fid the 95 th percetile whe the mea i 00 ad the tadard deviatio i 0, you hould eter.95, 00, 0. If you ue = 0 ad =, the the default i the z-core for the tadard ormal ditributio. Chapter 7 Cofidece Iterval for oe ample The 00( - )% cofidece iterval for, whe i kow, i X z a /. Go to the [App] Stat/Lit Editor, the elect d the F7 [It], the elect : ZIterval. Chooe the iput method, data i whe you have etered data ito a lit previouly or tat whe you are give the mea ad tadard deviatio already. Type i the populatio tadard deviatio, ample mea, ample ize (or lit ame (lit), ad Freq: ) ad cofidece level, ad pre the [ENTER] key to calculate. The calculator retur the awer i iterval otatio. The 00( - )% cofidece iterval for, whe i ukow, i X t, /. Go to the [App] Stat/Lit Editor, the elect d the F7 [It], the elect :TIterval. Chooe the iput method, data i whe you have etered data ito a lit previouly or tat whe you are give the mea ad tadard deviatio already. Type i the mea, tadard deviatio, ample ize (or lit ame (lit), ad Freq: ) ad cofidece level, ad pre the [ENTER] key. The calculator retur the awer i iterval otatio. Be careful, if you accidetally ue the [:ZIterval] optio you would get the wrog awer.

3 A 00 % cofidece iterval for the populatio proportio p i pˆ pˆ pˆ z /. Go to the [App] Stat/Lit Editor, the elect d the F7 [It], the elect 5: -PropZIt. Type i the value for X, ample ize ad cofidece level, ad pre the [ENTER] key. The calculator retur the awer i iterval otatio. Note: ometime you are ot give the x value but a percetage itead. To fid the x value to ue i the calculator, multiply p by the ample ize ad roud off to the earet iteger. The calculator will give you a error meage if you put i a decimal for x or. For example if p =. ad = 4 the.*4 = 7.8, o ue x = 7. Note: you caot do the chi-quared cofidece iterval for oe variace o the TI-89. Chapter 8 Hypothei tetig for oe ample X 0 Hypothei tet for a populatio mea whe i kow, tet tatitic i Z. Go to the [App] Stat/Lit Editor, the elect d the F6 [Tet], the elect : ZTet. Chooe the iput method, data i whe you have etered data ito a lit previouly or tat whe you are give the mea ad tadard deviatio already. Type i the hypotheized mea ( 0 ), populatio tadard deviatio, ample mea, ample ize, (or lit ame (lit), ad Freq: ), arrow over to the, <, > ig ad elect the ame a the problem alterative hypothei tatemet the pre the [ENTER] key to calculate. The calculator retur the z-tet tatitic ad p-value. X Hypothei tet for a populatio mea whe i ukow, tet tatitic i t 0. Go to the [App] Stat/Lit Editor, the elect d the F6 [Tet], the elect : T-Tet. Chooe the iput method, data i whe you have etered data ito a lit previouly or tat whe you are give the mea ad tadard deviatio already. The type i the hypotheized mea ( 0 ), ample tadard deviatio, ample mea, ample ize, (or lit ame (lit), ad Freq: ), arrow over to the, <, > ad elect the ig that i the ame a the problem alterative hypothei tatemet the pre the [ENTER] key to calculate. The calculator retur the t-tet tatitic ad p-value. pˆ p0 Hypothei tet for oe ample populatio proportio, tet tatitic i Z. Go to the [App] p0 p0 Stat/Lit Editor, the elect d the F6 [Tet], the elect 5: -PropZ-Tet. Type i the hypotheized proportio ( p 0 ), x, ample ize, arrow over to the, <, > ig that i the ame i the problem alterative hypothei tatemet the pre the [ENTER] key to calculate. The calculator retur the z-tet tatitic ad the p-value. Note: ometime you are ot give the x value but a percetage itead. To fid the x value to ue i the calculator, multiply p by the ample ize ad roud off to the earet iteger. The calculator will give you a error meage if you put i a decimal for x or. For example if p =. ad = 4 the.*4 = 7.8, o ue x = 7. Note: you caot do the chi-quared tet for oe variace o the TI-89. Chapter 9 9. Cofidece Iterval ad Hypothei Tet for Two Populatio Mea Large Idepedet Sample 3

4 Hypothei tet for the differece betwee the mea of two populatio, X X z idepedet ample, ad σ i kow, tet tatitic i to the [App] Stat/Lit Editor, the elect d the F6 [Tet], the elect 3: -SampZ-Tet. The type i the populatio tadard deviatio, the firt ample mea ad ample ize, the the ecod ample mea ad ample ize, (or lit ame (lit3 & lit4), ad Freq: & Freq:), arrow over to the, <, > ig that i the ame i the problem alterative hypothei tatemet the pre the [ENTER] key to calculate. The calculator retur the z- tet tatitic ad the p-value. The 00( - )% cofidece iterval for the differece betwee the mea of two populatio, idepedet ample, i X X z a / to the [App] Stat/Lit Editor, the elect d the F5 [It], the elect 3: - SampZIt. The type i the populatio tadard deviatio, the firt ample mea ad ample ize, the the ecod ample mea ad ample ize, (or lit ame (lit3 & lit4), ad Freq: & Freq:), the eter the cofidece level. To calculate pre the [ENTER] key. The calculator retur the cofidece iterval. 9. Cofidece Iterval ad Hypothei Tet for Two Populatio Mea Small Idepedet Sample The 00( - )% cofidece iterval for the differece betwee the mea of two populatio, idepedet ample, ad σ are ukow i X X ta /. Go to the [App] Stat/Lit Editor, the elect d the F5 [It], the elect 4: -SampTIt. Eter the ample mea, ample tadard deviatio, ample ize, (or lit ame (lit3 & lit4), ad Freq: & Freq:), cofidece level. Highlight the No optio uder Pooled. Pre the [ENTER] key to calculate. The calculator retur the cofidece iterval. Hypothei tet for the differece betwee the mea of two populatio, idepedet ample, ad σ are ukow. The tet tatitic i X X t. Go to the [App] Stat/Lit Editor, the elect d the F6 S S [Tet], the elect 4: -SampT-Tet. Eter the ample mea, ample tadard deviatio, ad ample ize, (or lit ame (lit3 & lit4), ad Freq: & Freq:). The arrow over to the ot equal, <, > ad elect the ig that i the ame i the problem alterative hypothei tatemet. Highlight the No optio uder Pooled. Pre the [ENTER] key to calculate. The calculator retur the t-tet tatitic ad the p-value. 9.3 Cofidece Iterval ad Hypothei Tet for Small Depedet Sample (Matched Pair) Hypothei tet for the differece betwee the mea of two populatio d for depedet ample (matched pair) tet tatitic i D t D D. Firt fid the differece betwee the ample. Go to the [App] Stat/Lit Editor, the eter the differece ito lit. Select d the F6 [Tet], the elect : T-Tet. Select the [Data] meu. The type i the hypotheized mea a 0, Lit: lit, Freq:, arrow over to the, <, > ad elect the ig that i the ame i the. Go. Go 4

5 problem alterative hypothei, pre the [ENTER] key to calculate. The calculator retur the t-tet tatitic, p-value, X D ad S x S D. The 00( - )% cofidece iterval for the differece betwee the mea of two populatio d, depedet ample D (matched pair), i D t, /. Firt fid the differece betwee the ample. Go to the [App] Stat/Lit Editor, the eter the differece ito lit. Select d the F7 [It], the elect : T-Iterval. Select the [Data] meu. Eter i Lit: lit, Freq:. The type i the cofidece level. Pre the [ENTER] key to calculate. The calculator retur the cofidece iterval. 9.4 Cofidece Iterval ad Hypothei Tet for Two Populatio Proportio Hypothei tet for the differece betwee the proportio of two populatio p tet tatitic i Z pˆ pˆ p p p( p) p,. Go to the [App] Stat/Lit Editor, the elect d the F6 [Tet], the elect 6: -PropZTet. Type i the x,, x,, arrow over to the, <, > ad elect the ig that i the ame i the problem alterative hypothei tatemet. Pre the [ENTER] key to calculate. The calculator retur the z-tet tatitic, ample proportio, pooled proportio, ad the p- value. p i The 00( - )% cofidece iterval for the differece betwee the proportio of two populatio ˆ ˆ pˆ pˆ pˆ pˆ p p p Z. Go to the [App] Stat/Lit Editor, the elect d the F7 [It], the elect 6: -PropZIt. Type i the x,, x,, the cofidece level, the pre the [ENTER] key to calculate. The calculator retur the cofidece iterval. 9.5 Hypothei Tet For Two Populatio Variace Hypothei tet for two populatio variace or tadard deviatio, tet tatitic i F. Go to the [App] Stat/Lit Editor, the elect d the F6 [Tet], the elect 9: -SampFTet. The type i the,,,, (or lit ame lit & lit), elect the ig,, <, > that i the ame i the problem alterative hypothei tatemet, pre the [ENTER] key to calculate. The calculator retur the F-tet tatitic ad the p-value. Chapter 0 Simple liear regreio. Go to the [App] Stat/Lit Editor, the type i the x-value ito lit ad the y-value ito lit. Select d the F6 [Tet], the elect A:LiRegTTet. Eter the followig, Xlit: lit ; Y Lit: lit ; Freq:, elect the alterative hypothei a & 0, tore reult to: oe. Pre the [ENTER] key to calculate. The calculator retur the t-tet tatitic, the y-itercept a, lope b, = MSE, R, ad r. For a F-tet ue the Multiple Regreio tet with oly oe x lit, (idepedet variable = ). 5

6 Chapter Goode of Fit Tet Hypothei tet for three or more proportio (goode of fit tet). Go to the [App] Stat/Lit Editor, the type i the oberved value ito lit, ad the expected value ito lit. Select d the F6 [Tet], the elect 7: Chi-GOF. Type i the lit ame ad the degree of freedom (df = k-). The pre the [ENTER] key to calculate. The calculator retur the -tet tatitic ad the p-value. Tet for Idepedece Hypothei tet for the idepedece of two variable (cotigecy table). Firt you eed to create the matrix for the oberved value: Pre: [Home] to retur to the Home cree, pre [App] ad elect 6:Data/Matrix Editor. A meu i diplayed, elect 3:New. The New dialog box i diplayed. Pre the right arrow key to highlight :Matrix, ad pre [ENTER] to chooe Matrix type. Pre the dow arrow key to highlight :mai, ad pre [ENTER], to chooe mai folder. Pre the dow arrow key, ad the eter the ame o i the Variable field. Eter 3 for Row dimeio ad for Colum dimeio. Pre [ENTER] to diplay the matrix editor. Eter 4, 9, 5 i c ad 7,, 3 i c. Pre [App] [ENTER] to cloe the matrix editor ad retur to the lit editor. If you have more tha oe Applicatio loaded, pre [App], ad the elect Stat/Lit Editor. To diplay the Chi-quare -Way dialog box, pre d the F6 [Tet], the elect 8: Chi- -way. Eter i i the Oberved Mat: o ; Store Expected to: tatvar\e ; Store CompMat to: tatvar\c. Thi will tore the expected value i the matrix folder tatvar with the ame e, ad the (oe) /e value i the matrix c. Pre the [ENTER] key to calculate. The calculator retur the -tet tatitic ad the p-value. If you go back to the matrix meu you will ee all of the expected ad (o-e) /e value. Chapter Aalyi of Variace ANOVA, hypothei tet for the equality of k populatio mea. ). Go to the [App] Stat/Lit Editor, the type i the data for each group ito a eparate lit, (or if you do t have the raw data, eter the ample ize, ample mea ad ample variace for group ito lit i that order, repeat for lit, etc). Select d the F6 [Tet], the elect C:ANOVA. Select the iput method data or tat. Select the umber of group. Pre the [ENTER] key to calculate. The calculator retur the F-tet tatitic, the p-value, Factor (Betwee) df, SS ad MS, Error (Withi) df, SS ad MS. The lat value Sxp i the quare root of the MSE. The calculator will alo do a Two-way ANOVA block deig. 6

TI-83, TI-83 Plus or TI-84 for Non-Business Statistics

TI-83, TI-83 Plus or TI-84 for Non-Business Statistics TI-83, TI-83 Plu or TI-84 for No-Buie Statitic Chapter 3 Eterig Data Pre [STAT] the firt optio i already highlighted (:Edit) o you ca either pre [ENTER] or. Make ure the curor i i the lit, ot o the lit

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

Topic 5: Confidence Intervals (Chapter 9)

Topic 5: Confidence Intervals (Chapter 9) Topic 5: Cofidece Iterval (Chapter 9) 1. Itroductio The two geeral area of tatitical iferece are: 1) etimatio of parameter(), ch. 9 ) hypothei tetig of parameter(), ch. 10 Let X be ome radom variable with

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

Sampling Distribution And Central Limit Theorem

Sampling Distribution And Central Limit Theorem () Samplig Distributio & Cetral Limit Samplig Distributio Ad Cetral Limit Samplig distributio of the sample mea If we sample a umber of samples (say k samples where k is very large umber) each of size,

More information

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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: 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

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

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

More examples for Hypothesis Testing

More examples for Hypothesis Testing More example for Hypothei Tetig Part I: Compoet 1. Null ad alterative hypothee a. The ull hypothee (H 0 ) i a tatemet that the value of a populatio parameter (mea) i equal to ome claimed value. Ex H 0:

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

Math C067 Sampling Distributions

Math C067 Sampling Distributions Math C067 Samplig Distributios Sample Mea ad Sample Proportio Richard Beigel Some time betwee April 16, 2007 ad April 16, 2007 Examples of Samplig A pollster may try to estimate the proportio of voters

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

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

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

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

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

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

Confidence Intervals

Confidence Intervals Cofidece Itervals Cofidece Itervals are a extesio of the cocept of Margi of Error which we met earlier i this course. Remember we saw: The sample proportio will differ from the populatio proportio by more

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

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

Confidence Intervals (2) QMET103

Confidence Intervals (2) QMET103 Cofidece Iterval () QMET03 Library, Teachig ad Learig Geeral Remember: three value are ued to cotruct all cofidece iterval: Samle tatitic Z or t Stadard error of amle tatitic Deciio ad Parameter to idetify:

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

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

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

0.674 0.841 1.036 1.282 1.645 1.960 2.054 2.326 2.576 2.807 3.091 3.291 50% 60% 70% 80% 90% 95% 96% 98% 99% 99.5% 99.8% 99.9%

0.674 0.841 1.036 1.282 1.645 1.960 2.054 2.326 2.576 2.807 3.091 3.291 50% 60% 70% 80% 90% 95% 96% 98% 99% 99.5% 99.8% 99.9% Sectio 10 Aswer Key: 0.674 0.841 1.036 1.282 1.645 1.960 2.054 2.326 2.576 2.807 3.091 3.291 50% 60% 70% 80% 90% 95% 96% 98% 99% 99.5% 99.8% 99.9% 1) A simple radom sample of New Yorkers fids that 87 are

More information

Stats on the TI 83 and TI 84 Calculator

Stats on the TI 83 and TI 84 Calculator Stats on the TI 83 and TI 84 Calculator Entering the sample values STAT button Left bracket { Right bracket } Store (STO) List L1 Comma Enter Example: Sample data are {5, 10, 15, 20} 1. Press 2 ND and

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

Chapter 14 Nonparametric Statistics

Chapter 14 Nonparametric Statistics Chapter 14 Noparametric Statistics A.K.A. distributio-free statistics! Does ot deped o the populatio fittig ay particular type of distributio (e.g, ormal). Sice these methods make fewer assumptios, they

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

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

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

, a Wishart distribution with n -1 degrees of freedom and scale matrix.

, a Wishart distribution with n -1 degrees of freedom and scale matrix. UMEÅ UNIVERSITET Matematisk-statistiska istitutioe Multivariat dataaalys D MSTD79 PA TENTAMEN 004-0-9 LÖSNINGSFÖRSLAG TILL TENTAMEN I MATEMATISK STATISTIK Multivariat dataaalys D, 5 poäg.. Assume that

More information

STATISTICAL METHODS FOR BUSINESS

STATISTICAL METHODS FOR BUSINESS STATISTICAL METHODS FOR BUSINESS UNIT 7: INFERENTIAL TOOLS. DISTRIBUTIONS ASSOCIATED WITH SAMPLING 7.1.- Distributios associated with the samplig process. 7.2.- Iferetial processes ad relevat distributios.

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

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

hp calculators HP 12C Statistics - average and standard deviation Average and standard deviation concepts HP12C average and standard deviation

hp calculators HP 12C Statistics - average and standard deviation Average and standard deviation concepts HP12C average and standard deviation HP 1C Statistics - average ad stadard deviatio Average ad stadard deviatio cocepts HP1C average ad stadard deviatio Practice calculatig averages ad stadard deviatios with oe or two variables HP 1C Statistics

More information

POWER ANALYSIS OF INDEPENDENCE TESTING FOR CONTINGENCY TABLES

POWER ANALYSIS OF INDEPENDENCE TESTING FOR CONTINGENCY TABLES ZESZYTY NAUKOWE AKADEMII MARYNARKI WOJENNEJ SCIENTIFIC JOURNAL OF POLISH NAVAL ACADEMY 05 (LVI) (00) Piotr Sulewki, Ryzard Motyka DOI: 0.5604/0860889X.660 POWER ANALYSIS OF INDEPENDENCE TESTING FOR CONTINGENCY

More information

OMG! Excessive Texting Tied to Risky Teen Behaviors

OMG! Excessive Texting Tied to Risky Teen Behaviors BUSIESS WEEK: EXECUTIVE EALT ovember 09, 2010 OMG! Excessive Textig Tied to Risky Tee Behaviors Kids who sed more tha 120 a day more likely to try drugs, alcohol ad sex, researchers fid TUESDAY, ov. 9

More information

Independent Samples T- test

Independent Samples T- test Independent Sample T- tet With previou tet, we were intereted in comparing a ingle ample with a population With mot reearch, you do not have knowledge about the population -- you don t know the population

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

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

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

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

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

Incremental calculation of weighted mean and variance

Incremental calculation of weighted mean and variance Icremetal calculatio of weighted mea ad variace Toy Fich faf@cam.ac.uk dot@dotat.at Uiversity of Cambridge Computig Service February 009 Abstract I these otes I eplai how to derive formulae for umerically

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

2-3 The Remainder and Factor Theorems

2-3 The Remainder and Factor Theorems - The Remaider ad Factor Theorems Factor each polyomial completely usig the give factor ad log divisio 1 x + x x 60; x + So, x + x x 60 = (x + )(x x 15) Factorig the quadratic expressio yields x + x x

More information

Descriptive Statistics

Descriptive Statistics Descriptive Statistics We leared to describe data sets graphically. We ca also describe a data set umerically. Measures of Locatio Defiitio The sample mea is the arithmetic average of values. We deote

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

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

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

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

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

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

Desktop Management. Desktop Management Tools

Desktop Management. Desktop Management Tools Desktop Maagemet 9 Desktop Maagemet Tools Mac OS X icludes three desktop maagemet tools that you might fid helpful to work more efficietly ad productively: u Stacks puts expadable folders i the Dock. Clickig

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

STA 2023 Practice Questions Exam 2 Chapter 7- sec 9.2. Case parameter estimator standard error Estimate of standard error

STA 2023 Practice Questions Exam 2 Chapter 7- sec 9.2. Case parameter estimator standard error Estimate of standard error STA 2023 Practice Questios Exam 2 Chapter 7- sec 9.2 Formulas Give o the test: Case parameter estimator stadard error Estimate of stadard error Samplig Distributio oe mea x s t (-1) oe p ( 1 p) CI: prop.

More information

Basic Elements of Arithmetic Sequences and Series

Basic Elements of Arithmetic Sequences and Series MA40S PRE-CALCULUS UNIT G GEOMETRIC SEQUENCES CLASS NOTES (COMPLETED NO NEED TO COPY NOTES FROM OVERHEAD) Basic Elemets of Arithmetic Sequeces ad Series Objective: To establish basic elemets of arithmetic

More information

1) Assume that the sample is an SRS. The problem state that the subjects were randomly selected.

1) Assume that the sample is an SRS. The problem state that the subjects were randomly selected. 12.1 Homework for t Hypothei Tet 1) Below are the etimate of the daily intake of calcium in milligram for 38 randomly elected women between the age of 18 and 24 year who agreed to participate in a tudy

More information

Sequences and Series Using the TI-89 Calculator

Sequences and Series Using the TI-89 Calculator RIT Calculator Site Sequeces ad Series Usig the TI-89 Calculator Norecursively Defied Sequeces A orecursively defied sequece is oe i which the formula for the terms of the sequece is give explicitly. For

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

Multi-server Optimal Bandwidth Monitoring for QoS based Multimedia Delivery Anup Basu, Irene Cheng and Yinzhe Yu

Multi-server Optimal Bandwidth Monitoring for QoS based Multimedia Delivery Anup Basu, Irene Cheng and Yinzhe Yu Multi-server Optimal Badwidth Moitorig for QoS based Multimedia Delivery Aup Basu, Iree Cheg ad Yizhe Yu Departmet of Computig Sciece U. of Alberta Architecture Applicatio Layer Request receptio -coectio

More information

Introduction to Hypothesis Testing

Introduction to Hypothesis Testing Iroducio o Hyohei Teig Iroducio o Hyohei Teig Scieific Mehod. Sae a reearch hyohei or oe a queio.. Gaher daa or evidece (obervaioal or eerimeal) o awer he queio. 3. Summarize daa ad e he hyohei. 4. Draw

More information

T-test for dependent Samples. Difference Scores. The t Test for Dependent Samples. The t Test for Dependent Samples. s D

T-test for dependent Samples. Difference Scores. The t Test for Dependent Samples. The t Test for Dependent Samples. s D The t Tet for ependent Sample T-tet for dependent Sample (ak.a., Paired ample t-tet, Correlated Group eign, Within- Subject eign, Repeated Meaure,.. Repeated-Meaure eign When you have two et of core from

More information

Basic statistics formulas

Basic statistics formulas Wth complmet of tattcmetor.com, the te for ole tattc help Set De Morga Law Bac tattc formula Meaure of Locato Sample mea (AUB) c A c B c Commutatvty & (A B) c A c U B c A U B B U A ad A B B A Aocatvty

More information

Lecture 4: Cauchy sequences, Bolzano-Weierstrass, and the Squeeze theorem

Lecture 4: Cauchy sequences, Bolzano-Weierstrass, and the Squeeze theorem Lecture 4: Cauchy sequeces, Bolzao-Weierstrass, ad the Squeeze theorem The purpose of this lecture is more modest tha the previous oes. It is to state certai coditios uder which we are guarateed that limits

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

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 haupt@ieee.org Abstract:

More information

Infinite Sequences and Series

Infinite Sequences and Series CHAPTER 4 Ifiite Sequeces ad Series 4.1. Sequeces A sequece is a ifiite ordered list of umbers, for example the sequece of odd positive itegers: 1, 3, 5, 7, 9, 11, 13, 15, 17, 19, 21, 23, 25, 27, 29...

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

BINOMIAL EXPANSIONS 12.5. In this section. Some Examples. Obtaining the Coefficients

BINOMIAL EXPANSIONS 12.5. In this section. Some Examples. Obtaining the Coefficients 652 (12-26) Chapter 12 Sequeces ad Series 12.5 BINOMIAL EXPANSIONS I this sectio Some Examples Otaiig the Coefficiets The Biomial Theorem I Chapter 5 you leared how to square a iomial. I this sectio you

More information

This document contains a collection of formulas and constants useful for SPC chart construction. It assumes you are already familiar with SPC.

This document contains a collection of formulas and constants useful for SPC chart construction. It assumes you are already familiar with SPC. SPC Formulas ad Tables 1 This documet cotais a collectio of formulas ad costats useful for SPC chart costructio. It assumes you are already familiar with SPC. Termiology Geerally, a bar draw over a symbol

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

ISO tadard o determiatio of the Detectio Limit ad Deciio threhod for ioiig radiatio meauremet ISO/CD 1199-1: Determiatio of the Detectio Limit ad Deci

ISO tadard o determiatio of the Detectio Limit ad Deciio threhod for ioiig radiatio meauremet ISO/CD 1199-1: Determiatio of the Detectio Limit ad Deci ICRM Gamma Spectrometry Workig Group Workhop Pari, Laoratoire atioa d Eai 3-4 Feruary 9 Detectio imit Deciio threhod Appicatio of ISO 1199 P. De Feice EEA atioa Ititute for Ioiig Radiatio Metroogy defeice@caaccia.eea.it.1/13

More information

Time Value of Money. First some technical stuff. HP10B II users

Time Value of Money. First some technical stuff. HP10B II users Time Value of Moey Basis for the course Power of compoud iterest $3,600 each year ito a 401(k) pla yields $2,390,000 i 40 years First some techical stuff You will use your fiacial calculator i every sigle

More information

A Test of Normality. 1 n S 2 3. n 1. Now introduce two new statistics. The sample skewness is defined as:

A Test of Normality. 1 n S 2 3. n 1. Now introduce two new statistics. The sample skewness is defined as: A Test of Normality Textbook Referece: Chapter. (eighth editio, pages 59 ; seveth editio, pages 6 6). The calculatio of p values for hypothesis testig typically is based o the assumptio that the populatio

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

BaanERP. BaanERP Windows Client Installation Guide

BaanERP. BaanERP Windows Client Installation Guide BaaERP A publicatio of: Baa Developmet B.V. P.O.Box 143 3770 AC Bareveld The Netherlads Prited i the Netherlads Baa Developmet B.V. 1999. All rights reserved. The iformatio i this documet is subject to

More information

S. Tanny MAT 344 Spring 1999. be the minimum number of moves required.

S. Tanny MAT 344 Spring 1999. be the minimum number of moves required. S. Tay MAT 344 Sprig 999 Recurrece Relatios Tower of Haoi Let T be the miimum umber of moves required. T 0 = 0, T = 7 Iitial Coditios * T = T + $ T is a sequece (f. o itegers). Solve for T? * is a recurrece,

More information

THE TWO-VARIABLE LINEAR REGRESSION MODEL

THE TWO-VARIABLE LINEAR REGRESSION MODEL THE TWO-VARIABLE LINEAR REGRESSION MODEL Herma J. Bieres Pesylvaia State Uiversity April 30, 202. Itroductio Suppose you are a ecoomics or busiess maor i a college close to the beach i the souther part

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 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

Chair for Network Architectures and Services Institute of Informatics TU München Prof. Carle. Network Security. Chapter 2 Basics

Chair for Network Architectures and Services Institute of Informatics TU München Prof. Carle. Network Security. Chapter 2 Basics Chair for Network Architectures ad Services Istitute of Iformatics TU Müche Prof. Carle Network Security Chapter 2 Basics 2.4 Radom Number Geeratio for Cryptographic Protocols Motivatio It is crucial to

More information

A Mathematical Perspective on Gambling

A Mathematical Perspective on Gambling A Mathematical Perspective o Gamblig Molly Maxwell Abstract. This paper presets some basic topics i probability ad statistics, icludig sample spaces, probabilistic evets, expectatios, the biomial ad ormal

More information

Repeating Decimals are decimal numbers that have number(s) after the decimal point that repeat in a pattern.

Repeating Decimals are decimal numbers that have number(s) after the decimal point that repeat in a pattern. 5.5 Fractios ad Decimals Steps for Chagig a Fractio to a Decimal. Simplify the fractio, if possible. 2. Divide the umerator by the deomiator. d d Repeatig Decimals Repeatig Decimals are decimal umbers

More information