STATISTICS AND PROBABILITY

Size: px
Start display at page:

Download "STATISTICS AND PROBABILITY"

Transcription

1 CHAPTER 4 STATISTICS AND PROBABILITY (A) Mai Cocepts ad Results Statistics Meaig of statistics, Primary ad secodary data, Raw/ugrouped data, Rage of data, Grouped data-class itervals, Class marks, Presetatio of data - frequecy distributio table, Discrete frequecy distributio ad cotiuous frequecy distributio. Graphical represetatio of data : (i) Bar graphs (ii) Histograms of uiform width ad of varyig widths (iii) Frequecy polygos Measures of Cetral tedecy (a) (i) Mea Mea of raw data xi x + x x i= Mea = x = = where x, x 2,..., x are observatios.

2 30 EXEMPLAR PROBLEMS (ii) Mea of ugrouped data x (b) Media f x i = f i i where f i s are frequecies of x i s. A media is the value of the observatio which divides the data ito two equal parts, whe the data is arraged i ascedig (or descedig) order. Calculatio of Media Whe the ugrouped data is arraged i ascedig (or descedig) order, the media of data is calculated as follows : (i) (ii) (c) Mode Whe the umber of observatios () is odd, the media is the value of the + 2 th observatio. Whe the umber of observatios () is eve, the media is the average or mea of the 2 th ad + 2 th observatios. The observatio that occurs most frequetly, i.e., the observatio with maximum frequecy is called mode. Mode of ugrouped data ca be determied by observatio/ ispectio. Probability Radom experimet or simply a experimet Outcomes of a experimet Meaig of a trial of a experimet The experimetal (or empirical) probability of a evet E (deoted by P(E)) is give by P(E) = Number of trials i which the evet has happeed Total umber of trials The probability of a evet E ca be ay umber from 0 to. It ca also be 0 or i some special cases.

3 STATISTICS AND PROBABILITY 3 (B)Multiple Choice Questios Write the correct aswer i each of the followig : Sample Questio : The marks obtaied by 7 studets i a mathematics test (out of 00) are give below : 9, 82, 00, 00, 96, 65, 82, 76, 79, 90, 46, 64, 72, 68, 66, 48, 49. The rage of the data is : (A) 46 (B) 54 (C) 90 (D) 00 Solutio : Aswer (B) Sample Questio 2: The class-mark of the class is : (A) 30 (B) 35 (C) 40 (D) 45 Solutio : Aswer (C) Sample Questio 3 : A die is throw 000 times ad the outcomes were recorded as follows : Outcome Frequecy If the die is throw oce more, the the probability that it shows 5 is : (A) 9 50 Solutio : Aswer (B) (B) 3 20 (C) EXERCISE 4. Write the correct aswer i each of the followig :. The class mark of the class is : (A) 90 (B) 05 (C) 5 (D) The rage of the data : 25, 8, 20, 22, 6, 6, 7, 5, 2, 30, 32, 0, 9, 8,, 20 is 4 25 (D) (A) 0 (B) 5 (C) 8 (D) I a frequecy distributio, the mid value of a class is 0 ad the width of the class is 6. The lower limit of the class is : (A) 6 (B) 7 (C) 8 (D)

4 32 EXEMPLAR PROBLEMS 4. The width of each of five cotiuous classes i a frequecy distributio is 5 ad the lower class-limit of the lowest class is 0. The upper class-limit of the highest class is: (A) 5 (B) 25 (C) 35 (D) Let m be the mid-poit ad l be the upper class limit of a class i a cotiuous frequecy distributio. The lower class limit of the class is : (A) 2m + l (B) 2m l (C) m l (D) m 2l 6. The class marks of a frequecy distributio are give as follows : 5, 20, 25,... The class correspodig to the class mark 20 is : (A) (B) (C) (D) I the class itervals 0-20, 20-30, the umber 20 is icluded i : (A) 0-20 (B) (C) both the itervals (D) oe of these itervals 8. A grouped frequecy table with class itervals of equal sizes usig (270 ot icluded i this iterval) as oe of the class iterval is costructed for the followig data : 268, 220, 368, 258, 242, 30, 272, 342, 30, 290, 300, 320, 39, 304, 402, 38, 406, 292, 354, 278, 20, 240, 330, 36, 406, 25, 258, 236. The frequecy of the class is: (A) 4 (B) 5 (C) 6 (D) 7 9. A grouped frequecy distributio table with classes of equal sizes usig (72 icluded) as oe of the class is costructed for the followig data : 30, 32, 45, 54, 74, 78, 08, 2, 66, 76, 88, 40, 4, 20, 5, 35, 44, 66, 75, 84, 95, 96, 02, 0, 88, 74, 2, 4, 34, 44. The umber of classes i the distributio will be : (A) 9 (B) 0 (C) (D) 2 0. To draw a histogram to represet the followig frequecy distributio : Class iterval Frequecy

5 STATISTICS AND PROBABILITY 33 the adjusted frequecy for the class is : (A) 6 (B) 5 (C) 3 (D) 2. The mea of five umbers is 30. If oe umber is excluded, their mea becomes 28. The excluded umber is : (A) 28 (B) 30 (C) 35 (D) If the mea of the observatios : x, x + 3, x + 5, x + 7, x + 0 is 9, the mea of the last three observatios is (A) 0 3 (B) (C) 3 (D) If x represets the mea of observatios x, x 2,..., x, the value of ( xi x) is: (A) (B) 0 (C) (D) 4. If each observatio of the data is icreased by 5, the their mea (A) remais the same (B) becomes 5 times the origial mea (C) is decreased by 5 (D) is icreased by 5 5. Let x be the mea of x, x 2,..., x ad y the mea of y, y 2,..., y. If z is the mea of x, x 2,..., x, y, y 2,..., y, the z is equal to (A) x + y (B) x + y 2 (C) x + y (D) i = x + y 2 6. If x is the mea of x, x 2,..., x, the for a 0, the mea of ax, ax 2,..., ax, x a, x 2 a,..., x a is (A) a + x a (B) x a + a 2 (C) x a + a (D) a + x a 2 7. If x, x 2, x 3,..., x are the meas of groups with, 2,..., umber of observatios respectively, the the mea x of all the groups take together is give by :

6 34 EXEMPLAR PROBLEMS (A) i = x i i (B) x i i i= 2 (C) i i i= i= x i i x i= (D) 2 i 8. The mea of 00 observatios is 50. If oe of the observatios which was 50 is replaced by 50, the resultig mea will be : (A) 50.5 (B) 5 (C) 5.5 (D) There are 50 umbers. Each umber is subtracted from 53 ad the mea of the umbers so obtaied is foud to be 3.5. The mea of the give umbers is : (A) 46.5 (B) 49.5 (C) 53.5 (D) The mea of 25 observatios is 36. Out of these observatios if the mea of first 3 observatios is 32 ad that of the last 3 observatios is 40, the 3 th observatio is : (A) 23 (B) 36 (C) 38 (D) The media of the data 78, 56, 22, 34, 45, 54, 39, 68, 54, 84 is (A) 45 (B) 49.5 (C) 54 (D) For drawig a frequecy polygo of a cotious frequecy distributio, we plot the poits whose ordiates are the frequecies of the respective classes ad abcissae are respectively : (A) upper limits of the classes (B) lower limits of the classes (C) class marks of the classes (D) upper limits of perceedig classes 23. Media of the followig umbers : 4, 4, 5, 7, 6, 7, 7, 2, 3 is (A) 4 (B) 5 (C) 6 (D) Mode of the data 5, 4, 9, 20, 4, 5, 6, 4, 5, 8, 4, 9, 5, 7, 5 is (A) 4 (B) 5 (C) 6 (D) I a sample study of 642 people, it was foud that 54 people have a high school certificate. If a perso is selected at radom, the probability that the perso has a high school certificate is : (A) 0.5 (B) 0.6 (C) 0.7 (D) 0.8

7 STATISTICS AND PROBABILITY I a survey of 364 childre aged 9-36 moths, it was foud that 9 liked to eat potato chips. If a child is selected at radom, the probability that he/she does ot like to eat potato chips is: (A) 0.25 (B) 0.50 (C) 0.75 (D) I a medical examiatio of studets of a class, the followig blood groups are recorded: Blood group A AB B O Number of studets A studet is selected at radom from the class. The probability that he/she has blood group B, is: 3 3 (A) (B) (C) (D) Two cois are tossed 000 times ad the outcomes are recorded as below : Number of heads 2 0 Frequecy Based o this iformatio, the probability for at most oe head is (A) 5 (B) bulbs are selected at radom from a lot ad their life time (i hrs) is recorded i the form of a frequecy table give below : (C) 4 5 (D) Life time (i hours) Frequecy Oe bulb is selected at radom from the lot. The probability that its life is 50 hours, is (A) 80 (B) 7 6 (C) 0 (D) 3 4

8 36 EXEMPLAR PROBLEMS 30. Refer to Q.29 above : The probability that bulbs selected radomly from the lot has life less tha 900 hours is : (A) 40 (B) 5 6 (C) 7 6 (D) 9 6 (C) Short Aswer Questios with Reasoig Sample Questio : The mea of the data : 2, 8, 6, 5, 4, 5, 6, 3, 6, 4, 9,, 5, 6, 5 is give to be 5. Based o this iformatio, is it correct to say that the mea of the data: 0, 2, 0, 2, 8, 8, 2, 6, 2, 0, 8, 0, 2, 6, 4 is 0? Give reaso. Solutio : It is correct. Sice the 2d data is obtaied by multiplyig each observatio of st data by 2, therefore, the mea will be 2 times the mea of the st data. Sample Questio 2 : I a histogram, the areas of the rectagles are proportioal to the frequecies. Ca we say that the legths of the rectagles are also proportioal to the frequecies? Solutio: No. It is true oly whe the class sizes are the same. Sample Quetio 3 : Cosider the data : 2, 3, 9, 6, 9, 3, 9. Sice 6 is the highest value i the observatios, is it correct to say that it is the mode of the data? Give reaso. Solutio : 6 is ot the mode of the data. The mode of a give data is the observatio with highest frequecy ad ot the observatio with highest value.. The frequecy distributio : EXERCISE 4.2 Marks Number of Studets has bee represeted graphically as follows :

9 STATISTICS AND PROBABILITY 37 Fig. 4. Do you thik this represetatio is correct? Why? 2. I a diagostic test i mathematics give to studets, the followig marks (out of 00) are recorded: 46, 52, 48,, 4, 62, 54, 53, 96, 40, 98, 44 Which average will be a good represetative of the above data ad why? 3. A child says that the media of 3, 4, 8, 20, 5 is 8. What does t the child uderstad about fidig the media? 4. A football player scored the followig umber of goals i the 0 matches :, 3, 2, 5, 8, 6,, 4, 7, 9 Sice the umber of matches is 0 (a eve umber), therefore, the media = th 5 observatio + 6 observatio 2 th = = 7 2 Is it the correct aswer ad why? 5. Is it correct to say that i a histogram, the area of each rectagle is proportioal to the class size of the correspodig class iterval? If ot, correct the statemet. 6. The class marks of a cotiuous distributio are :.04,.4,.24,.34,.44,.54 ad.64 Is it correct to say that the last iterval will be ? Justify your aswer.

10 38 EXEMPLAR PROBLEMS childre were asked about the umber of hours they watched TV programmes last week. The results are recorded as uder : Number of hours Frequecy Ca we say that the umber of childre who watched TV for 0 or more hours a week is 22? Justify your aswer. 8. Ca the experimetal probability of a evet be a egative umber? If ot, why? 9. Ca the experimetal probability of a evet be greater tha? Justify your awer. 0. As the umber of tosses of a coi icreases, the ratio of the umber of heads to the total umber of tosses will be. Is it correct? If ot, write the correct oe. 2 (D) Short Aswer Questios Sample Questio : Heights (i cm) of 30 girls of Class IX are give below: 40, 40, 60, 39, 53, 53, 46, 50, 48, 50, 52, 46, 54, 50, 60, 48, 50, 48, 40, 48, 53, 38, 52, 50, 48, 38, 52, 40, 46, 48. Prepare a frequecy distributio table for this data. Solutio : Frequecy distributio of heights of 30 girls Height Tally Marks Frequecy (i cm) Total 30

11 STATISTICS AND PROBABILITY 39 Sample Questio 2 : The followig observatios are arraged i ascedig order : 26, 29, 42, 53, x, x + 2, 70, 75, 82, 93 If the media is 65, fid the value of x. Solutio : Number of observatios () = 0, which is eve. Therefore, media is the mea of 2 Here, th ad + 2 th observatio, i.e., 5 th ad 6 th observatio. 5 th observatio = x 6 th observatio = x + 2 Now, Media = x + ( x + 2) = x + 2 x + = 65 (Give) Therefore, x = 64 Thus, the value of x is 64. Sample Questio 3 : Here is a extract from a mortality table. (i) (ii) Solutio : (i) Age (i years) Number of persos survivig out of a sample of oe millio Based o this iformatio, what is the probability of a perso aged 60 of dyig withi a year? What is the probability that a perso aged 6 will live for 4 years? We see that 6090 persos aged 60, ( ), i.e., 4600 died before reachig their 6 st birthday. Therefore, P(a perso aged 60 die withi a year) = =

12 40 EXEMPLAR PROBLEMS (ii) Number of persos aged 6 years = 490 Number of persos survivig for 4 years = 2320 P(a perso aged 6 will live for 4 years) = = EXERCISE 4.3. The blood groups of 30 studets are recorded as follows: A, B, O, A, AB, O, A, O, B, A, O, B, A, AB, B, A, AB, B, A, A, O, A, AB, B, A, O, B, A, B, A Prepare a frequecy distributio table for the data. 2. The value of π upto 35 decimal places is give below: Make a frequecy distributio of the digits 0 to 9 after the decimal poit. 3. The scores (out of 00) obtaied by 33 studets i a mathematics test are as follows: 69, 48, 84, 58, 48, 73, 83, 48, 66, 58, , 64, 7, 64, 66, 69, 66, 83, 66, 69, 7 8, 7, 73, 69, 66, 66, 64, 58, 64, 69, 69 Represet this data i the form of a frequecy distributio. 4. Prepare a cotiuous grouped frequecy distributio from the followig data: Mid-poit Frequecy Also fid the size of class itervals. 5. Covert the give frequecy distributio ito a cotiuous grouped frequecy distributio:

13 STATISTICS AND PROBABILITY 4 Class iterval Frequecy I which itervals would 53.5 ad 57.5 be icluded? 6. The expediture of a family o differet heads i a moth is give below: Head Food Educatio Clothig House Ret Others Savigs Expediture (i Rs) Draw a bar graph to represet the data above. 7. Expediture o Educatio of a coutry durig a five year period ( ), i crores of rupees, is give below: Elemetary educatio 240 Secodary Educatio 20 Uiversity Educatio 90 Teacher s Traiig 20 Social Educatio 0 Other Educatioal Programmes 5 Cultural programmes 25 Techical Educatio 25 Represet the iformatio above by a bar graph. 8. The followig table gives the frequecies of most commoly used letters a, e, i, o, r, t, u from a page of a book : Letters a e i o r t u Frequecy Represet the iformatio above by a bar graph.

14 42 EXEMPLAR PROBLEMS 9. If the mea of the followig data is 20.2, fid the value of p: x f 6 8 p Obtai the mea of the followig distributio: Frequecy Variable A class cosists of 50 studets out of which 30 are girls. The mea of marks scored by girls i a test is 73 (out of 00) ad that of boys is 7. Determie the mea score of the whole class. 2. Mea of 50 observatios was foud to be But later o, it was discovered that 96 was misread as 69 at oe place. Fid the correct mea. 3. Te observatios 6, 4, 5, 7, x +, 2x 3, 30, 32, 34, 43 are writte i a ascedig order. The media of the data is 24. Fid the value of x. 4. The poits scored by a basket ball team i a series of matches are as follows: 7, 2, 7, 27, 25, 5, 4, 8, 0, 24, 48, 0, 8, 7, 0, 28 Fid the media ad mode for the data. 5. I Fig. 4.2, there is a histogram depictig daily wages of workers i a factory. Costruct the frequecy distributio table. Fig. 4.2

15 STATISTICS AND PROBABILITY A compay selected 4000 households at radom ad surveyed them to fid out a relatioship betwee icome level ad the umber of televisio sets i a home. The iformatio so obtaied is listed i the followig table: Fid the probability: (i) (ii) (iii) Mothly icome Number of Televisios/household (i Rs) 0 2 Above 2 < ad above of a household earig Rs 0000 Rs 4999 per year ad havig exactly oe televisio. of a household earig Rs ad more per year ad owig 2 televisios. of a household ot havig ay televisio. 7. Two dice are throw simultaeously 500 times. Each time the sum of two umbers appearig o their tops is oted ad recorded as give i the followig table: Sum Frequecy

16 44 EXEMPLAR PROBLEMS If the dice are throw oce more, what is the probability of gettig a sum (i) 3? (ii) more tha 0? (iii) less tha or equal to 5? (iv) betwee 8 ad 2? 8. Bulbs are packed i cartos each cotaiig 40 bulbs. Seve hudred cartos were examied for defective bulbs ad the results are give i the followig table: Number of defective bulbs more tha 6 Frequecy Oe carto was selected at radom. What is the probability that it has (i) o defective bulb? (ii) defective bulbs from 2 to 6? (iii) defective bulbs less tha 4? 9. Over the past 200 workig days, the umber of defective parts produced by a machie is give i the followig table: Number of defective parts Days Determie the probability that tomorrow s output will have (i) (ii) (iii) (iv) o defective part atleast oe defective part ot more tha 5 defective parts more tha 3 defective parts 20. A recet survey foud that the ages of workers i a factory is distributed as follows: Age (i years) ad above Number of workers If a perso is selected at radom, fid the probability that the perso is: (i) (ii) 40 years or more uder 40 years

17 STATISTICS AND PROBABILITY 45 (iii) (iv) havig age from 30 to 39 years uder 60 but over 39 years (E) Log Aswer Questios Sample Questio : Followig is the frequecy distributio of total marks obtaied by the studets of differet sectios of Class VIII. Marks Number of studets Draw a histogram for the distributio above. Solutio: I the give frequecy distributio, the class itervals are ot of equal width. Therefore, we would make modificatios i the legths of the rectagles i the histogram so that the areas of rectagles are proportioal to the frequecies. Thus, we have: Marks Frequecy Width of the class Legth of the rectagle = = = = = Now, we draw rectagles with legths as give i the last colum. The histogram of the data is give below :

18 46 EXEMPLAR PROBLEMS Fig. 4.3 Sample Questio 2 : Two sectios of Class IX havig 30 studets each appeared for mathematics olympiad. The marks obtaied by them are show below: Costruct a group frequecy distributio of the data above usig the classes 0-9, 0-9 etc., ad hece fid the umber of studets who secured more tha 49 marks. Solutio : Class Tally Marks Frequecy Total 60

19 STATISTICS AND PROBABILITY 47 From the table above, we fid that the umber of studets who secure more tha 49 marks is ( ), i.e., 32. EXERCISE 4.4. The followig are the marks (out of 00) of 60 studets i mathematics. 6, 3, 5, 80, 86, 7, 5, 48, 24, 56, 70, 9, 6, 7, 6, 36, 34, 42, 34, 35, 72, 55, 75, 3, 52, 28,72, 97, 74, 45, 62, 68, 86, 35, 85, 36, 8, 75, 55, 26, 95, 3, 7, 78, 92, 62, 52, 56, 5, 63,25, 36, 54, 44, 47, 27, 72, 7, 4, 30. Costruct a grouped frequecy distributio table with width 0 of each class startig from Refer to Q above. Costruct a grouped frequecy distributio table with width 0 of each class, i such a way that oe of the classes is 0-20 (20 ot icluded). 3. Draw a histogram of the followig distributio : Heights (i cm) Number of studets Draw a histogram to represet the followig grouped frequecy distributio : Ages (i years) Number of teachers

20 48 EXEMPLAR PROBLEMS 5. The legths of 62 leaves of a plat are measured i millimetres ad the data is represeted i the followig table : Legth (i mm) Number of leaves Draw a histogram to represet the data above. 6. The marks obtaied (out of 00) by a class of 80 studets are give below : Marks Number of studets Costruct a histogram to represet the data above. 7. Followig table shows a frequecy distributio for the speed of cars passig through at a particular spot o a high way : Class iterval (km/h) Frequecy Draw a histogram ad frequecy polygo represetig the data above.

21 STATISTICS AND PROBABILITY Refer to Q. 7 : Draw the frequecy polygo represetig the above data without drawig the histogram. 9. Followig table gives the distributio of studets of sectios A ad B of a class accordig to the marks obtaied by them. Sectio A Sectio B Marks Frequecy Marks Frequecy Represet the marks of the studets of both the sectios o the same graph by two frequecy polygos.what do you observe? 0. The mea of the followig distributio is 50. x f a a 90 9 Fid the value of a ad hece the frequecies of 30 ad 70.. The mea marks (out of 00) of boys ad girls i a examiatio are 70 ad 73, respectively. If the mea marks of all the studets i that examiatio is 7, fid the ratio of the umber of boys to the umber of girls. 2. A total of 25 patiets admitted to a hospital are tested for levels of blood sugar, (mg/dl) ad the results obtaied were as follows : Fid mea, media ad mode (mg/dl) of the above data.

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

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

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

Mathematical goals. Starting points. Materials required. Time needed

Mathematical goals. Starting points. Materials required. Time needed Level A1 of challege: C A1 Mathematical goals Startig poits Materials required Time eeded Iterpretig algebraic expressios To help learers to: traslate betwee words, symbols, tables, ad area represetatios

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

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

MEP Pupil Text 9. The mean, median and mode are three different ways of describing the average.

MEP Pupil Text 9. The mean, median and mode are three different ways of describing the average. 9 Data Aalysis 9. Mea, Media, Mode ad Rage I Uit 8, you were lookig at ways of collectig ad represetig data. I this uit, you will go oe step further ad fid out how to calculate statistical quatities which

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

NATIONAL SENIOR CERTIFICATE GRADE 11

NATIONAL SENIOR CERTIFICATE GRADE 11 NATIONAL SENIOR CERTIFICATE GRADE MATHEMATICS P EXEMPLAR 007 MARKS: 50 TIME: 3 hours This questio paper cosists of pages, 4 diagram sheets ad a -page formula sheet. Please tur over Mathematics/P DoE/Exemplar

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

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

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

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

AP Calculus AB 2006 Scoring Guidelines Form B

AP Calculus AB 2006 Scoring Guidelines Form B AP Calculus AB 6 Scorig Guidelies Form B The College Board: Coectig Studets to College Success The College Board is a ot-for-profit membership associatio whose missio is to coect studets to college success

More information

AP Calculus BC 2003 Scoring Guidelines Form B

AP Calculus BC 2003 Scoring Guidelines Form B AP Calculus BC Scorig Guidelies Form B The materials icluded i these files are iteded for use by AP teachers for course ad exam preparatio; permissio for ay other use must be sought from the Advaced Placemet

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

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

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

Multiple Representations for Pattern Exploration with the Graphing Calculator and Manipulatives

Multiple Representations for Pattern Exploration with the Graphing Calculator and Manipulatives Douglas A. Lapp Multiple Represetatios for Patter Exploratio with the Graphig Calculator ad Maipulatives To teach mathematics as a coected system of cocepts, we must have a shift i emphasis from a curriculum

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

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

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

Chapter 5 O A Cojecture Of Erdíos Proceedigs NCUR VIII è1994è, Vol II, pp 794í798 Jeærey F Gold Departmet of Mathematics, Departmet of Physics Uiversity of Utah Do H Tucker Departmet of Mathematics Uiversity

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

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

Chapter XIV: Fundamentals of Probability and Statistics *

Chapter XIV: Fundamentals of Probability and Statistics * Objectives Chapter XIV: Fudametals o Probability ad Statistics * Preset udametal cocepts o probability ad statistics Review measures o cetral tedecy ad dispersio Aalyze methods ad applicatios o descriptive

More information

FM4 CREDIT AND BORROWING

FM4 CREDIT AND BORROWING FM4 CREDIT AND BORROWING Whe you purchase big ticket items such as cars, boats, televisios ad the like, retailers ad fiacial istitutios have various terms ad coditios that are implemeted for the cosumer

More information

Biology 171L Environment and Ecology Lab Lab 2: Descriptive Statistics, Presenting Data and Graphing Relationships

Biology 171L Environment and Ecology Lab Lab 2: Descriptive Statistics, Presenting Data and Graphing Relationships Biology 171L Eviromet ad Ecology Lab Lab : Descriptive Statistics, Presetig Data ad Graphig Relatioships Itroductio Log lists of data are ofte ot very useful for idetifyig geeral treds i the data or the

More information

Listing terms of a finite sequence List all of the terms of each finite sequence. a) a n n 2 for 1 n 5 1 b) a n for 1 n 4 n 2

Listing terms of a finite sequence List all of the terms of each finite sequence. a) a n n 2 for 1 n 5 1 b) a n for 1 n 4 n 2 74 (4 ) Chapter 4 Sequeces ad Series 4. SEQUENCES I this sectio Defiitio Fidig a Formula for the th Term The word sequece is a familiar word. We may speak of a sequece of evets or say that somethig is

More information

Elementary Theory of Russian Roulette

Elementary Theory of Russian Roulette Elemetary Theory of Russia Roulette -iterestig patters of fractios- Satoshi Hashiba Daisuke Miematsu Ryohei Miyadera Itroductio. Today we are goig to study mathematical theory of Russia roulette. If some

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

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

CHAPTER 3 DIGITAL CODING OF SIGNALS

CHAPTER 3 DIGITAL CODING OF SIGNALS CHAPTER 3 DIGITAL CODING OF SIGNALS Computers are ofte used to automate the recordig of measuremets. The trasducers ad sigal coditioig circuits produce a voltage sigal that is proportioal to a quatity

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

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

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

MATH 083 Final Exam Review

MATH 083 Final Exam Review MATH 08 Fial Eam Review Completig the problems i this review will greatly prepare you for the fial eam Calculator use is ot required, but you are permitted to use a calculator durig the fial eam period

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

NATIONAL SENIOR CERTIFICATE GRADE 11

NATIONAL SENIOR CERTIFICATE GRADE 11 NATIONAL SENIOR CERTIFICATE GRADE MATHEMATICS P NOVEMBER 007 MARKS: 50 TIME: 3 hours This questio paper cosists of 9 pages, diagram sheet ad a -page formula sheet. Please tur over Mathematics/P DoE/November

More information

Institute of Actuaries of India Subject CT1 Financial Mathematics

Institute of Actuaries of India Subject CT1 Financial Mathematics Istitute of Actuaries of Idia Subject CT1 Fiacial Mathematics For 2014 Examiatios Subject CT1 Fiacial Mathematics Core Techical Aim The aim of the Fiacial Mathematics subject is to provide a groudig i

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

Solving equations. Pre-test. Warm-up

Solving equations. Pre-test. Warm-up Solvig equatios 8 Pre-test Warm-up We ca thik of a algebraic equatio as beig like a set of scales. The two sides of the equatio are equal, so the scales are balaced. If we add somethig to oe side of the

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

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

THE PROBABLE ERROR OF A MEAN. Introduction

THE PROBABLE ERROR OF A MEAN. Introduction THE PROBABLE ERROR OF A MEAN By STUDENT Itroductio Ay experimet may he regarded as formig a idividual of a populatio of experimets which might he performed uder the same coditios. A series of experimets

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

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

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

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

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

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

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

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

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

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

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

.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

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

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

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

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

France caters to innovative companies and offers the best research tax credit in Europe

France caters to innovative companies and offers the best research tax credit in Europe 1/5 The Frech Govermet has three objectives : > improve Frace s fiscal competitiveess > cosolidate R&D activities > make Frace a attractive coutry for iovatio Tax icetives have become a key elemet of public

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

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

Building Blocks Problem Related to Harmonic Series

Building Blocks Problem Related to Harmonic Series TMME, vol3, o, p.76 Buildig Blocks Problem Related to Harmoic Series Yutaka Nishiyama Osaka Uiversity of Ecoomics, Japa Abstract: I this discussio I give a eplaatio of the divergece ad covergece of ifiite

More information

How to read A Mutual Fund shareholder report

How to read A Mutual Fund shareholder report Ivestor BulletI How to read A Mutual Fud shareholder report The SEC s Office of Ivestor Educatio ad Advocacy is issuig this Ivestor Bulleti to educate idividual ivestors about mutual fud shareholder reports.

More information

7.1 Finding Rational Solutions of Polynomial Equations

7.1 Finding Rational Solutions of Polynomial Equations 4 Locker LESSON 7. Fidig Ratioal Solutios of Polyomial Equatios Name Class Date 7. Fidig Ratioal Solutios of Polyomial Equatios Essetial Questio: How do you fid the ratioal roots of a polyomial equatio?

More information

TO: Users of the ACTEX Review Seminar on DVD for SOA Exam FM/CAS Exam 2

TO: Users of the ACTEX Review Seminar on DVD for SOA Exam FM/CAS Exam 2 TO: Users of the ACTEX Review Semiar o DVD for SOA Exam FM/CAS Exam FROM: Richard L. (Dick) Lodo, FSA Dear Studets, Thak you for purchasig the DVD recordig of the ACTEX Review Semiar for SOA Exam FM (CAS

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

SEQUENCES AND SERIES CHAPTER

SEQUENCES AND SERIES CHAPTER CHAPTER SEQUENCES AND SERIES Whe the Grat family purchased a computer for $,200 o a istallmet pla, they agreed to pay $00 each moth util the cost of the computer plus iterest had bee paid The iterest each

More information

3 Basic Definitions of Probability Theory

3 Basic Definitions of Probability Theory 3 Basic Defiitios of Probability Theory 3defprob.tex: Feb 10, 2003 Classical probability Frequecy probability axiomatic probability Historical developemet: Classical Frequecy Axiomatic The Axiomatic defiitio

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

Department of Computer Science, University of Otago

Department of Computer Science, University of Otago Departmet of Computer Sciece, Uiversity of Otago Techical Report OUCS-2006-09 Permutatios Cotaiig May Patters Authors: M.H. Albert Departmet of Computer Sciece, Uiversity of Otago Micah Colema, Rya Fly

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

Question 2: How is a loan amortized?

Question 2: How is a loan amortized? Questio 2: How is a loa amortized? Decreasig auities may be used i auto or home loas. I these types of loas, some amout of moey is borrowed. Fixed paymets are made to pay off the loa as well as ay accrued

More information

Project Deliverables. CS 361, Lecture 28. Outline. Project Deliverables. Administrative. Project Comments

Project Deliverables. CS 361, Lecture 28. Outline. Project Deliverables. Administrative. Project Comments Project Deliverables CS 361, Lecture 28 Jared Saia Uiversity of New Mexico Each Group should tur i oe group project cosistig of: About 6-12 pages of text (ca be loger with appedix) 6-12 figures (please

More information

Predictive Modeling Data. in the ACT Electronic Student Record

Predictive Modeling Data. in the ACT Electronic Student Record Predictive Modelig Data i the ACT Electroic Studet Record overview Predictive Modelig Data Added to the ACT Electroic Studet Record With the release of studet records i September 2012, predictive modelig

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

G r a d e. 2 M a t h e M a t i c s. statistics and Probability

G r a d e. 2 M a t h e M a t i c s. statistics and Probability G r a d e 2 M a t h e M a t i c s statistics ad Probability Grade 2: Statistics (Data Aalysis) (2.SP.1, 2.SP.2) edurig uderstadigs: data ca be collected ad orgaized i a variety of ways. data ca be used

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 William.Hut@mail.wvu.edu 1 Itroductio Imagie you are a matchmaker,

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

A Guide to the Pricing Conventions of SFE Interest Rate Products

A Guide to the Pricing Conventions of SFE Interest Rate Products A Guide to the Pricig Covetios of SFE Iterest Rate Products SFE 30 Day Iterbak Cash Rate Futures Physical 90 Day Bak Bills SFE 90 Day Bak Bill Futures SFE 90 Day Bak Bill Futures Tick Value Calculatios

More information