SlideShare uma empresa Scribd logo
1 de 33
QUANTITATIVE METHODS
PROBABILITY DISTRIBUTIONS ,[object Object],[object Object],[object Object],[object Object],The Rationale for Using Probability Theory
PROBABILITY DISTRIBUTIONS ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],In this session ….
PROBABILITY DISTRIBUTIONS DISCRETE CONTINUOUS ,[object Object],[object Object],[object Object],[object Object],[object Object]
- the B-school Binomial Distribution  ,[object Object],[object Object],[object Object],[object Object],[object Object]
- the B-school Binomial Formula p = probability of success, q = 1- p = probability of failure, r = number of successes, n = total number of trials, µ = np = mean.     =  = standard deviation.
Binomial Formula – Exercise ,[object Object],[object Object],[object Object]
Binomial Formula – Exercise (solution)
- the B-school Binomial Distribution – A Graphical Exploration  Draw probability histograms of the binomial distribution for (a) n = 10 and p = 0.1, 0.3, 0.5, 0.7, 0.9 (b) p = 0.4 and n = 5, 10, 30
- the B-school Binomial Distribution – A Graphical Exploration  The binomial probability histogram for n = 10 and p = 0.1,0.3,0.5,0.7,0.9
- the B-school Binomial Distribution – A Graphical Exploration  The binomial probability histogram for p = 0.4 and n = 5,10,30
- the B-school Normal Distribution  ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
- the B-school Standard Normal Probability Distribution  ,[object Object],[object Object],[object Object],[object Object],[object Object]
Standard Normal Probability Distribution Example 1 ,[object Object],[object Object],[object Object]
Standard Normal Probability Distribution  Example1 (Solution) ,[object Object],[object Object],[object Object],[object Object],[object Object]
- the B-school Standard Normal Probability Distribution - Example  Jarrid Medical, is developing a compact kidney dialysis machine but is having trouble controlling the variability of the rate at which fluid moves through the device. Medical standards require that the hourly flow be 4 litres, plus or minus, 0.1 litre, 80% of the time. Testing the prototype has revealed that 68% of the time the hourly flow is within 0.08 litres of 4.02 litres. Does the prototype satisfy medical standards?
- the B-school Standard Normal Probability Distribution – Example (Solution) Solution:    = 4.02,    = 0.08 Thus the prototype does not satisfy the medical requirements. (required rate of flow)
- the B-school Central Limit Theorem ,[object Object],[object Object],[object Object],[object Object],[object Object]
- the B-school Central Limit Theorem If the random variables x 1 , x 2 ,…..x N  are independent and identically distributed with mean µ and standard deviation   , then is normally distributed with mean    and  standard deviation  .
- the B-school Central Limit Theorem – Exercise  If the weights of individual packets of 2 minute noodles varies according to a normal distribution with a mean of 85.8 grams and a standard deviation of 1.9 grams a. Describe the distribution that will describe the mean weight of a simple random sample of 5 such packets of noodles. Such a sample of 5 packets of noodles in a multi- pack is labeled “average weight of contents: 85 grams” b. Determine the proportion of such multi-packs with an average weight within 1 gram of the claimed average weight. c. Determine the proportion of such multi-packs with an average weight less than what is claimed.
- the B-school Central Limit Theorem – Exercise (solution) a. The mean weight  is normally distributed with a mean of 85.8 grams and a standard deviation of  b.  c. (use NORMSDIST)
ESTIMATION POINT ESTIMATE INTERVAL ESTIMATE A single number is used to estimate an unknown population parameter. e.g our current data of MBA enrolments indicates that 20% of the students who will enrol next session will be women. Range of values used to estimate a population parameter. e.g our current data of MBA enrolments indicates that 15% to 24 % of the students who will enrol next session  will be women.
- the B-school Point Estimates ,[object Object],[object Object],[object Object]
- the B-school Point Estimates - Example The National Bank of Lincon is trying to determine the number of tellers available during the lunch rush on Fridays. The bank has collected data on the number of people who entered the bank during the last 3 months on Friday from 11 A.M to 1 P.M. Using the data below, find the point estimates of the mean and standard deviation of the population from which the sample was drawn. 242  275  289  306  342  385  279  245  269  305  294  328
- the B-school Point Estimates Solution:
- the B-school Interval Estimates and Confidence Intervals ,[object Object],[object Object],[object Object],[object Object],[object Object]
- the B-school t  Distribution (Student’s t distribution)  ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
- the B-school Interval Estimates for Population Mean Seven homemakers were randomly sampled and it was determined that the distances they walked in their housework had an average of 39.2 miles per week and a sample standard deviation of 3.2 miles per week. Construct a 95% confidence interval for the population mean.
- the B-school Interval Estimates for Population Mean Solution:  Sample size = n = 7 (we use t distribution since n  ≤  30 ) Degrees of freedom = 6. Sample mean =  = 39.2 miles. Sample sd =   = 3.2 miles (estimate of population sd) Standard error =   x  =  = 3.2/2.645 = 1.209 t value (from t table under column 0.05, 6 df,use TINV) = 2.447   = 39.2 ± 2.447 * 1.209 = 39.2 ± 2.9596   = (36.240,42.160) miles
- the B-school Interval Estimates for Population Proportion A quality control inspector collected a random sample of 500 tubes of toothpaste from the production line and found that 41 of them had leaks from the tail end. Construct a 90% confidence interval for the percentage of all toothpaste tubes that had leakage.
- the B-school Interval Estimates for Population Proportion Solution:  Sample size = 500 Point estimate of population proportion =  = 41/500 = 0.082 Confidence level of p = 0.90 = 1 -  α . α  = 0.10 Standard error = critical z value = Z  /2  (use NORMSINV(0.05)) = 1.645   = 0.082 ± 1.645* 0.0122 = 0.082 ±0.0201   = (0.062,0.1022) tubes Approx 6.2% – 10.2% of the tubes will have leakages.
- the B-school Determining Sample Size The university is considering raising tution to improve school facilities and they want to determine what percentage of students favour the increase. The university needs to be 90% confident that the percentage has been estimated to within 2% of the true value. How large a sample is needed to guarantee this accuracy regardless of the true percentage?
- the B-school Determining Sample Size Solution:  z value for 90% confidence level is = 1.645 (NORMSINV(0.05)) Standard error = pq/n = 0.00014884 n = pq/0.00014884  The largest value of n will be obtained when pq is largest i.e when p = q = 0.5 n = 0.5*0.5/ 0.00014884 = 1680

Mais conteúdo relacionado

Mais procurados

6.5 central limit
6.5 central limit6.5 central limit
6.5 central limit
leblance
 
Statistics-Measures of dispersions
Statistics-Measures of dispersionsStatistics-Measures of dispersions
Statistics-Measures of dispersions
Capricorn
 

Mais procurados (20)

Measures of association
Measures of associationMeasures of association
Measures of association
 
Analysis of variance (ANOVA)
Analysis of variance (ANOVA)Analysis of variance (ANOVA)
Analysis of variance (ANOVA)
 
Descriptive statistics
Descriptive statisticsDescriptive statistics
Descriptive statistics
 
Sampling Distribution
Sampling DistributionSampling Distribution
Sampling Distribution
 
Binomial probability distributions
Binomial probability distributions  Binomial probability distributions
Binomial probability distributions
 
6.5 central limit
6.5 central limit6.5 central limit
6.5 central limit
 
Measures of dispersion
Measures of dispersionMeasures of dispersion
Measures of dispersion
 
Sampling & Sampling Distribtutions
Sampling & Sampling DistribtutionsSampling & Sampling Distribtutions
Sampling & Sampling Distribtutions
 
Systematic ranom sampling for slide share
Systematic ranom sampling for slide shareSystematic ranom sampling for slide share
Systematic ranom sampling for slide share
 
Ppt for 1.1 introduction to statistical inference
Ppt for 1.1 introduction to statistical inferencePpt for 1.1 introduction to statistical inference
Ppt for 1.1 introduction to statistical inference
 
Statistics-Measures of dispersions
Statistics-Measures of dispersionsStatistics-Measures of dispersions
Statistics-Measures of dispersions
 
Sampling Distribution
Sampling DistributionSampling Distribution
Sampling Distribution
 
Ch6 Testing the Difference between Means, Variances
Ch6 Testing the Difference between Means, VariancesCh6 Testing the Difference between Means, Variances
Ch6 Testing the Difference between Means, Variances
 
Sampling and sampling distributions
Sampling and sampling distributionsSampling and sampling distributions
Sampling and sampling distributions
 
Gamma, Expoential, Poisson And Chi Squared Distributions
Gamma, Expoential, Poisson And Chi Squared DistributionsGamma, Expoential, Poisson And Chi Squared Distributions
Gamma, Expoential, Poisson And Chi Squared Distributions
 
Mann Whitney U Test | Statistics
Mann Whitney U Test | StatisticsMann Whitney U Test | Statistics
Mann Whitney U Test | Statistics
 
Sampling distribution
Sampling distributionSampling distribution
Sampling distribution
 
Measures of Variation
Measures of VariationMeasures of Variation
Measures of Variation
 
Categorical data analysis
Categorical data analysisCategorical data analysis
Categorical data analysis
 
Statistical inference 2
Statistical inference 2Statistical inference 2
Statistical inference 2
 

Destaque (20)

Research Design
Research DesignResearch Design
Research Design
 
Les5e ppt 01
Les5e ppt 01Les5e ppt 01
Les5e ppt 01
 
1.1 An Overview of Statistics
1.1 An Overview of Statistics1.1 An Overview of Statistics
1.1 An Overview of Statistics
 
Chi square
Chi squareChi square
Chi square
 
Sopheap's Using Spss presentation
Sopheap's Using Spss presentationSopheap's Using Spss presentation
Sopheap's Using Spss presentation
 
Emotional and Behavioral Disorders (EBD) and Grade I Pupils' Achievements
Emotional and Behavioral Disorders (EBD) and Grade I Pupils' AchievementsEmotional and Behavioral Disorders (EBD) and Grade I Pupils' Achievements
Emotional and Behavioral Disorders (EBD) and Grade I Pupils' Achievements
 
Applied Statistical Techniques
Applied Statistical TechniquesApplied Statistical Techniques
Applied Statistical Techniques
 
Thiyagu statistics
Thiyagu   statisticsThiyagu   statistics
Thiyagu statistics
 
Handling data scattergraph
Handling data scattergraphHandling data scattergraph
Handling data scattergraph
 
Statistic Chart
Statistic ChartStatistic Chart
Statistic Chart
 
Descriptive Statistic in Assessment 1
Descriptive Statistic in Assessment 1Descriptive Statistic in Assessment 1
Descriptive Statistic in Assessment 1
 
Questionnaires
QuestionnairesQuestionnaires
Questionnaires
 
Bsc agri 2 pae u-2.3 capitalformation
Bsc agri  2 pae  u-2.3 capitalformationBsc agri  2 pae  u-2.3 capitalformation
Bsc agri 2 pae u-2.3 capitalformation
 
Nets
NetsNets
Nets
 
Correlation testing
Correlation testingCorrelation testing
Correlation testing
 
Statistical techniques
Statistical techniquesStatistical techniques
Statistical techniques
 
Chapter018
Chapter018Chapter018
Chapter018
 
Extreme semaphore 12 pax id290
Extreme semaphore 12 pax id290Extreme semaphore 12 pax id290
Extreme semaphore 12 pax id290
 
Xtream presentations
Xtream presentationsXtream presentations
Xtream presentations
 
Handling data 1
Handling data 1Handling data 1
Handling data 1
 

Semelhante a Probability Distributions

Statistik Chapter 6
Statistik Chapter 6Statistik Chapter 6
Statistik Chapter 6
WanBK Leo
 
PSUnit_III_Lesson_2_Finding_the_Mean _and_Variance_of_the_Sampling_Distributi...
PSUnit_III_Lesson_2_Finding_the_Mean _and_Variance_of_the_Sampling_Distributi...PSUnit_III_Lesson_2_Finding_the_Mean _and_Variance_of_the_Sampling_Distributi...
PSUnit_III_Lesson_2_Finding_the_Mean _and_Variance_of_the_Sampling_Distributi...
harlene9
 
Module Five Normal Distributions & Hypothesis TestingTop of F.docx
Module Five Normal Distributions & Hypothesis TestingTop of F.docxModule Five Normal Distributions & Hypothesis TestingTop of F.docx
Module Five Normal Distributions & Hypothesis TestingTop of F.docx
roushhsiu
 
Chapter 7 – Confidence Intervals And Sample Size
Chapter 7 – Confidence Intervals And Sample SizeChapter 7 – Confidence Intervals And Sample Size
Chapter 7 – Confidence Intervals And Sample Size
Rose Jenkins
 

Semelhante a Probability Distributions (20)

Statistik Chapter 6
Statistik Chapter 6Statistik Chapter 6
Statistik Chapter 6
 
Hypothesis testing: A single sample test
Hypothesis testing: A single sample testHypothesis testing: A single sample test
Hypothesis testing: A single sample test
 
PSUnit_III_Lesson_2_Finding_the_Mean _and_Variance_of_the_Sampling_Distributi...
PSUnit_III_Lesson_2_Finding_the_Mean _and_Variance_of_the_Sampling_Distributi...PSUnit_III_Lesson_2_Finding_the_Mean _and_Variance_of_the_Sampling_Distributi...
PSUnit_III_Lesson_2_Finding_the_Mean _and_Variance_of_the_Sampling_Distributi...
 
Tbs910 sampling hypothesis regression
Tbs910 sampling hypothesis regressionTbs910 sampling hypothesis regression
Tbs910 sampling hypothesis regression
 
Chap 6
Chap 6Chap 6
Chap 6
 
statistical inference.pptx
statistical inference.pptxstatistical inference.pptx
statistical inference.pptx
 
2_5332511410507220042.ppt
2_5332511410507220042.ppt2_5332511410507220042.ppt
2_5332511410507220042.ppt
 
Chp11 - Research Methods for Business By Authors Uma Sekaran and Roger Bougie
Chp11  - Research Methods for Business By Authors Uma Sekaran and Roger BougieChp11  - Research Methods for Business By Authors Uma Sekaran and Roger Bougie
Chp11 - Research Methods for Business By Authors Uma Sekaran and Roger Bougie
 
Statistical inference: Estimation
Statistical inference: EstimationStatistical inference: Estimation
Statistical inference: Estimation
 
day9.ppt
day9.pptday9.ppt
day9.ppt
 
Module Five Normal Distributions & Hypothesis TestingTop of F.docx
Module Five Normal Distributions & Hypothesis TestingTop of F.docxModule Five Normal Distributions & Hypothesis TestingTop of F.docx
Module Five Normal Distributions & Hypothesis TestingTop of F.docx
 
3. Statistical inference_anesthesia.pptx
3.  Statistical inference_anesthesia.pptx3.  Statistical inference_anesthesia.pptx
3. Statistical inference_anesthesia.pptx
 
estimation
estimationestimation
estimation
 
Estimation
EstimationEstimation
Estimation
 
Estimating a Population Proportion
Estimating a Population Proportion  Estimating a Population Proportion
Estimating a Population Proportion
 
STATISTIC ESTIMATION
STATISTIC ESTIMATIONSTATISTIC ESTIMATION
STATISTIC ESTIMATION
 
Chapter08
Chapter08Chapter08
Chapter08
 
Chapter09
Chapter09Chapter09
Chapter09
 
Basic statistics
Basic statisticsBasic statistics
Basic statistics
 
Chapter 7 – Confidence Intervals And Sample Size
Chapter 7 – Confidence Intervals And Sample SizeChapter 7 – Confidence Intervals And Sample Size
Chapter 7 – Confidence Intervals And Sample Size
 

Mais de Harish Lunani

O General Air Conditoners Catalogue 2014
O General Air Conditoners Catalogue 2014O General Air Conditoners Catalogue 2014
O General Air Conditoners Catalogue 2014
Harish Lunani
 
TRANE - Consumer Electronics Fare - Delhi
TRANE - Consumer Electronics Fare - DelhiTRANE - Consumer Electronics Fare - Delhi
TRANE - Consumer Electronics Fare - Delhi
Harish Lunani
 
Society activation – palm meadows
Society activation – palm meadowsSociety activation – palm meadows
Society activation – palm meadows
Harish Lunani
 
Marketing Management Session 6
Marketing Management Session 6Marketing Management Session 6
Marketing Management Session 6
Harish Lunani
 
Marketing Management Session 1 & 2
Marketing Management Session 1 & 2 Marketing Management Session 1 & 2
Marketing Management Session 1 & 2
Harish Lunani
 
Marketing Management Session on CRM
Marketing Management Session on CRMMarketing Management Session on CRM
Marketing Management Session on CRM
Harish Lunani
 
Marketing Management Session 14
Marketing Management Session 14Marketing Management Session 14
Marketing Management Session 14
Harish Lunani
 

Mais de Harish Lunani (20)

Govt Presentation for Kakinada as Smart City
Govt Presentation for Kakinada as Smart CityGovt Presentation for Kakinada as Smart City
Govt Presentation for Kakinada as Smart City
 
Kakinada Smart City MP Presentation
Kakinada Smart City MP PresentationKakinada Smart City MP Presentation
Kakinada Smart City MP Presentation
 
O General Air Conditoners Catalogue 2014
O General Air Conditoners Catalogue 2014O General Air Conditoners Catalogue 2014
O General Air Conditoners Catalogue 2014
 
YD Rama Rao Affidavit for MLA KKD 2014 Elections
YD Rama Rao Affidavit for MLA KKD 2014 ElectionsYD Rama Rao Affidavit for MLA KKD 2014 Elections
YD Rama Rao Affidavit for MLA KKD 2014 Elections
 
Mootha Sasidhar Affidavit for MLA KKD 2014 Elections
Mootha Sasidhar Affidavit for MLA KKD 2014 ElectionsMootha Sasidhar Affidavit for MLA KKD 2014 Elections
Mootha Sasidhar Affidavit for MLA KKD 2014 Elections
 
Pantham Venkateswararao ( Nanaji) Affidavit for MLA KKD 2014 Elections.
Pantham Venkateswararao ( Nanaji) Affidavit for MLA KKD 2014 Elections.Pantham Venkateswararao ( Nanaji) Affidavit for MLA KKD 2014 Elections.
Pantham Venkateswararao ( Nanaji) Affidavit for MLA KKD 2014 Elections.
 
Vanamadi Venkateswar Rao ( Kondababu) Affidavit for MLA KKD 2014 Elections.
Vanamadi Venkateswar Rao ( Kondababu) Affidavit for MLA KKD 2014 Elections.Vanamadi Venkateswar Rao ( Kondababu) Affidavit for MLA KKD 2014 Elections.
Vanamadi Venkateswar Rao ( Kondababu) Affidavit for MLA KKD 2014 Elections.
 
Dwarampudi ChandraSekhar Reddy Affidavit for 2014 MLA KKD Elections
Dwarampudi ChandraSekhar Reddy Affidavit for 2014 MLA KKD ElectionsDwarampudi ChandraSekhar Reddy Affidavit for 2014 MLA KKD Elections
Dwarampudi ChandraSekhar Reddy Affidavit for 2014 MLA KKD Elections
 
Trane Commercial Andhra Pradesh Customers List
Trane Commercial Andhra Pradesh Customers ListTrane Commercial Andhra Pradesh Customers List
Trane Commercial Andhra Pradesh Customers List
 
TRANE - Consumer Electronics Fare - Delhi
TRANE - Consumer Electronics Fare - DelhiTRANE - Consumer Electronics Fare - Delhi
TRANE - Consumer Electronics Fare - Delhi
 
Society activation – palm meadows
Society activation – palm meadowsSociety activation – palm meadows
Society activation – palm meadows
 
TRANE Interactive Air Conditioners
TRANE Interactive Air ConditionersTRANE Interactive Air Conditioners
TRANE Interactive Air Conditioners
 
Sarita's Spices
Sarita's SpicesSarita's Spices
Sarita's Spices
 
Marketing Management Session 6
Marketing Management Session 6Marketing Management Session 6
Marketing Management Session 6
 
Marketing Management Session 1 & 2
Marketing Management Session 1 & 2 Marketing Management Session 1 & 2
Marketing Management Session 1 & 2
 
Marketing McDonalds
Marketing McDonaldsMarketing McDonalds
Marketing McDonalds
 
CRM Loyalty
CRM LoyaltyCRM Loyalty
CRM Loyalty
 
CRM _ Marketing
CRM _ MarketingCRM _ Marketing
CRM _ Marketing
 
Marketing Management Session on CRM
Marketing Management Session on CRMMarketing Management Session on CRM
Marketing Management Session on CRM
 
Marketing Management Session 14
Marketing Management Session 14Marketing Management Session 14
Marketing Management Session 14
 

Probability Distributions

  • 2.
  • 3.
  • 4.
  • 5.
  • 6. - the B-school Binomial Formula p = probability of success, q = 1- p = probability of failure, r = number of successes, n = total number of trials, µ = np = mean.  = = standard deviation.
  • 7.
  • 8. Binomial Formula – Exercise (solution)
  • 9. - the B-school Binomial Distribution – A Graphical Exploration Draw probability histograms of the binomial distribution for (a) n = 10 and p = 0.1, 0.3, 0.5, 0.7, 0.9 (b) p = 0.4 and n = 5, 10, 30
  • 10. - the B-school Binomial Distribution – A Graphical Exploration The binomial probability histogram for n = 10 and p = 0.1,0.3,0.5,0.7,0.9
  • 11. - the B-school Binomial Distribution – A Graphical Exploration The binomial probability histogram for p = 0.4 and n = 5,10,30
  • 12.
  • 13.
  • 14.
  • 15.
  • 16. - the B-school Standard Normal Probability Distribution - Example Jarrid Medical, is developing a compact kidney dialysis machine but is having trouble controlling the variability of the rate at which fluid moves through the device. Medical standards require that the hourly flow be 4 litres, plus or minus, 0.1 litre, 80% of the time. Testing the prototype has revealed that 68% of the time the hourly flow is within 0.08 litres of 4.02 litres. Does the prototype satisfy medical standards?
  • 17. - the B-school Standard Normal Probability Distribution – Example (Solution) Solution:  = 4.02,  = 0.08 Thus the prototype does not satisfy the medical requirements. (required rate of flow)
  • 18.
  • 19. - the B-school Central Limit Theorem If the random variables x 1 , x 2 ,…..x N are independent and identically distributed with mean µ and standard deviation  , then is normally distributed with mean  and standard deviation .
  • 20. - the B-school Central Limit Theorem – Exercise If the weights of individual packets of 2 minute noodles varies according to a normal distribution with a mean of 85.8 grams and a standard deviation of 1.9 grams a. Describe the distribution that will describe the mean weight of a simple random sample of 5 such packets of noodles. Such a sample of 5 packets of noodles in a multi- pack is labeled “average weight of contents: 85 grams” b. Determine the proportion of such multi-packs with an average weight within 1 gram of the claimed average weight. c. Determine the proportion of such multi-packs with an average weight less than what is claimed.
  • 21. - the B-school Central Limit Theorem – Exercise (solution) a. The mean weight is normally distributed with a mean of 85.8 grams and a standard deviation of b. c. (use NORMSDIST)
  • 22. ESTIMATION POINT ESTIMATE INTERVAL ESTIMATE A single number is used to estimate an unknown population parameter. e.g our current data of MBA enrolments indicates that 20% of the students who will enrol next session will be women. Range of values used to estimate a population parameter. e.g our current data of MBA enrolments indicates that 15% to 24 % of the students who will enrol next session will be women.
  • 23.
  • 24. - the B-school Point Estimates - Example The National Bank of Lincon is trying to determine the number of tellers available during the lunch rush on Fridays. The bank has collected data on the number of people who entered the bank during the last 3 months on Friday from 11 A.M to 1 P.M. Using the data below, find the point estimates of the mean and standard deviation of the population from which the sample was drawn. 242 275 289 306 342 385 279 245 269 305 294 328
  • 25. - the B-school Point Estimates Solution:
  • 26.
  • 27.
  • 28. - the B-school Interval Estimates for Population Mean Seven homemakers were randomly sampled and it was determined that the distances they walked in their housework had an average of 39.2 miles per week and a sample standard deviation of 3.2 miles per week. Construct a 95% confidence interval for the population mean.
  • 29. - the B-school Interval Estimates for Population Mean Solution: Sample size = n = 7 (we use t distribution since n ≤ 30 ) Degrees of freedom = 6. Sample mean = = 39.2 miles. Sample sd =  = 3.2 miles (estimate of population sd) Standard error =  x = = 3.2/2.645 = 1.209 t value (from t table under column 0.05, 6 df,use TINV) = 2.447 = 39.2 ± 2.447 * 1.209 = 39.2 ± 2.9596 = (36.240,42.160) miles
  • 30. - the B-school Interval Estimates for Population Proportion A quality control inspector collected a random sample of 500 tubes of toothpaste from the production line and found that 41 of them had leaks from the tail end. Construct a 90% confidence interval for the percentage of all toothpaste tubes that had leakage.
  • 31. - the B-school Interval Estimates for Population Proportion Solution: Sample size = 500 Point estimate of population proportion = = 41/500 = 0.082 Confidence level of p = 0.90 = 1 - α . α = 0.10 Standard error = critical z value = Z  /2 (use NORMSINV(0.05)) = 1.645 = 0.082 ± 1.645* 0.0122 = 0.082 ±0.0201 = (0.062,0.1022) tubes Approx 6.2% – 10.2% of the tubes will have leakages.
  • 32. - the B-school Determining Sample Size The university is considering raising tution to improve school facilities and they want to determine what percentage of students favour the increase. The university needs to be 90% confident that the percentage has been estimated to within 2% of the true value. How large a sample is needed to guarantee this accuracy regardless of the true percentage?
  • 33. - the B-school Determining Sample Size Solution: z value for 90% confidence level is = 1.645 (NORMSINV(0.05)) Standard error = pq/n = 0.00014884 n = pq/0.00014884 The largest value of n will be obtained when pq is largest i.e when p = q = 0.5 n = 0.5*0.5/ 0.00014884 = 1680