Enviar búsqueda
Cargar
Ch08(1)
•
Descargar como PPTX, PDF
•
0 recomendaciones
•
316 vistas
Marouane Zouzhi
Seguir
Denunciar
Compartir
Denunciar
Compartir
1 de 68
Descargar ahora
Recomendados
Kadoma Rimuka group of clinics - Profiles and final request
Kadoma Rimuka group of clinics - Profiles and final request
Zimbabwe Network For Health - Europe
Anxiolytics screening methods
Anxiolytics screening methods
Dekollu Suku
Preclinical Screening for Alzheimer's
Preclinical Screening for Alzheimer's
Drx Burade
Multivariate Data Analysis Project Report
Multivariate Data Analysis Project Report
Utkarsh Agrawal
Preclinical Screening for Neurodegenerative Disease (Multiple Sclerosis)
Preclinical Screening for Neurodegenerative Disease (Multiple Sclerosis)
Drx Burade
Business Statistics Chapter 8
Business Statistics Chapter 8
Lux PP
D2C Insider Omni Channel - Omnichannel CDP & Marketing Automation for E-Comme...
D2C Insider Omni Channel - Omnichannel CDP & Marketing Automation for E-Comme...
D2C Insider
Screening methods for antianginal & antimalarial drugs
Screening methods for antianginal & antimalarial drugs
Charu Pundir
Recomendados
Kadoma Rimuka group of clinics - Profiles and final request
Kadoma Rimuka group of clinics - Profiles and final request
Zimbabwe Network For Health - Europe
Anxiolytics screening methods
Anxiolytics screening methods
Dekollu Suku
Preclinical Screening for Alzheimer's
Preclinical Screening for Alzheimer's
Drx Burade
Multivariate Data Analysis Project Report
Multivariate Data Analysis Project Report
Utkarsh Agrawal
Preclinical Screening for Neurodegenerative Disease (Multiple Sclerosis)
Preclinical Screening for Neurodegenerative Disease (Multiple Sclerosis)
Drx Burade
Business Statistics Chapter 8
Business Statistics Chapter 8
Lux PP
D2C Insider Omni Channel - Omnichannel CDP & Marketing Automation for E-Comme...
D2C Insider Omni Channel - Omnichannel CDP & Marketing Automation for E-Comme...
D2C Insider
Screening methods for antianginal & antimalarial drugs
Screening methods for antianginal & antimalarial drugs
Charu Pundir
Analytics & retail analytics
Analytics & retail analytics
Dale Sternberg
Preclinical screening of antifertility agents. kahkesha
Preclinical screening of antifertility agents. kahkesha
kahkesha samshad
Retail Analytics
Retail Analytics
Aaum Research and Analytics Private Limited
Big Data & Analytics to Improve Supply Chain and Business Performance
Big Data & Analytics to Improve Supply Chain and Business Performance
Bristlecone SCC
Innovations in Market Mix Modelling
Innovations in Market Mix Modelling
Absolutdata Analytics
Behavior and muscle coordination
Behavior and muscle coordination
Bhupen Singh
Glucose uptake and calcium influx assays.pptx
Glucose uptake and calcium influx assays.pptx
ashharnomani
High throughput screening (hts) copy
High throughput screening (hts) copy
Iqrar Ansari
CRM Index
CRM Index
Alan See
Industrial Chemicals Inc.
Industrial Chemicals Inc.
Argha Ray
Evaluation of Anti-Epileptic Drugs
Evaluation of Anti-Epileptic Drugs
Dr. Shivesh Gupta
Immunoassay of insulin (neha)
Immunoassay of insulin (neha)
ANANYAPANDEY71
Infarmaciticolina
Infarmaciticolina
juan luis delgadoestévez
Call sheet
Call sheet
reuben95
Fase ii planificación grupo-i
Fase ii planificación grupo-i
delio81
adjectives
adjectives
Geraldine Balladares
Histograms
Histograms
Nophytha II
Ph Yrev1
Ph Yrev1
kitcoffeen
Chap008.ppt
Chap008.ppt
najwalyaa
Chapter on Confidence interval notes.ppt
Chapter on Confidence interval notes.ppt
AbdulMuhith4
Business Analytics _ Confidence Interval
Business Analytics _ Confidence Interval
Ravindra Nath Shukla
Bbs11 ppt ch08
Bbs11 ppt ch08
Tuul Tuul
Más contenido relacionado
La actualidad más candente
Analytics & retail analytics
Analytics & retail analytics
Dale Sternberg
Preclinical screening of antifertility agents. kahkesha
Preclinical screening of antifertility agents. kahkesha
kahkesha samshad
Retail Analytics
Retail Analytics
Aaum Research and Analytics Private Limited
Big Data & Analytics to Improve Supply Chain and Business Performance
Big Data & Analytics to Improve Supply Chain and Business Performance
Bristlecone SCC
Innovations in Market Mix Modelling
Innovations in Market Mix Modelling
Absolutdata Analytics
Behavior and muscle coordination
Behavior and muscle coordination
Bhupen Singh
Glucose uptake and calcium influx assays.pptx
Glucose uptake and calcium influx assays.pptx
ashharnomani
High throughput screening (hts) copy
High throughput screening (hts) copy
Iqrar Ansari
CRM Index
CRM Index
Alan See
Industrial Chemicals Inc.
Industrial Chemicals Inc.
Argha Ray
Evaluation of Anti-Epileptic Drugs
Evaluation of Anti-Epileptic Drugs
Dr. Shivesh Gupta
Immunoassay of insulin (neha)
Immunoassay of insulin (neha)
ANANYAPANDEY71
La actualidad más candente
(12)
Analytics & retail analytics
Analytics & retail analytics
Preclinical screening of antifertility agents. kahkesha
Preclinical screening of antifertility agents. kahkesha
Retail Analytics
Retail Analytics
Big Data & Analytics to Improve Supply Chain and Business Performance
Big Data & Analytics to Improve Supply Chain and Business Performance
Innovations in Market Mix Modelling
Innovations in Market Mix Modelling
Behavior and muscle coordination
Behavior and muscle coordination
Glucose uptake and calcium influx assays.pptx
Glucose uptake and calcium influx assays.pptx
High throughput screening (hts) copy
High throughput screening (hts) copy
CRM Index
CRM Index
Industrial Chemicals Inc.
Industrial Chemicals Inc.
Evaluation of Anti-Epileptic Drugs
Evaluation of Anti-Epileptic Drugs
Immunoassay of insulin (neha)
Immunoassay of insulin (neha)
Destacado
Infarmaciticolina
Infarmaciticolina
juan luis delgadoestévez
Call sheet
Call sheet
reuben95
Fase ii planificación grupo-i
Fase ii planificación grupo-i
delio81
adjectives
adjectives
Geraldine Balladares
Histograms
Histograms
Nophytha II
Ph Yrev1
Ph Yrev1
kitcoffeen
Destacado
(6)
Infarmaciticolina
Infarmaciticolina
Call sheet
Call sheet
Fase ii planificación grupo-i
Fase ii planificación grupo-i
adjectives
adjectives
Histograms
Histograms
Ph Yrev1
Ph Yrev1
Similar a Ch08(1)
Chap008.ppt
Chap008.ppt
najwalyaa
Chapter on Confidence interval notes.ppt
Chapter on Confidence interval notes.ppt
AbdulMuhith4
Business Analytics _ Confidence Interval
Business Analytics _ Confidence Interval
Ravindra Nath Shukla
Bbs11 ppt ch08
Bbs11 ppt ch08
Tuul Tuul
Estimating population values ppt @ bec doms
Estimating population values ppt @ bec doms
Babasab Patil
Msb12e ppt ch06
Msb12e ppt ch06
Subas Nandy
Chap08
Chap08
Marouane Zouzhi
Chapter 7 Section 3.ppt
Chapter 7 Section 3.ppt
ManoloTaquire
Newbold_chap08.ppt
Newbold_chap08.ppt
cfisicaster
Ch08 ci estimation
Ch08 ci estimation
Mohamed Elias
Les5e ppt 06
Les5e ppt 06
Subas Nandy
Chapter09
Chapter09
rwmiller
BUS173 Lecture 5.pdf
BUS173 Lecture 5.pdf
SusantoSaha1
Estimation and confidence interval
Estimation and confidence interval
Homework Guru
Statistik 1 7 estimasi & ci
Statistik 1 7 estimasi & ci
Selvin Hadi
Lesson04_new
Lesson04_new
shengvn
Lesson04_Static11
Lesson04_Static11
thangv
Javier Garcia - Verdugo Sanchez - Six Sigma Training - W3 Confidence Intervals
Javier Garcia - Verdugo Sanchez - Six Sigma Training - W3 Confidence Intervals
J. García - Verdugo
Mca admission in india
Mca admission in india
Edhole.com
Lecture6 xing
Lecture6 xing
Tianlu Wang
Similar a Ch08(1)
(20)
Chap008.ppt
Chap008.ppt
Chapter on Confidence interval notes.ppt
Chapter on Confidence interval notes.ppt
Business Analytics _ Confidence Interval
Business Analytics _ Confidence Interval
Bbs11 ppt ch08
Bbs11 ppt ch08
Estimating population values ppt @ bec doms
Estimating population values ppt @ bec doms
Msb12e ppt ch06
Msb12e ppt ch06
Chap08
Chap08
Chapter 7 Section 3.ppt
Chapter 7 Section 3.ppt
Newbold_chap08.ppt
Newbold_chap08.ppt
Ch08 ci estimation
Ch08 ci estimation
Les5e ppt 06
Les5e ppt 06
Chapter09
Chapter09
BUS173 Lecture 5.pdf
BUS173 Lecture 5.pdf
Estimation and confidence interval
Estimation and confidence interval
Statistik 1 7 estimasi & ci
Statistik 1 7 estimasi & ci
Lesson04_new
Lesson04_new
Lesson04_Static11
Lesson04_Static11
Javier Garcia - Verdugo Sanchez - Six Sigma Training - W3 Confidence Intervals
Javier Garcia - Verdugo Sanchez - Six Sigma Training - W3 Confidence Intervals
Mca admission in india
Mca admission in india
Lecture6 xing
Lecture6 xing
Más de Marouane Zouzhi
Ch07
Ch07
Marouane Zouzhi
Chap07
Chap07
Marouane Zouzhi
Ch09(1)
Ch09(1)
Marouane Zouzhi
Ch08(2)
Ch08(2)
Marouane Zouzhi
Chap09
Chap09
Marouane Zouzhi
Chap07
Chap07
Marouane Zouzhi
Más de Marouane Zouzhi
(6)
Ch07
Ch07
Chap07
Chap07
Ch09(1)
Ch09(1)
Ch08(2)
Ch08(2)
Chap09
Chap09
Chap07
Chap07
Ch08(1)
1.
Chap 8-1Copyright ©2012
Pearson Education Chap 8-1 Chapter 8 Confidence Interval Estimation Basic Business Statistics 12th Edition Global Edition
2.
Chap 8-2Copyright ©2012
Pearson Education Chap 8-2 Learning Objectives In this chapter, you learn: To construct and interpret confidence interval estimates for the mean and the proportion How to determine the sample size necessary to develop a confidence interval estimate for the mean or proportion How to use confidence interval estimates in auditing
3.
Chap 8-3Copyright ©2012
Pearson Education Chap 8-3 Chapter Outline Confidence Intervals for the Population Mean, μ when Population Standard Deviation σ is Known when Population Standard Deviation σ is Unknown Confidence Intervals for the Population Proportion, π Determining the Required Sample Size
4.
Chap 8-4Copyright ©2012
Pearson Education Chap 8-4 Point and Interval Estimates A point estimate is a single number, a confidence interval provides additional information about the variability of the estimate Point Estimate Lower Confidence Limit Upper Confidence Limit Width of confidence interval DCOVA
5.
Chap 8-5Copyright ©2012
Pearson Education Chap 8-5 We can estimate a Population Parameter … Point Estimates with a Sample Statistic (a Point Estimate) Mean Proportion pπ Xμ DCOVA
6.
Chap 8-6Copyright ©2012
Pearson Education Chap 8-6 Confidence Intervals How much uncertainty is associated with a point estimate of a population parameter? An interval estimate provides more information about a population characteristic than does a point estimate. Such interval estimates are called confidence intervals. DCOVA
7.
Chap 8-7Copyright ©2012
Pearson Education Chap 8-7 Confidence Interval Estimate An interval gives a range of values: Takes into consideration variation in sample statistics from sample to sample Based on observations from 1 sample Gives information about closeness to unknown population parameters Stated in terms of level of confidence e.g. 95% confident, 99% confident Can never be 100% confident DCOVA
8.
Chap 8-8Copyright ©2012
Pearson Education Chap 8-8 Confidence Interval Example Cereal fill example Population has µ = 368 and σ = 15. If you take a sample of size n = 25 you know 368 1.96 * 15 / = (362.12, 373.88) contains 95% of the sample means When you don’t know µ, you use X to estimate µ If X = 362.3 the interval is 362.3 1.96 * 15 / = (356.42, 368.18) Since 356.42 ≤ µ ≤ 368.18 the interval based on this sample makes a correct statement about µ. But what about the intervals from other possible samples of size 25? 25 25 DCOVA
9.
Chap 8-9Copyright ©2012
Pearson Education Chap 8-9 Confidence Interval Example (continued) Sample # X Lower Limit Upper Limit Contain µ? 1 362.30 356.42 368.18 Yes 2 369.50 363.62 375.38 Yes 3 360.00 354.12 365.88 No 4 362.12 356.24 368.00 Yes 5 373.88 368.00 379.76 Yes DCOVA
10.
Chap 8-10Copyright ©2012
Pearson Education Chap 8-10 Confidence Interval Example In practice you only take one sample of size n In practice you do not know µ so you do not know if the interval actually contains µ However, you do know that 95% of the intervals formed in this manner will contain µ Thus, based on the one sample you actually selected, you can be 95% confident your interval will contain µ (this is a 95% confidence interval) (continued) Note: 95% confidence is based on the fact that we used Z = 1.96. DCOVA
11.
Chap 8-11Copyright ©2012
Pearson Education Chap 8-11 Estimation Process (mean, μ, is unknown) Population Random Sample Mean X = 50 Sample I am 95% confident that μ is between 40 & 60. DCOVA
12.
Chap 8-12Copyright ©2012
Pearson Education Chap 8-12 General Formula The general formula for all confidence intervals is: Point Estimate (Critical Value)(Standard Error) Where: •Point Estimate is the sample statistic estimating the population parameter of interest •Critical Value is a table value based on the sampling distribution of the point estimate and the desired confidence level •Standard Error is the standard deviation of the point estimate DCOVA
13.
Chap 8-13Copyright ©2012
Pearson Education Chap 8-13 Confidence Level Confidence Level Confidence the interval will contain the unknown population parameter A percentage (less than 100%) DCOVA
14.
Chap 8-14Copyright ©2012
Pearson Education Chap 8-14 Confidence Level, (1- ) Suppose confidence level = 95% Also written (1 - ) = 0.95, (so = 0.05) A relative frequency interpretation: 95% of all the confidence intervals that can be constructed will contain the unknown true parameter A specific interval either will contain or will not contain the true parameter No probability involved in a specific interval (continued) DCOVA
15.
Chap 8-15Copyright ©2012
Pearson Education Chap 8-15 Confidence Intervals Population Mean σ Unknown Confidence Intervals Population Proportion σ Known DCOVA
16.
Chap 8-16Copyright ©2012
Pearson Education Chap 8-16 Confidence Interval for μ (σ Known) Assumptions Population standard deviation σ is known Population is normally distributed If population is not normal, use large sample Confidence interval estimate: where is the point estimate Zα/2 is the normal distribution critical value for a probability of /2 in each tail is the standard error n σ /2ZX α X nσ/ DCOVA
17.
Chap 8-17Copyright ©2012
Pearson Education Chap 8-17 Finding the Critical Value, Zα/2 Consider a 95% confidence interval: Zα/2 = -1.96 Zα/2 = 1.96 0.05so0.951 αα 0.025 2 α 0.025 2 α Point Estimate Lower Confidence Limit Upper Confidence Limit Z units: X units: Point Estimate 0 1.96/2Zα DCOVA
18.
Chap 8-18Copyright ©2012
Pearson Education Chap 8-18 Common Levels of Confidence Commonly used confidence levels are 90%, 95%, and 99% Confidence Level Confidence Coefficient, Zα/2 value 1.28 1.645 1.96 2.33 2.58 3.08 3.27 0.80 0.90 0.95 0.98 0.99 0.998 0.999 80% 90% 95% 98% 99% 99.8% 99.9% 1 DCOVA
19.
Chap 8-19Copyright ©2012
Pearson Education Chap 8-19 μμx Intervals and Level of Confidence Confidence Intervals Intervals extend from to (1- )x100% of intervals constructed contain μ; ( )x100% do not. Sampling Distribution of the Mean n σ 2/αZX n σ 2/αZX x x1 x2 /2 /21 DCOVA
20.
Chap 8-20Copyright ©2012
Pearson Education Chap 8-20 Example A sample of 11 circuits from a large normal population has a mean resistance of 2.20 ohms. We know from past testing that the population standard deviation is 0.35 ohms. Determine a 95% confidence interval for the true mean resistance of the population. DCOVA
21.
Chap 8-21Copyright ©2012
Pearson Education Chap 8-21 2.40681.9932 0.20682.20 )11(0.35/1.962.20 n σ /2ZX μ α Example A sample of 11 circuits from a large normal population has a mean resistance of 2.20 ohms. We know from past testing that the population standard deviation is 0.35 ohms. Solution: (continued) DCOVA
22.
Chap 8-22Copyright ©2012
Pearson Education Chap 8-22 Interpretation We are 95% confident that the true mean resistance is between 1.9932 and 2.4068 ohms Although the true mean may or may not be in this interval, 95% of intervals formed in this manner will contain the true mean DCOVA
23.
Chap 8-23Copyright ©2012
Pearson Education Chap 8-23 Confidence Intervals Population Mean σ Unknown Confidence Intervals Population Proportion σ Known DCOVA
24.
Chap 8-24Copyright ©2012
Pearson Education Chap 8-24 Do You Ever Truly Know σ? Probably not! In virtually all real world business situations, σ is not known. If there is a situation where σ is known, then µ is also known (since to calculate σ you need to know µ.) If you truly know µ there would be no need to gather a sample to estimate it.
25.
Chap 8-25Copyright ©2012
Pearson Education Chap 8-25 If the population standard deviation σ is unknown, we can substitute the sample standard deviation, S This introduces extra uncertainty, since S is variable from sample to sample So we use the t distribution instead of the normal distribution Confidence Interval for μ (σ Unknown) DCOVA
26.
Chap 8-26Copyright ©2012
Pearson Education Chap 8-26 Assumptions Population standard deviation is unknown Population is normally distributed If population is not normal, use large sample Use Student’s t Distribution Confidence Interval Estimate: (where tα/2 is the critical value of the t distribution with n -1 degrees of freedom and an area of α/2 in each tail) Confidence Interval for μ (σ Unknown) n S tX 2/α (continued) DCOVA
27.
Chap 8-27Copyright ©2012
Pearson Education Chap 8-27 Student’s t Distribution The t is a family of distributions The tα/2 value depends on degrees of freedom (d.f.) Number of observations that are free to vary after sample mean has been calculated d.f. = n - 1 DCOVA
28.
Chap 8-28Copyright ©2012
Pearson Education Chap 8-28 If the mean of these three values is 8.0, then X3 must be 9 (i.e., X3 is not free to vary) Degrees of Freedom (df) Here, n = 3, so degrees of freedom = n – 1 = 3 – 1 = 2 (2 values can be any numbers, but the third is not free to vary for a given mean) Idea: Number of observations that are free to vary after sample mean has been calculated Example: Suppose the mean of 3 numbers is 8.0 Let X1 = 7 Let X2 = 8 What is X3? DCOVA
29.
Chap 8-29Copyright ©2012
Pearson Education Chap 8-29 Student’s t Distribution t0 t (df = 5) t (df = 13) t-distributions are bell- shaped and symmetric, but have ‘fatter’ tails than the normal Standard Normal (t with df = ∞) Note: t Z as n increases DCOVA
30.
Chap 8-30Copyright ©2012
Pearson Education Chap 8-30 Student’s t Table Upper Tail Area df .10 .05 .025 1 3.078 6.314 12.706 2 1.886 3 1.638 2.353 3.182 t0 2.920 The body of the table contains t values, not probabilities Let: n = 3 df = n - 1 = 2 = 0.10 /2 = 0.05 /2 = 0.05 DCOVA 4.3032.920
31.
Chap 8-31Copyright ©2012
Pearson Education Chap 8-31 Selected t distribution values With comparison to the Z value Confidence t t t Z Level (10 d.f.) (20 d.f.) (30 d.f.) (∞ d.f.) 0.80 1.372 1.325 1.310 1.28 0.90 1.812 1.725 1.697 1.645 0.95 2.228 2.086 2.042 1.96 0.99 3.169 2.845 2.750 2.58 Note: t Z as n increases DCOVA
32.
Chap 8-32Copyright ©2012
Pearson Education Chap 8-32 Example of t distribution confidence interval A random sample of n = 25 has X = 50 and S = 8. Form a 95% confidence interval for μ d.f. = n – 1 = 24, so The confidence interval is 2.06390.025t/2αt 25 8 (2.0639)50 n S /2αtX 46.698 ≤ μ ≤ 53.302 DCOVA
33.
Chap 8-33Copyright ©2012
Pearson Education Chap 8-33 Example of t distribution confidence interval Interpreting this interval requires the assumption that the population you are sampling from is approximately a normal distribution (especially since n is only 25). This condition can be checked by creating a: Normal probability plot or Boxplot (continued) DCOVA
34.
Chap 8-34Copyright ©2012
Pearson Education Chap 8-34 Confidence Intervals Population Mean σ Unknown Confidence Intervals Population Proportion σ Known DCOVA
35.
Chap 8-35Copyright ©2012
Pearson Education Chap 8-35 Confidence Intervals for the Population Proportion, π An interval estimate for the population proportion ( π ) can be calculated by adding an allowance for uncertainty to the sample proportion ( p ) DCOVA
36.
Chap 8-36Copyright ©2012
Pearson Education Chap 8-36 Confidence Intervals for the Population Proportion, π Recall that the distribution of the sample proportion is approximately normal if the sample size is large, with standard deviation We will estimate this with sample data (continued) n p)p(1 n )(1 σp DCOVA
37.
Chap 8-37Copyright ©2012
Pearson Education Chap 8-37 Confidence Interval Endpoints Upper and lower confidence limits for the population proportion are calculated with the formula where Zα/2 is the standard normal value for the level of confidence desired p is the sample proportion n is the sample size Note: must have X > 5 and n – X > 5 n p)p(1 /2Zp α DCOVA
38.
Chap 8-38Copyright ©2012
Pearson Education Chap 8-38 Example A random sample of 100 people shows that 25 are left-handed. Form a 95% confidence interval for the true proportion of left-handers DCOVA
39.
Chap 8-39Copyright ©2012
Pearson Education Chap 8-39 Example A random sample of 100 people shows that 25 are left-handed. Form a 95% confidence interval for the true proportion of left-handers. /1000.25(0.75)1.9625/100 p)/np(1Zp /2 0.33490.1651 (0.0433)1.960.25 (continued) DCOVA np = 100 * 0.25 = 25 > 5 & n(1-p) = 100 * 0.75 = 75 > 5 Make sure the sample is big enough
40.
Chap 8-40Copyright ©2012
Pearson Education Chap 8-40 Interpretation We are 95% confident that the true percentage of left-handers in the population is between 16.51% and 33.49%. Although the interval from 0.1651 to 0.3349 may or may not contain the true proportion, 95% of intervals formed from samples of size 100 in this manner will contain the true proportion. DCOVA
41.
Chap 8-41Copyright ©2012
Pearson Education Chap 8-41 Determining Sample Size For the Mean Determining Sample Size For the Proportion DCOVA
42.
Chap 8-42Copyright ©2012
Pearson Education Chap 8-42 Sampling Error The required sample size can be found to obtain a desired margin of error (e) with a specified level of confidence (1 - ) The margin of error is also called sampling error the amount of imprecision in the estimate of the population parameter the amount added and subtracted to the point estimate to form the confidence interval DCOVA
43.
Chap 8-43Copyright ©2012
Pearson Education Chap 8-43 Determining Sample Size For the Mean Determining Sample Size n σ 2/αZX n σ 2/αZe Sampling error (margin of error) DCOVA
44.
Chap 8-44Copyright ©2012
Pearson Education Chap 8-44 Determining Sample Size For the Mean Determining Sample Size n σ 2/Ze (continued) 2 22 2/ σ e Z nNow solve for n to get DCOVA
45.
Chap 8-45Copyright ©2012
Pearson Education Chap 8-45 Determining Sample Size To determine the required sample size for the mean, you must know: The desired level of confidence (1 - ), which determines the critical value, Zα/2 The acceptable sampling error, e The standard deviation, σ (continued) DCOVA
46.
Chap 8-46Copyright ©2012
Pearson Education Chap 8-46 Required Sample Size Example If = 45, what sample size is needed to estimate the mean within ± 5 with 90% confidence? (Always round up) 219.19 5 (45)(1.645) e σZ n 2 22 2 22 So the required sample size is n = 220 DCOVA
47.
Chap 8-47Copyright ©2012
Pearson Education Chap 8-47 If σ is unknown If unknown, σ can be estimated when determining the required sample size Use a value for σ that is expected to be at least as large as the true σ Select a pilot sample and estimate σ with the sample standard deviation, S DCOVA
48.
Chap 8-48Copyright ©2012
Pearson Education Chap 8-48 Determining Sample Size Determining Sample Size For the Proportion 2 2 e )(1Z n ππNow solve for n to getn )(1 Ze ππ (continued) DCOVA
49.
Chap 8-49Copyright ©2012
Pearson Education Chap 8-49 Determining Sample Size To determine the required sample size for the proportion, you must know: The desired level of confidence (1 - ), which determines the critical value, Zα/2 The acceptable sampling error, e The true proportion of events of interest, π π can be estimated with a pilot sample if necessary (or conservatively use 0.5 as an estimate of π) (continued) DCOVA
50.
Chap 8-50Copyright ©2012
Pearson Education Chap 8-50 Required Sample Size Example How large a sample would be necessary to estimate the true proportion defective in a large population within 3%, with 95% confidence? (Assume a pilot sample yields p = 0.12) DCOVA
51.
Chap 8-51Copyright ©2012
Pearson Education Chap 8-51 Required Sample Size Example Solution: For 95% confidence, use Zα/2 = 1.96 e = 0.03 p = 0.12, so use this to estimate π So use n = 451 450.74 (0.03) 0.12)(0.12)(1(1.96) e )(1Z n 2 2 2 2 /2 ππ (continued) DCOVA
52.
Chap 8-52Copyright ©2012
Pearson Education Chap 8-52 Applications in Auditing Six advantages of statistical sampling in auditing Sampling is less time consuming and less costly Sampling provides an objective way to calculate the sample size in advance Sampling provides results that are objective and defensible Because the sample size is based on demonstrable statistical principles, the audit is defensible before one’s superiors and in a court of law DCOVA
53.
Chap 8-53Copyright ©2012
Pearson Education Chap 8-53 Applications in Auditing Sampling provides an estimate of the sampling error Allows auditors to generalize their findings to the population with a known sampling error Can provide more accurate conclusions about the population Sampling is often more accurate for drawing conclusions about large populations Examining every item in a large population is subject to significant non-sampling error Sampling allows auditors to combine, and then evaluate collectively, samples collected by different individuals (continued) DCOVA
54.
Chap 8-54Copyright ©2012
Pearson Education Chap 8-54 Confidence Interval for Population Total Amount Point estimate for a population of size N: Confidence interval estimate: XNtotalPopulation 1n S )2/( N nN tNXN α (This is sampling without replacement, so use the finite population correction factor in the confidence interval formula) DCOVA
55.
Chap 8-55Copyright ©2012
Pearson Education Chap 8-55 Confidence Interval for Population Total: Example A firm has a population of 1,000 accounts and wishes to estimate the total population value. A sample of 80 accounts is selected with average balance of $87.6 and standard deviation of $22.3. Construct the 95% confidence interval estimate of the total balance. DCOVA
56.
Chap 8-56Copyright ©2012
Pearson Education Chap 8-56 Example Solution The 95% confidence interval for the population total balance is $82,837.52 to $92,362.48 /2 S ( ) 1n 22.3 1,000 80 (1,000)(87.6) (1,000)(1.9905) 1,000 180 87,600 4,762.48 N n N X N t N 22.3S,6.87X80,n,1000N DCOVA
57.
Chap 8-57Copyright ©2012
Pearson Education Chap 8-57 Point estimate for a population of size N: Where the average difference, D, is: DNDifferenceTotal Confidence Interval for Total Difference valueoriginal-valueauditedDwhere n D D i n 1i i DCOVA
58.
Chap 8-58Copyright ©2012
Pearson Education Chap 8-58 Confidence interval estimate: where 1n S )( D 2/ N nN tNDN Confidence Interval for Total Difference (continued) 1 )D( 1 2 n D S n i i D DCOVA
59.
Chap 8-59Copyright ©2012
Pearson Education Chap 8-59 One-Sided Confidence Intervals Application: find the upper bound for the proportion of items that do not conform with internal controls where Zα is the standard normal value for the level of confidence desired p is the sample proportion of items that do not conform n is the sample size N is the population size 1N nN n p)p(1 ZpboundUpper α DCOVA
60.
Chap 8-60Copyright ©2012
Pearson Education Chap 8-60 Ethical Issues A confidence interval estimate (reflecting sampling error) should always be included when reporting a point estimate The level of confidence should always be reported The sample size should be reported An interpretation of the confidence interval estimate should also be provided
61.
Chap 8-61Copyright ©2012
Pearson Education Chap 8-61 Chapter Summary Introduced the concept of confidence intervals Discussed point estimates Developed confidence interval estimates Created confidence interval estimates for the mean (σ known) Determined confidence interval estimates for the mean (σ unknown) Created confidence interval estimates for the proportion Determined required sample size for mean and proportion confidence interval estimates with a desired margin of error
62.
Chap 8-62Copyright ©2012
Pearson Education Chap 8-62 Chapter Summary Developed applications of confidence interval estimation in auditing Confidence interval estimation for population total Confidence interval estimation for total difference in the population One-sided confidence intervals for the proportion nonconforming Addressed confidence interval estimation and ethical issues (continued)
63.
Chap 8-63Copyright ©2012
Pearson Education Online Topic Estimation & Sample Size Determination For Finite Populations Basic Business Statistics 12th Edition Global Edition
64.
Chap 8-64Copyright ©2012
Pearson Education Topic Learning Objectives In this topic, you learn: When to use a finite population correction factor in calculating a confidence interval for either µ or π How to use a finite population correction factor in calculating a confidence interval for either µ or π How to use a finite population correction factor in calculating a sample size for a confidence interval for either µ or π
65.
Chap 8-65Copyright ©2012
Pearson Education Use A fpc When Sampling More Than 5% Of The Population (n/N > 0.05) DCOVA Confidence Interval For µ with a fpc /2 1 S N n X t Nn Confidence Interval For π with a fpc 1 )1( 2/ N nN n pp zp A fpc simply reduces the standard error of either the sample mean or the sample proportion
66.
Chap 8-66Copyright ©2012
Pearson Education Confidence Interval for µ with a fpc DCOVA 10s50,X100,n1000,NSuppose )88.51,12.48(88.150 11000 1001000 100 10 984.150 1 :forCI95% 2/ N nN n s tX
67.
Chap 8-67Copyright ©2012
Pearson Education Determining Sample Size with a fpc Calculate the sample size (n0) without a fpc For µ: For π: Apply the fpc utilizing the following formula to arrive at the final sample size (n). n = n0N / (n0 + (N-1)) DCOVA 2 2 / 2 0 2 Z n e 2 / 2 0 2 (1 )Z n e
68.
Chap 8-68Copyright ©2012
Pearson Education Topic Summary Described when to use a finite population correction in calculating a confidence interval for either µ or π Examined the formulas for calculating a confidence interval for either µ or π utilizing a finite population correction Examined the formulas for calculating a sample size in a confidence interval for either µ or π
Descargar ahora