BA 275 Quantitative Business Methods

Slides:



Advertisements
Similar presentations
1 BA 275 Quantitative Business Methods Statistical Inference: Hypothesis Testing Type I and II Errors Power of a Test Hypothesis Testing Using Statgraphics.
Advertisements

Inference about the Difference Between the
1 1 Slide © 2009 Econ-2030-Applied Statistics-Dr. Tadesse. Chapter 11: Comparisons Involving Proportions and a Test of Independence n Inferences About.
Statistical Inference About Means and Proportions With Two Populations
Lecture 3 Miscellaneous details about hypothesis testing Type II error
Chapter 9 Chapter 10 Chapter 11 Chapter 12
1 BA 275 Quantitative Business Methods Review Statistical Inference on Two Populations The p-value Approach Practice Problems Office Hours: Monday, 2/27/06:
BCOR 1020 Business Statistics Lecture 21 – April 8, 2008.
1 BA 275 Quantitative Business Methods Statistical Inference: Confidence Interval Estimation Introduction Estimating the population mean  Examples Office.
Chapter 9 Hypothesis Testing.
BCOR 1020 Business Statistics Lecture 20 – April 3, 2008.
Chapter 8 Introduction to Hypothesis Testing
1 BA 275 Quantitative Business Methods Hypothesis Testing using the p-Value Statistical Inference on Two Populations Quiz #6 Agenda.
ESTIMATION AND HYPOTHESIS TESTING: TWO POPULATIONS
5-3 Inference on the Means of Two Populations, Variances Unknown
Statistics for Managers Using Microsoft® Excel 5th Edition
Chapter 11a: Comparisons Involving Proportions and a Test of Independence Inference about the Difference between the Proportions of Two Populations Hypothesis.
CHAPTER 23 Inference for Means.
QA 233 PRACTICE PROBLEMS PROBABILITY, SAMPLING DISTRIBUTIONS CONFIDENCE INTERVALS & HYPOTHESIS TESTING These problems will give you an opportunity to practice.
Math 227 Elementary Statistics
McGraw-Hill/IrwinCopyright © 2009 by The McGraw-Hill Companies, Inc. All Rights Reserved. Chapter 9 Hypothesis Testing.
Experimental Statistics - week 2
Confidence Intervals and Hypothesis Testing - II
Fundamentals of Hypothesis Testing: One-Sample Tests
1 BA 275 Quantitative Business Methods Hypothesis Testing Elements of a Test Concept behind a Test Examples Agenda.
Review of Basic Statistics. Definitions Population - The set of all items of interest in a statistical problem e.g. - Houses in Sacramento Parameter -
Copyright © Cengage Learning. All rights reserved. 13 Linear Correlation and Regression Analysis.
Inference for One-Sample Means
Section 10.1 ~ t Distribution for Inferences about a Mean Introduction to Probability and Statistics Ms. Young.
INFERENCE ABOUT MEANS Chapter 23. CLT!! If our data come from a simple random sample (SRS) and the sample size is sufficiently large, then we know the.
More About Significance Tests
+ The Practice of Statistics, 4 th edition – For AP* STARNES, YATES, MOORE Chapter 9: Testing a Claim Section 9.3a Tests About a Population Mean.
Albert Morlan Caitrin Carroll Savannah Andrews Richard Saney.
Economics 173 Business Statistics Lecture 6 Fall, 2001 Professor J. Petry
More Inferences About Means Student’s t distribution and sample standard deviation, s.
1 BA 275 Quantitative Business Methods Confidence Interval Estimation Estimating the Population Proportion Hypothesis Testing Elements of a Test Concept.
1 1 Slide © 2006 Thomson/South-Western Slides Prepared by JOHN S. LOUCKS St. Edward’s University Slides Prepared by JOHN S. LOUCKS St. Edward’s University.
1 1 Slide Chapter 11 Comparisons Involving Proportions n Inference about the Difference Between the Proportions of Two Populations Proportions of Two Populations.
Chapter 23 Inference for One- Sample Means. Steps for doing a confidence interval: 1)State the parameter 2)Conditions 1) The sample should be chosen randomly.
Introduction to Inferece BPS chapter 14 © 2010 W.H. Freeman and Company.
1 1 Slide © 2009 Thomson South-Western. All Rights Reserved Slides by JOHN LOUCKS St. Edward’s University.
Slide Slide 1 Copyright © 2007 Pearson Education, Inc Publishing as Pearson Addison-Wesley. Overview.
Copyright ©2013 Pearson Education, Inc. publishing as Prentice Hall 9-1 σ σ.
Chap 8-1 Fundamentals of Hypothesis Testing: One-Sample Tests.
1 BA 275 Quantitative Business Methods Quiz #3 Statistical Inference: Hypothesis Testing Types of a Test P-value Agenda.
1 BA 275 Quantitative Business Methods Quiz #2 Sampling Distribution of a Statistic Statistical Inference: Confidence Interval Estimation Introduction.
Inen 460 Lecture 2. Estimation (ch. 6,7) and Hypothesis Testing (ch.8) Two Important Aspects of Statistical Inference Point Estimation – Estimate an unknown.
Hypothesis Testing Errors. Hypothesis Testing Suppose we believe the average systolic blood pressure of healthy adults is normally distributed with mean.
© Copyright McGraw-Hill 2004
Math 4030 – 9b Comparing Two Means 1 Dependent and independent samples Comparing two means.
Inferences Concerning Variances
Confidence Intervals for a Population Mean, Standard Deviation Unknown.
Understanding Basic Statistics Fourth Edition By Brase and Brase Prepared by: Lynn Smith Gloucester County College Chapter Nine Hypothesis Testing.
Chapter 6 Test Review z area ararea ea
Daniel S. Yates The Practice of Statistics Third Edition Chapter 12: Significance Tests in Practice Copyright © 2008 by W. H. Freeman & Company.
4-1 Statistical Inference Statistical inference is to make decisions or draw conclusions about a population using the information contained in a sample.
AP Test Practice. A student organization at a university is interested in estimating the proportion of students in favor of showing movies biweekly instead.
Confidence Intervals for Means
Chapter Nine Hypothesis Testing.
Math 4030 – 10b Inferences Concerning Variances: Hypothesis Testing
Math 4030 – 10a Tests for Population Mean(s)
Chapters 20, 21 Hypothesis Testing-- Determining if a Result is Different from Expected.
Chapter 9 Testing A Claim
Chapter 9 Hypothesis Testing.
Chapter Nine Part 1 (Sections 9.1 & 9.2) Hypothesis Testing
Daniela Stan Raicu School of CTI, DePaul University
Daniela Stan Raicu School of CTI, DePaul University
Homework: pg. 693 #4,6 and pg. 698#11,12 4.) A. µ=the mean gas mileage for Larry’s car on the highway Ho: µ=26 mpg Ha: µ>26 mpg B.
Significance Tests: The Basics
How Confident Are You?.
Presentation transcript:

BA 275 Quantitative Business Methods Agenda Statistical Inference for Small Sample Size Statistical Inference for Two-Sample Problems

Quiz #4: Question 1 Past experience indicates that the monthly long-distance telephone bill is normally distributed with a mean of $17.85 and a (population) standard deviation $3.87. After an advertising campaign aimed at increasing long-distance telephone usage, a random sample of 25 household bills was taken. You are concerned whether the campaign was successful, and would like to perform a test to find out. What are the null and the alternative hypotheses? If the sample mean turns out to be $15, do you reject the null hypothesis? Why or why not? Assume a = 5%. If the sample mean turns out to be $29.13, do you reject the null hypothesis? Why or why not? Assume a = 5%. Finally, the actual sample mean in your sample is $19.13. Do you reject the null hypothesis? Was the campaign successful? Assume a = 5%.

Quiz #4: Question 2 The Survey of Study Habits and Attitudes (SSHA) is a psychological test that measures the motivation, attitude toward school, and study habits of students. The mean score for U.S. college students is about 115, and the standard deviation is about 30. A teacher suspects that older students (30 years or older) have better attitudes toward school and wishes to test H0: m = 115 vs. Ha: m > 115. To test the hypothesis, she decides to use a sample of n = 25, and to reject the null hypothesis H0 if the sample mean > 126. Find the probability of a Type I error. Find the probability of a Type II error when m = 135. Find the power of the test when m = 135. Suppose the sample mean turns out to be 131.2. Find the p-value of the test. Construct a 95% confidence interval for the mean SSHA score for older students. Write your final answer in the following format: ( point estimate ) ± ( margin of error )

Central Limit Theorem (CLT) s is unknown but n is large

Central Limit Theorem (CLT) s is unknown and n is small

T distribution with degrees of freedom 5 vs. Normal(0, 1)

Example 1 (Example 3 from 2-7-07) A random sample of 10 one-bedroom apartments (Ouch, a small sample) from your local newspaper gives a sample mean of $541.5 and sample standard deviation of $69.16. Assume a = 5%. Q1. Does the sample give good reason to believe that the mean rent of all advertised apartments is greater than $500 per month? (Need H0 and Ha, rejection region and conclusion.) Q2. Find the p-value. Q3. Construct a 95% confidence interval for the mean rent of all advertised apartments. Q4. What assumption is necessary to answer Q1-Q3.

Example 2 A bank wonders whether omitting the annual credit card fee for customers who charge at least $2400 in a year would increase the amount charged on its credit card the following year. A random sample of 51 customers is chosen to see if the mean amount charged increases from the previous year under the no-fee offer. The mean increase is $342 and the standard deviation is $108. Q1. Let a = 5%. State H0 and Ha and carry out a t test. Approximate the p-value. Q2. Give a 95% confidence interval for the mean amount of the increase. Q3. Suppose that the bank wanted to be quite certain of detecting a mean increase of m = $100 in the amount charged. Is n = 51 enough to detect the increase? SG Demo

Two-Sample Inference on m1 – m2 Population #1 Population #2 Sample #1 Sample #2

Example 3 U.S. Sales Japan Sales Sample size 30 50 Sample mean $14,545 $15,243 Sample Std. $ 1,989 $ 1,842 Q1. Do we have enough evidence to claim that the auto retail price in Japan is higher? Q2. If so, by how much?

Example 4 Do government employees take longer coffee breaks than private sector workers?

Answer Key Example 1: see the slides from 2-7-2007. Example 2: Q1. H0: m = 0 vs. Ha: m > 0. Given a = 5% and df = n – 1 = 50, the rejection region is defined as: Reject H0 if t > 1.676 (or if the sample mean > 25.5984.) Since , we should reject H0. Q2. 342 ± 2.009 x 108 / SQRT(51) Q3. Power = P( detecting a mean increase of $100 ) = P( being able to reject H0 when true m = $100 ) = P( the sample mean > 25.5984 when m = $100 ) = 1.0000. Note that 25.5984 came from the rejection region in Q1.

T Table (Table D)