Testing p1 – p2 (Case I) Example: The effectiveness of Aspirin

Slides:

Advertisements

Similar presentations

Objective: To test claims about inferences for two proportions, under specific conditions Chapter 22.

Advertisements

Chi square.  Non-parametric test that’s useful when your sample violates the assumptions about normality required by other tests ◦ All other tests we’ve.

Testing the Difference Between Means (Dependent Samples)

Probability & Statistical Inference Lecture 7 MSc in Computing (Data Analytics)

Introduction to Hypothesis Testing

Hypothesis Testing Steps of a Statistical Significance Test. 1. Assumptions Type of data, form of population, method of sampling, sample size.

Introduction to Hypothesis Testing

1 Inference about Comparing Two Populations Chapter 13.

1 Inference about Comparing Two Populations Chapter 13.

BCOR 1020 Business Statistics Lecture 21 – April 8, 2008.

Statistics Testing the Difference Between Proportions Statistics Mrs. Spitz Spring 2009.

Economics 173 Business Statistics Lecture 9 Fall, 2001 Professor J. Petry

Hypothesis Testing for Proportions 1 Section 7.4.

8.1 Inference for a Single Proportion

Section Inference for Experiments Objectives: 1.To understand how randomization differs in surveys and experiments when comparing two populations.

Econ 338/envr 305 clicker questions

STATISTICAL INFERENCE PART VIII HYPOTHESIS TESTING - APPLICATIONS – TWO POPULATION TESTS 1.

1 Inference about Two Populations Chapter Introduction Variety of techniques are presented to compare two populations. We are interested in:

Example (which tire lasts longer?) To determine whether a new steel-belted radial tire lasts longer than a current model, the manufacturer designs the.

1 Nonparametric Statistical Techniques Chapter 17.

STATISTICAL INFERENCE PART VI HYPOTHESIS TESTING 1.

Statistical Significance for a two-way table Inference for a two-way table We often gather data and arrange them in a two-way table to see if two categorical.

1 Inference about Two Populations Chapter Introduction Variety of techniques are presented whose objective is to compare two populations. We.

We’ve learned: 1) Hypothesis testing for population mean when population variance is known ( Z-test ) ( large sample size or assume population is normal.

Copyright © 2013, 2009, and 2007, Pearson Education, Inc. Chapter 10 Comparing Two Groups Section 10.1 Categorical Response: Comparing Two Proportions.

AP Process Test of Significance for Population Proportion.

1 Economics 173 Business Statistics Lectures 5 & 6 Summer, 2001 Professor J. Petry.

Tests of Significance: The Basics ESS chapter 15 © 2013 W.H. Freeman and Company.

Major Steps. 1.State the hypotheses.  Be sure to state both the null hypothesis and the alternative hypothesis, and identify which is the claim. H0H0.

Copyright © 2015, 2012, and 2009 Pearson Education, Inc. 1 Chapter Hypothesis Testing with Two Samples 8.

HS 1679B: Comparing Proportions1 9B: Comparing two proportions.

STA Lecture 231 STA 291 Lecture 23 Testing hypothesis about population proportion(s) Examples.

Chapter 12 Inference About One Population. We shall develop techniques to estimate and test three population parameters.  Population mean   Population.

Chapter 9: Hypothesis Tests for One Population Mean 9.2 Terms, Errors, and Hypotheses.

Copyright © 2013, 2009, and 2007, Pearson Education, Inc. Chapter 10 Comparing Two Groups Section 10.3 Other Ways of Comparing Means and Comparing Proportions.

Lec. 19 – Hypothesis Testing: The Null and Types of Error.

Step by Step Example of Hypothesis Testing of a Proportion.

1 Nonparametric Statistical Techniques Chapter 18.

Section 7.4 Hypothesis Testing for Proportions © 2012 Pearson Education, Inc. All rights reserved. 1 of 101.

A telephone company representative estimates that 40% of its customers have call-waiting service. To test this hypothesis, she selected a sample of 100.

Testing Claims about a Population Mean Objective: test a claim.

Section 8.5 Day 3.

Testing the Difference Between Proportions

Hypothesis Testing for Proportions

Chapter 9: Inferences Involving One Population

Hypothesis Testing II: The Two-sample Case

Inference about Two Populations

Chapter 12 Tests with Qualitative Data

Hypothesis Tests for Two Population Proportions

Categorical Variables

Chapter Review Problems

Chapter 9: Hypothesis Testing

Section 12.2: Tests about a Population Proportion

Do you know population SD? Use Z Test Are there only 2 groups to

Elementary Statistics: Picturing The World

Hypothesis Tests for Proportions

Chapter 10 Analyzing the Association Between Categorical Variables

Hypothesis tests for the difference between two proportions

Summary of Tests Confidence Limits

Analyzing the Association Between Categorical Variables

CHAPTER 12 Inference for Proportions

Power Section 9.7.

CHAPTER 12 Inference for Proportions

More About Tests Notes from

STATISTICAL INFERENCE PART VI

Hypothesis Testing for Proportions

Warm Up Consumers of diet food products are concerned about the number of calories in each meal. A manufacturer advertises the number of calories in a.

MATH 2311 Section 8.3.

Presentation transcript:

Testing p1 – p2 (Case I) Example: The effectiveness of Aspirin A research project employing 22,000 American physicians was conduct to discover whether aspirin can prevent heart attacks. Half of the participants in the research took aspirin, and half took placebo. In a three years period,104 of those who took aspirin and 189 of those who took the placebo had had heart attacks. Is aspirin effective in preventing heart attacks?

The effectiveness of Aspirin Solution The problem objective is to compare the population of sales of the two packaging designs. The data is qualitative (yes/no for the purchase of the new design per customer) The hypotheses test are H0: p1 - p2 = 0 H1: p1 - p2 > 0 We identify here case 1. Population 1 – purchases of Design A Population 2 – purchases of Design B

Solving by hand For a 5% significance level the rejection region is z > za = -z.05 = -1.645 - 5.02 < - 1.645, so reject the null hypothesis.