Minitab Table of Contents

Detailed Table of Contents

10. Continuous Probability Distributions

PREVIEW

10.1 BASIC CONCEPTS

Continuous Random Variables
The Probability Density Function

10.2 THE NORMAL PROBABILITY DISTRIBUTION

A Graphical Exposition

APPLICATION 10.1 Honest Weights and the Normal Curve of Error

APPLICATION 10.2 The Great IQ Controversy

The Formula

10.3 THE STANDARD NORMAL CURVE

10.4 THE STANDARD NORMAL TABLES

Tables of Ordinates
Tables of Cumulative Relative Frequencies

APPLICATION 10.3 The Decision to Seed Hurricanes

10.5 NORMAL PROBABILITIES AND COMPUTER PROGRAMS

10.6 USING THE NORMAL PROBABILITY DISTRIBUTION TO APPROXIMATE DISCRETE PROBABILITY DISTRIBUTIONS

Approximating Binomial Probabilities
Approximating Poisson Probabilities
Approximating Hypergeometric Probabilities

10.7 THE EXPONENTIAL PROBABILITY DISTRIBUTION

A Graphical Exposition
The Formulas

10.8 EXPONENTIAL PROBABILITY TABLES

10.9 EXPONENTIAL PROBABILITIES AND COMPUTER PROGRAMS

10.10 THE UNIFORM PROBABILITY DISTRIBUTION

A Graphical Exposition
The Formulas

10.11 UNIFORM PROBABILITES AND COMPUTER PROGRAMS

Part V Basic Inference

11. Sampling Distributions

PREVIEW

11.1 REVIEWING BASIC CONCEPTS

11.2 THE CONCEPT OF THE SAMPLING DISTRIBUTION

Inferring Parameters From Statistics
Random Error Revisited
Deriving Sampling Distributions

11.3 SAMPLING DISTRIBUTION SUMMARY MEASURES

11.4 THE GENERAL SHAPE OF SAMPLING DISTRIBUTIONS

The Sampling Distribution of the Sample Mean
The Sampling Distribution of the Sample Proportion

11.5 MATHEMATICAL RELATIONSHIPS AMONG SUMMARY MEASURES

The Sampling Distribution of X bar
The Sampling Distribution of P

11.6 THE CENTRAL LIMIT THEOREM REVIEWED

APPLICATION 11.1 Improvements in Sampling Techniques: Nielsen's People Meter

APPLICATION 11.2 Poll on Abortion Finds Nation Sharply Divided

11.7 COMPUTER SIMULATIONS

12. Estimation

PREVIEW

12.1 BASIC CONCEPTS

12.2 DEFINING A GOOD ESTIMATOR

Unbiasedness
Efficiency
Consistency
A Compromise: Mean Squared Error

12.3 MAKING POINT ESTIMATES

Estimating a Single Population Mean
Estimating a Single Population Proportion
Estimating the Difference Between Two Parameters

12.4 THE NATURE OF INTERVAL ESTIMATES

The Margin of Error
Constructing Confidence Intervals
80-Percent Confidence Intervals
Favorite Confidence Levels

12.5 LARGE-SAMPLE INTERVAL ESTIMATES OF A POPULATION MEAN

12.6 LARGE-SAMPLE INTERVAL ESTIMATES OF A POPULATION PROPORTION

APPLICATION 12.1 Election Advice

12.7 LARGE-SAMPLE INTERVAL ESTIMATES OF THE DIFFERENCE BETWEEN TWO POPULATION MEANS

Large and Independent Samples

APPLICATION 12.2 The Danger of Being Left-Handed

Large Matched-Pairs Sample

12.8 LARGE-SAMPLE INTERVAL ESTIMATES OF THE DIFFERENCE BETWEEN TWO POPULATION PROPORTIONS

12.9 SMALL-SAMPLE INTERVAL ESTIMATES OF A POPULATION MEAN

Student's t Distribution
The t Distribution Table

12.10 SMALL-SAMPLE INTERVAL ESTIMATES OF THE DIFFERENCE BETWEEN TWO POPULATION MEANS

Small and Independent Samples
Small Matched-Pairs Sample

APPLICATION 12.3 Matched-Pairs Sampling in the Chemical Industry

12.11 THE OPTIMAL SAMPLE SIZE

Best Sample Size When Estimating a Population Mean

APPLICATION 12.4 Reducing Sample-Collection Costs by Work Sampling

Best Sample Size When Estimating a Population Proportion

12.12 A NOTE ON BAYESIAN ESTIMATION

13. Hypothesis Testing: The Classical Technique

PREVIEW

13.1 INTRODUCTION

13.2 STEP 1: FORMULATING TWO OPPOSING HYPOTHESES

Hypotheses About a Population Mean
Hypotheses About the Difference Between Two Population Means
Hypotheses About a Population Proportion (or the Difference Between Two Such Proportions)

13.3 STEP 2: SELECTING A TEST STATISTIC

13.4 STEP 3: DERIVING A DECISION RULE

A Dilemma
The Dilemma Solved
The Level of Significance
Three Illustrations

13.5 STEP 4: USING SAMPLE DATA TO COMPUTE THE TEST STATISTIC AND CONFRONTING IT WITH THE DECISION RULE

13.6 THE POSSIBILITY OF EROR

When the Null Hypothesis Is True
When the Null Hypothesis Is False
The Trade-Off Between a Type I Error and a Type II Error

13.7 LARGE-SAMPLE HYPOTHESIS TESTS

Tests of a Population Mean
Tests of a Population Proportion
Tests of the Difference Between Two Population Means: Independent Samples
Tests of the Difference Between Two Population Means: Matched-Pairs Samples
Tests of the Difference Between Two Population Proportions

APPLICATION 13.1 Antitrust Pork Barrel

13.8 USING p VALUES: A FAMOUS CONTROVERSY

Neyman/Pearson Versus Fisher
Calculating p Values
Forming an Opinion
Reconciling the Neyman/Pearson and Fisher Approaches

13.9 SMALL-SAMPLE HYPOTHESIS TESTS

Tests of a Population Mean
Tests of the Difference Between Two Population Means: Independent Samples

APPLICATION 13.2 The Never-Ending Search for New and Better Drugs

APPLICATION 13.3 Do Nursing Homes Discriminate Against the Poor?

Tests of the Difference Between Two Population Means: Matched-Pairs Samples

13.10 p VALUES IN SMALL-SAMPLE TESTS

13.11 CRITICISMS OF HYPOTHESIS TESTING

Serious Violations of Assumptions
Rigid Interpretation of the "Acceptance" Criterion
Nonpublication of Nonsignificant Results

14. Hypothesis Testing: The Chi-Square Technique

PREVIEW

14.1 INTRODUCTION

14.2 TESTING THE ALLEGED INDEPENDENCE OF TWO QUALITATIVE VARIABLES

Independent Versus Dependent Events
Contingency Tables
Computing the Chi-Square Statistic
Interpreting the Chi-Square Statistic
Sampling Distributions of Chi Square
The Chi-Square Table
Summary of Procedure

APPLICATION 14.1 Racial Bias in Mortgage Lending?

14.3 MAKING INFERENCES ABOUT MORE THAN TWO POPULATION PROPORTIONS

APPLICATION 14.2 Were Mendel's Data Fudged?

14.4 MAKING INFERENCES ABOUT A POPULATION VARIANCE

Probability Intervals for the Sample Variance
Confidence Intervals for the Population Variance
Testing Hypotheses About the Population Variance

14.5 CONDUCTING GOODNESS-OF-FIT TESTS

Testing a Fit to the Binomial Distribution
Testing a Fit to the Poisson Distribution
Testing a Fit to the Normal Distribution
Testing a Fit to the Uniform Distribution

APPLICATION 14.3 Testing Random Digits for Randomness

Testing a Fit to Any Specified Distribution

Part VI Advanced Inference

15. Analysis of Variance

PREVIEW

15.1 INTRODUCTION

15.2 THE NATURE OF ANOVA

Crucial Assumptions
Comparing Two Variances
A Graphical Exposition

15.3 ALTERNATIVE VERSIONS OF ANOVA

15.4 ONE-WAY ANOVA

Variation Among Columns: Explained By Treatments
Variation Within Columns: Due to Error
The One-Way ANOVA Table
The F Distribution

15.5 TWO-WAY ANOVA: NO INTERACTION

The Toothpaste Study
Revisited Variation Among Columns: Explained By Treatments
Variation Among Rows: Explained By Blocks
Variation Due to Error
The Two-Way ANOVA Table Without Interaction

15.6 TWO-WAY ANOVA: WITH INTERACTION

Variation Among Columns: Explained By Treatments
Variation Among Rows: Explained By Blocks
Variation Explained By Interaction
Variation Due to Error
The Two-Way ANOVA Table With Interaction

15.7 THREE-WAY ANOVA

Variation Explained By Treatments
Variation Explained By Column Blocks
Variation Explained By Row Blocks
Variation Due to Error
The Three-Way ANOVA Table

15.8 DISCRIMINATING AMONG DIFFERENT POPULATION MEANS

Establishing Confidence Intervals For Individual Population Means
Establishing Confidence Intervals For the Difference Between Two Population Means
Tukey's HSD Test

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Table of Contents Part III

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