## Description

**Solution Manual for Statistics for Understanding Statistical Analysis and Modeling Bruhl**

**Solution Manual for Statistics for Understanding Statistical Analysis and Modeling By Robert Bruhl, ISBN: 9781506317410**

**Table of Contents**

Introduction

Acknowledgments

About the Author

PART I. RESEARCH DESIGN

Purpose: Making Sense of What We Observe

Deciding How to Represent Properties of a Phenomenon

Describing Differences or Explaining Differences Between Phenomena?

Deciding How to Collect Observations

Chapter 1. “Why” Conduct Research, and “Why” Use Statistics?

1.0 Learning Objectives

1.1 Motivation

1.2 Representation and Modeling

1.3 A Special Case: Investigating Subjective Behavior

1.4 Reasons for an Empirical Investigation

1.5 Summary

1.6 Exercises

1.7 Some Formal Terminology (Optional)

Chapter 2. Methods of Quantitative Empirical Investigation

2.0 Learning Objectives

2.1 Motivation

2.2 Instrumentation: Choosing a Tool to Assess a Property of Interest

2.3 Limited Focus or Intent to Generalize

2.4 Controlled or Natural Observations

2.5 Applied Versus Pure Research

2.6 Summary

2.7 Exercises

PART II. DESCRIPTIVE STATISTICS

Organizing and Describing a Set of Observations

Measuring the Variability in a Set of Observations

Describing a Set of Observations in Terms of Their Variability

Chapter 3. The Frequency Distribution Report: Organizing a Set of Observations

3.0 Learning Objectives

3.1 Motivation: Comparing, Sorting, and Counting

3.2 Constructing a Sample Frequency Distribution for a “Qualitative” Property

3.3 Constructing a Sample Frequency Distribution for an “Ordinal” Property

3.4 Some Important Technical Notes

3.5 Summary

3.6 SPSS Tutorial

3.7 Exercises

Chapter 4. The Mode, the Median, and the Mean: Describing a Typical Value of a Quantitative Property Observed for a Set of Phenomena

4.0 Learning Objectives

4.1 Motivation

4.2 A Cautionary Note Regarding Quantitatively Assessed Properties

4.3 Constructing a Sample Frequency Distribution for a Quantitative Property

4.4 Identifying a Typical Phenomenon from a Set of Phenomena

4.5 Assessing and Using the Median of a Set of Observations

4.6 Assessing and Using the Mean of a Set of Observations

4.7 Interpreting and Comparing the Mode, the Median, and the Mean

4.8 Summary

4.9 SPSS Tutorial

4.10 Exercises

Chapter 5. The Variance and the Standard Deviation: Describing the Variability Observed for a Quantitative Property of a Set of Phenomena

5.0 Learning Objectives

5.1 Motivation

5.2 A Case Example: The Frequency Distribution Report

5.3 The Range of a Set of Observations

5.4 The Mean Absolute Difference

5.5 The Variance and the Standard Deviation

5.6 Interpreting the Variance and the Standard Deviation

5.7 Comparing the Mean Absolute Difference and the Standard Deviation

5.8 A Useful Note on Calculating the Variance

5.9 A Note on Modeling and the Assumption of Variability

5.10 Summary

5.11 SPSS Tutorial

5.12 Exercises

5.13 The Method of Moments (Optional)

5.14 A Distribution of “Squared Differences from a Mean” (Optional)

Chapter 6. The z-Transformation and Standardization: Using the Standard Deviation to Compare Observations

6.0 Learning Objectives

6.1 Motivation

6.2 Executing the z-Transformation

6.3 An Example

6.4 Summary

6.5 An Exercise

PART III. STATISTICAL INFERENCE AND PROBABILITY

Why Probability Theory?

The Concept of a Probability

Predicting Events Involving Two Coexisting Properties

Sampling and the Normal Probability Model

Chapter 7. The Concept of a Probability

7.0 Learning Objectives

7.1 Motivation

7.2 Uncertainty, Chance, and Probabilit

7.3 Selection Outcomes and Probabilities

7.4 Events and Probabilities

7.5 Describing a Probability Model for a Quantitative Property

7.6 Summary

7.7 Exercises

Chapter 8. Coexisting Properties and Joint Probability Models

8.0 Learning Objectives

8.1 Motivation

8.2 Probability Models Involving Coexisting Properties

8.3 Models of Association, Conditional Probabilities, and Stochastic Independence

8.4 Covariability in Two Quantitative Properties

8.5 Importance of Stochastic Independence and Covariance in Statistical Inference

8.6 Summary

8.7 Exercises

Chapter 9. Sampling and the Normal Probability Model

9.0 Learning Objectives

9.1 Motivation

9.2 Samples and Sampling

9.3 Bernoulli Trials and the Binomial Distribution

9.4 Representing the Character of a Population

9.5 Predicting Potential Samples from a Known Population

9.6 The Normal Distribution

9.7 The Central Limit Theorem

9.8 Normal Sampling Variability and Statistical Significance

9.9 Summary

9.10 Exercises

PART IV. TOOLS FOR MAKING STATISTICAL INFERENCES

Estimation Studies

Association Studies

Chapter 10. Estimation Studies: Inferring the Parameters of a Population from the Statistics of a Sample

10.0 Learning Objectives

10.1 Motivation

10.2 Estimating the Occurrence of a Qualitative Property for a Population

10.3 Estimating the Occurrences of a Quantitative Property for a Population

10.4 Some Notes on Sampling

10.5 SPSS Tutorial

10.6 Summary

10.7 Exercises

Chapter 11. Chi-Square Analysis: Investigating a Suspected Association Between Two Qualitative Properties

11.0 Learning Objectives

11.1 Motivation

11.2 An Example

11.3 An Extension: Testing the Statistical Significance of Population Proportions

11.4 Summary

11.5 SPSS Tutorial

11.6 Exercises

Chapter 12. The t-Test of Statistical Significance: Comparing a Quantitative Property Assessed for Two Different Groups

12.0 Learning Objectives

12.1 Motivation

12.2 An Example

12.3 Comparing Sample Means Using the Central Limit Theorem (Optional)

12.4 Comparing Sample Means Using the t-Test

12.5 Summary

12.6 SPSS Tutorial

12.7 Exercises

Chapter 13. Analysis of Variance: Comparing a Quantitative Property Assessed for Several Different Groups

13.0 Learning Objectives

13.1 Motivation

13.2 An Example

13.3 The F-Test

13.4 A Note on Sampling Distributions (Optional)

13.5 Summary

13.6 SPSS Tutorial

13.7 Exercises

Chapter 14. Correlation Analysis and Linear Regression: Assessing the Covariability of Two Quantitative Properties

14.0 Learning Objectives

14.1 Motivation

14.2 An Example

14.3 Visual Interpretation with a Scatter Plot (Optional)

14.4 Assessing an Association as a Covariance

14.5 Regression Analysis: Representing a Correlation as a Linear Mathematical Model

14.6 Assessing the Explanatory Value of the Model

14.7 Summary

14.8 SPSS Tutorial

14.9 Exercises

Index