# Essential Statistics for Public Managers and Policy Analysts / Edition 1

In this text for students, public managers, and policy analysts, Berman (public administration, U. of Central Florida) illustrates the principles of statistics by applying them to familiar public issues. Topics include, for example, univariate analysis, hypothesis testing with chi-square, ANOVA assumptions, regression, and path analysis. All but the most basic of

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## Overview

In this text for students, public managers, and policy analysts, Berman (public administration, U. of Central Florida) illustrates the principles of statistics by applying them to familiar public issues. Topics include, for example, univariate analysis, hypothesis testing with chi-square, ANOVA assumptions, regression, and path analysis. All but the most basic of the calculations presented in the text are intended to be performed using statistics software. Annotation c. Book News, Inc., Portland, OR (booknews.com)

## Product Details

ISBN-13:
9781568026473
Publisher:
Congressional Quarterly, Inc.
Publication date:
09/01/2001
Edition description:
Older Edition
Pages:
195

## Related Subjects

 Preface xv Statistics Roadmap xx Introduction xxiii Chapter 1 Why Research? An Introduction 1 Chapter Objectives 1 Research Design 3 Six Steps 3 Relationships 5 Rival Hypotheses and Limitations of Experimental Study Designs 8 Measurement and Sampling 11 Measuring Concepts 11 Measuring Variables: Levels and Scales 12 Measuring Variables: Sampling 16 Data Collection 18 Administrative Data 18 Surveys 19 Other Data Sources 20 Putting It Together 21 Conclusion 23 Key Terms 24 Notes 24 Chapter 2 Univariate Analysis: Description 27 Chapter Objectives 27 Measures of Central Tendency 29 The Mean 29 The Median 30 The Mode 32 Using Grouped Data 33 Measures of Dispersion 35 Boxplots 35 Frequency Distributions 38 Standard Deviation 40 Conclusion 45 Key Terms 45 Notes 46 Chapter 3 Hypothesis Testing With Chi-Square 49 Chapter Objectives 49 Contingency Tables 50 Chi-Square 51 Hypothesis Testing 53 The Null Hypothesis 55 Statistical Significance 56 The Five Steps of Hypothesis Testing 57 Chi-Square Test Assumptions 59 Statistical Significance and Sample Size 60 A Useful Digression: The Goodness-of-Fit Test 62 The Practical Significance of Relationships 64 Rival Hypotheses: Adding a Control Variable 66 Conclusion 67 Key Terms 69 Notes 69 Chapter 4 Measures of Association 71 Chapter Objectives 71 Proportional Reduction in Error 72 Calculating PRE 72 Paired Cases 73 Statistics for Two Nominal Variables 74 Two Nominal Variables 75 The Problem of Dependent Samples 77 Small Sample Tests for Two-By-Two Tables 78 Statistics for Mixed Ordinal-Nominal Data 80 Evaluating Rankings 80 Equivalency of Two Samples 83 Statistics for Two Ordinal Variables 84 Conclusion 87 Key Terms 88 Notes 88 Chapter 5 T-Tests and Anova 93 Chapter Objectives 93 Creating Index Variables 94 T-Tests 96 T-Test Assumptions 98 A Working Example 101 Analysis of Variance 104 ANOVA Assumptions 108 A Working Example 108 Conclusion 111 Key Terms 112 Notes 113 Chapter 6 Regression I: Estimation 117 Chapter Objectives 117 Simple Regression 118 Scatterplot 119 Test of Significance 119 Goodness of Fit 121 Assumptions and Notation 123 Multiple Regression 124 Model Specification 124 A Working Example 126 Goodness of Fit for Multiple Regression 128 Standardized Coefficients 128 F-Test 129 Use of Nominal Variables 129 Conclusion 131 Key Terms 132 Notes 132 Chapter 7 Regression II: Assumptions, Time Series 135 Chapter Objectives 135 Testing Assumptions 136 Outliers 136 Multicollinearity 137 Linearity 139 Heteroscedasticity 140 Measurement and Specification 142 Time Series Analysis 145 Detecting Autocorrelation 145 Correcting Autocorrelation 146 Policy Evaluation 148 Lagged Variables 150 Forecasting 151 Forecasting with Few Observations 152 Forecasting with Periodic Effects 155 Conclusion 157 Key Terms 157 Notes 158 Chapter 8 Advanced Statistics 159 Chapter Objectives 159 Logistic Regression 160 Path Analysis 163 Survival Analysis 166 Regression-Based Forecasting 167 Forecasting with Leading Indicators 168 Curve Estimation 168 Exponential Smoothing 169 ARIMA 171 Precis of Other Techniques 171 Beyond Logistic Regression 172 Exploratory Analysis 172 Beyond Life Tables 173 Beyond One-Way ANOVA 173 Beyond Path Analysis 174 Conclusion 174 Key Terms 175 Notes 175 Appendix Statistical Tables 177 Normal Distribution 178 Chi-square Distribution 179 T-test Distribution 180 F-test Distribution 181 Durbin-Watson Distribution 185