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This practical text provides students with the statistical tools needed to analyze data, and shows how statistics can be used as a tool in making informed, intelligent policy decisions. The authors' approach helps students learn what statistical measures mean and focus on interpreting results, as opposed to memorizing and applying dozens of statistical formulae. The book includes more than 500 end-of-chapter problems, solvable with the easy-to-use Excel spreadsheet application developed by the authors. This template allows students to enter numbers into the appropriate sheet, sit back, and analyze the data. This comprehensive, hands-on textbook requires only a background in high school algebra and has been thoroughly classroom-tested in both undergraduate and graduate level courses. No prior expertise with Excel is required. A disk with the Excel template and the data sets is included with the book, and solutions to the end-of-chapter problems will be provided on the M.E. Sharpe website.

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• Unit I Descriptive Statistics
• 1 Introduction: What Is Statistics All About?
• understanding the General Field of Statistics
• Use of Statistics
• Understanding Research Problems and Variables
• Developing Scales of Measurement
• Summary
• Statistical Applications
• Exercises
• Bibliography
• 2 Using Polystat to do Statistical Analysis
• Introduction
• What is Polystat?
• General Directions for Using Polystat
• Bibliography
• 3 Presentation of Data
• Introduction
• Generating a Tabular Analysis
• How to Conduct Graphical Analysis
• Using Excel to Conduct Tabular and Graphical Analysis
• Statistical Applications
• Exercises
• Bibliography
• 4 Summarizing Data and Using Descriptive Statistics
• Introduction
• Measures of Central Tendency: The Median
• The Weighted Mean
• Measures of Central Tendency: The Median
• Other Measures of Central Tendency
• Measures of Dispersion: Range and Standard Deviation
• Interpreting the Standard Deviation
• How to Calculate Descriptive Statistics When You Do Not Have the Actual Data
• Using Polystat to Calculate Descriptive Statistics
• Statistical Applications
• Exercises
• Bibliography
• Unit II Basic Probability and Probability Distributions
• 5 Basic Probability: Theory and Applications
• Why Probability Is Important When Using Statistics?
• Understanding the Basic Laws of Probability
• Basic Law of Probability: Understanding the Classic Approach
• Another Basic Law of Probability: Relative Frequency Approach
• The Subjective Approach to Probability
• Some Fundamental Rules of Probability
• Other Fundamental Rules of Probability (Optional Material)
• Statistical Applications
• Exercises
• Bibliography
• 6 Sampling and the Normal Distribution
• Introduction
• What Is a Basic Probability Distribution?
• Understanding the Significance of the Bell-Shaped or Normal Curve
• How to Calculate a Probability for a Normal Distribution in Polystat
• How to Solve a Binomial Probability Using the Normal Distribution in Polystat
• The Poisson Distribution (Optional Material)
• Statistical Applications
• Exercises
• Bibliography
• 7 The Central Limit Theorem
• Introduction to the Central Limit Theorem
• What Do we Mean When We say Sampling Error?
• So What Is the Central Limit Theorem?
• Calculating Probabilities Under a Normal Curve Using the Central Limit Theorem
• Exercises
• Bibliography
• Unit III Hypothesis Testing
• 8 Introduction to Inferential Statistics
• Introduction to Inferential Statistics
• What Is Inferential Statistics?
• Key Concepts of Inferential Statistics
• What is Inferential Statistics and Hypothesis Testing?
• An Illustration of Hypothesis Testing
• Concluding Remarks About Hypothesis Testing
• Exercises
• Bibliography
• 9 Estimating Means, Proportions, and Sample Size with Confidence
• What Is a Confidence Interval?
• Confidence Intervals for Population Means
• Confidence Intervals for Population Proportions
• Understanding the Usefulness of a Confidence Interval
• Confidence Intervals in Opinion Research
• What Happens When You Have a Small Sample (a.k.a. the t-distribution)?
• Creating Confidence Intervals Using Polystat
• What Happens When You Have a Small Sample (aka the t-Distribution)?
• Determining an Adequate Sample Size Means
• tHow to Determine an Adequate Sample Size for a Proportion
• Estimating Adequate Sample Sizes Using Polystat
• Statistical Applications
• Exercises
• Bibliography
• Introduction
• Statistical Inferences with One Sample
• Testing a Population Mean Using a Sample Mean: Two-Tail Test for Large Samples
• Testing a Population Mean Using a Sample Mean: One-Tail Tests for Large Samples
• Using Polystat to Do One-Tail and Two-Tail Analysis of Measurement
• Testing a Single Population Mean: One-Tail and Two-Tail Tests with Small Samples
• Testing a Single Population Mean--One-Tail and Two-Tail Tests with Small Samples
• Statistical Applications
• Problems and Exercises
• Exercises
• Bibliography
• Introduction
• Hypothesis Tests Between Two Population Means
• Testing Differences for Two Population mean (Standard Deviations Are Known)
• Testing Differences for Two Population Means (Standard Deviations Are Unknown)
• Using Polystat to Test Differences Between Population Means
• Statistical Applications
• Problems and Exercises
• Exercises
• Bibliography
• Introduction
• Understanding Proportion Problems
• Hypothesis Test of a Single Proportion
• Using Polystat to Test a Hypothesis about a Single Population Proportion
• Problems and Exercises
• Exercises
• Bibliography
• Introduction
• Understanding Hypothesis Testing for Two Proportions
• Hypothesis Test for Difference Between Proportions
• Using Polystat to Test a Hypothesis Comparing Population Proportions
• Statistical Applications
• Problems and Exercises
• Exercises
• Bibliography
• 14 Comparing More than Two Population Means with Anova
• Introduction
• What Is Analysis of Variance (Anova)?
• Assumptions Needed for Analysis of Variance
• The One-Way Analysis of Variance Model
• The One-Way Anova Model
• A Practical Application of One-Way Anova
• The Two-Way Analysis of Variance Model
• The Two-way Anova (Optional Material)
• Exercises
• Bibliography
• 15 Comparing More than Two Proportions Using the Chi-Square Test
• Cross Tabulations and Frequency Distributions
• Testing for Differences Among Three or More Proportions
• Using Polystat Chi-Square to Test a Hypothesis Among Three or More Proportions
• Some Practical Applications of Chi-Square
• Limitations of the Chi-Square Test
• Problems and Exercises
• Exercises
• Bibliography
• 16 Determining Relationships for Two Variables Using Correlation
• Measures of Correlation
• Scatter Plots and Positive or Negative Relationships
• How to Calculate the Correlation Coefficient (r) Between Two Variables
• How to Calculate the Correlation Coefficient (r) Between Two Variables
• The Coefficient of Determination (r2)
• Testing the Correlation Coefficient (r) for Statistical Significance
• Some Practical Examples of Positive and Negative Relationships
• Some Words of Caution
• Problems and Exercises
• Exercises
• Bibliography
• 17 Measuring Relationships with Simple Regression Analysis
• What is Regression Analysis?
• What is Regression Analysis and How Does It Differ from Correlation?
• Making Predictions Using Regression Analysis
• Levels of Statistical Significance
• Problems and Exercises
• References
• 18 Measuring Multivariate Relationships with Multiple Regression Analysis
• Introduction
• What Is Multiple Regression?
• Using Polystat to Estimate Multiple Regression Statistics
• Exercises
• Bibliography
• 19 Planning Statistical Research
• Introduction
• Experimental Design
• Validity of Research
• Sampling Design
• Sampling Techniques
• Sampling Error
• Exercises
• Bibliography
• Appendix
• Area Under the Normal Curve
• Critical Values of t-Distribution
• Critical Values of F-Statistic
• Critical Values of Chi-Square Statistic
• Key Formulas
• Index
• About the Author