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| Introduction | |
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| About This Book | |
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| Conventions Used in This Book | |
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| What You're Not to Read | |
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| Foolish Assumptions | |
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| How This Book Is Organized | |
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| Tackling Data Analysis and Model-Building Basics | |
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| Using Different Types of Regression to Make Predictions | |
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| Analyzing Variance with ANOVA | |
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| Building Strong Connections with Chi-Square Tests | |
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| Nonparametric Statistics: Rebels without a Distribution | |
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| The Part of Tens | |
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| Icons Used in This Book | |
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| Where to Go from Here | |
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| Tackling Data Analysis and Model-Building Basics | |
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| Beyond Number Crunching: The Art and Science of Data Analysis | |
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| Data Analysis: Looking before You Crunch | |
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| Nothing (not even a Straight line) lasts forever | |
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| Data snooping isn't cool | |
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| No (data) fishing allowed | |
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| Getting the Big Picture: An Overview of Stats II | |
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| Population parameter | |
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| Sample statistic | |
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| Confidence interval | |
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| Hypothesis test | |
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| Analysis of variance (ANOVA) | |
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| Multiple comparisons | |
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| Interaction effects | |
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| Correlation | |
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| Linear regression | |
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| Chi-square tests | |
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| Nonparametrics | |
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| Finding the Right Analysis for the Job | |
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| Categorical versus Quantitative Variables | |
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| Statistics for Categorical Variables | |
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| Estimating a proportion | |
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| Comparing proportions | |
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| Looking for relationships between categorical variables | |
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| Building models to make predictions | |
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| Statistics for Quantitative Variables | |
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| Making estimates | |
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| Making comparisons | |
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| Exploring relationships | |
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| Predicting y using x | |
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| Avoiding Bias | |
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| Measuring Precision with Margin of Error | |
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| Knowing Your Limitations | |
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| Reviewing Confidence Intervals and Hypothesis Tests | |
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| Estimating Parameters by Using Confidence Intervals | |
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| Getting the basics: The general form of a confidence interval | |
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| Finding the confidence interval for a population mean | |
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| What changes the margin of error? | |
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| Interpreting a confidence interval | |
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| What's the Hype about Hypothesis Tests? | |
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| What Ho and Ha really represent | |
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| Gathering your evidence into a test statistic | |
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| Determining strength of evidence with a p-value | |
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| False alarms and missed opportunities: Type I and II errors | |
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| The power of a hypothesis test | |
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| Using Different Types of Regression to Make Predictions | |
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| Getting in Line with Simple Linear Regression | |
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| Exploring Relationships with Scatterplots and Correlations | |
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| Using scatterplots to explore relationships | |
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| Collating the information by using the correlation coefficient | |
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| Building a Simple Linear Regression Model | |
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| Finding the best-fitting line to model your data | |
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| The y-intercept of the regression line | |
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| The slope of the regression line | |
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| Making point estimates by using the regression line | |
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| No Conclusion Left Behind: Tests and Confidence Intervals for Regression | |
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| Scrutinizing the slope | |
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| Inspecting the y-intercept | |
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| Building confidence intervals for the average response | |
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| Making the band with prediction intervals | |
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| Checking the Model's Fit (The Data, Not the Clothes!) | |
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| Defining the conditions | |
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| Finding and exploring the residuals | |
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| Using r2 to measure model fit | |
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| Scoping for outliers | |
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| Knowing the Limitations of Your Regression Analysis | |
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| Avoiding slipping into cause-and-effect mode | |
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| Extrapolation: The ultimate no-no | |
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| Sometimes you need more than one variable | |
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| Multiple Regression with Two X Variables | |
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| Getting to Know the Multiple Regression Model | |
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| Discovering the uses of multiple regression | |
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| Looking at the general form of the multiple regression model | |
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| Stepping through the analysis | |
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| Looking at x's and y's | |
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| Collecting the Data | |
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| Pinpointing Possible Relationships | |
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| Making scatterplots | |
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| Correlations: Examining the bond | |
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| Checking for Multicolinearity | |
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| Finding the Best-Fitting Model for Two x Variables | |
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| Getting the multiple regression coefficients | |
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| Interpreting the coefficients | |
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| Testing the coefficients | |
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| Predicting y by Using the x Variables | |
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| Checking the Fit of the Multiple Regression Model | |
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| Noting the conditions | |
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| Plotting a plan to check the conditions | |
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| Checking the three conditions | |
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| How Can I Miss You If You Won't Leave? Regression Model Selection | |
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| Getting a Kick out of Estimating Punt Distance | |
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| Brainstorming variables and collecting data | |
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| Examining scatterplots and correlations | |
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| Just Like Buying Shoes: The Model Looks Nice, But Does It Fit? | |
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| Assessing the fit of multiple regression models | |
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| Model selection procedures | |
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| Getting Ahead of the Learning Curve with Nonlinear Regression | |
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| Anticipating Nonlinear Regression | |
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| Starting Out with Scatterplots | |
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| Handling Curves in the Road with Polynomials | |
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| Bringing back Polynomials | |
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| Searching for the best polynomial model | |
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| Using a second-degree polynomial to pass the quiz | |
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| Assessing the fit of a polynomial model | |
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| Making predictions | |
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| Going Up? Going Down? Go Exponential! | |
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| Recollecting exponential models | |
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| Searching for the best exponential model | |
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| Spreading secrets at an exponential rate | |
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| Yes, No, Maybe So: Making Predictions by Using Logistic Regression | |
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| Understanding a Logistic Regression Model | |
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| How is logistic regression different from other regressions? | |
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| Using an S-curve to estimate probabilities | |
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| Interpreting the coefficients of the logistic regression model | |
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| The logistic regression model in action | |
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| Carrying Out a Logistic Regression Analysis | |
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| Running the analysis in Minitab | |
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| Finding the coefficients and making the model | |
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| Estimating p | |
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| Checking the fit of the model | |
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| Fitting the Movie Model | |
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| Analyzing Variance with Anova | |
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| Testing Lots of Means? Come On Over to Anova! | |
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| Comparing Two Means with a t-Test | |
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| Evaluating More Means with Anova | |
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| Spitting seeds: A situation just waiting for Anova | |
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| Walking through the steps of Anova | |
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| Checking the Conditions | |
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| Verifying independence | |
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| Looking for what's normal | |
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| Taking note of spread | |
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| Setting Up the Hypotheses | |
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| Doing the F-Test | |
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| Running Anova in Minitab | |
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| Breaking down the variance into sums of squares | |
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| Locating those mean sums of squares | |
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| Figuring the F-statistic | |
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| Making conclusions from Anova | |
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| What's next? | |
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| Checking the Fit of the Anova Model | |
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| Sorting Out the Means with Multiple Comparisons | |
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| Following Up after Anova | |
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| Comparing cellphone minutes: An example | |
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| Setting the Stage for multiple comparison procedures | |
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| Pinpointing Differing Means with Fisher and Tukey | |
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| Fishing for differences with Fisher's LSD | |
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| Using Fisher's new and improved LSD | |
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| Separating the turkeys with Tukey's test | |
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| Examining the Output to Determine the Analysis | |
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| So Many Other Procedures, So Little Time! | |
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| Controlling for baloney with the Bonferroni adjustment | |
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| Comparing combinations by using Scheffe's method | |
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| Finding out whodunit with Dunnett's test | |
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| Staying cool with Student Newman-Keuls | |
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| Duncan's multiple range test | |
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| Going nonparametric with the Kruskal-Wallis test | |
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| Finding Your Way through Two-Way Anova | |
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| Setting Up the Two-Way Anova Model | |
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| Determining the treatments | |
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| Stepping through the sums of squares | |
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| Understanding Interaction Effects | |
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| What is interaction, anyway? | |
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| Interacting with interaction plots | |
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| Testing the Terms in Two-Way Anova | |
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| Running the Two-Way Anova Table | |
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| Interpreting the results: Numbers and graphs | |
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| Are Whites Whiter in Hot Water? Two-Way Anova Investigates | |
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| Regression and Anova: Surprise Relatives! | |
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| Seeing Regression through the Eyes of Variation | |
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| Spotting variability and finding an ""x-planation"" | |
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| Getting results with regression | |
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| Assessing the fit of the regression model | |
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| Regression and Anova: A Meeting of the Models | |
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| Comparing sums of squares | |
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| Dividing up the degrees of freedom | |
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| Bringing regression to the Anova table | |
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| Relating the F-and t-statistics: The final frontier | |
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| Building Strong Connections with Chi-Square Tests | |
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| Forming Associations with Two-Way Tables | |
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| Breaking Down a Two-Way Table | |
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| Organizing data into a two-way table | |
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| Filling in the cell counts | |
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| Making marginal totals | |
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| Breaking Down the Probabilities | |
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| Marginal probabilities | |
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| Joint probabilities | |
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| Conditional probabilities | |
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| Trying To Be Independent | |
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| Checking for independence between two categories | |
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| Checking for independence between two variables | |
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| Demystifying Simpson's Paradox | |
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| Experiencing Simpson's Paradox | |
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| Figuring out why Simpson's Paradox occurs | |
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| Keeping one eye open for Simpson's Paradox | |
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| Being Independent Enough for the Chi-Square Test | |
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| The Chi-square Test for Independence | |
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| Collecting and organizing the data | |
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| Determining the hypotheses | |
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| Figuring expected cell counts | |
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| Checking the conditions for the test | |
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| Calculating the Chi-square test statistic | |
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| Finding your results on the Chi-square table | |
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| Drawing your conclusions | |
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| Putting the Chi-square to the test | |
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| Comparing Two Tests for Comparing Two Proportions | |
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| Getting reacquainted with the Z-test for two population proportions | |
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| Equating Chi-square tests and Z-tests for a two-by-two table | |
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| Using Chi-Square Tests for Goodness-of-Fit (Your Data, Not Your Jeans) | |
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| Finding the Goodness-of-Fit Statistic | |
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| What's observed versus what's expected | |
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| Calculating the goodness-of-fit statistic | |
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| Interpreting the Goodness-of-Fit Statistic Using a Chi-Square | |
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| Checking the conditions before you start | |
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| The steps of the Chi-square goodness-of-fit test | |
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| Nonparametric Statistics: Rebels without a Distribution | |
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| Going Nonparametric | |
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| Arguing for Nonparametric Statistics | |
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| No need to fret if conditions aren't met | |
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| The median's in the spotlight for a change | |
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| So, what's the catch? | |
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| Mastering the Basics of Nonparametric Statistics | |
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| Sign | |
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| Rank | |
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| Signed rank | |
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| Rank sum | |
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| All Signs Point to the Sign test and Signed Rank Test | |
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| Reading the Signs: The Sign Test | |
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| Testing the median | |
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| Estimating the median | |
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| Testing matched pairs | |
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| Going a Step Further with the Signed Rank Test | |
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| A limitation of the sign test | |
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| Stepping through the signed rank test | |
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| Losing weight with signed ranks | |
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| Pulling Rank with the Rank Sum Test | |
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| Conducting the Rank Sum Test | |
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| Checking the conditions | |
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| Stepping through the test | |
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| Stepping up the sample size | |
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| Performing a Rank Sum Test: Which Real Estate Agent Sells Homes Faster? | |
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| Checking the conditions for this test | |
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| Testing the hypotheses | |
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| Do the Kruskal-Wallis and Rank the Sums with the Wilcoxon | |
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| Doing the Kruskal-Wallis Test to Compare More than Two Populations | |
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| Checking the conditions | |
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| Setting up the test | |
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| Conducting the test step by step | |
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| Pinpointing the Differences: The Wilcoxon Rank Sum Test | |
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| Pairing off with pariwise comparisons | |
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| Carrying out comparison tests to see who's different | |
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| Examining the medians to see how they're different | |
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| Pointing Out Correlations with Spearman's Rank | |
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| Pickin' On Pearson and His Precious Conditions | |
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| Scoring with Spearman's Rank Correlation | |
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| Figuring Spearman's rank correlation | |
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| Watching Spearman at work: Relating aptitude to performance | |
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| The Part of Tens | |
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| Ten Common Errors in Statistical Conclusions | |
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| Ten Ways to Get Ahead by Knowing Statistics | |
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| Ten Cool Jobs That Use Statistics | |
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| Appendix: Reference Tables | |
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| Index | |