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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 | |