STATISTICS II FOR DUMMIES.

Title Page
Copyright Page
Table of Contents
Introduction
	About This Book
	Foolish Assumptions
	Icons Used in This Book
	Beyond the Book
	Where to Go from Here
Part 1 Tackling Data Analysis and Model-Building Basics
	Chapter 1 Beyond Number Crunching: The Art and Science of Data Analysis
		Data Analysis: Looking before You Crunch
			Nothing (not even a straight line) lasts forever
			Data snooping isn’t cool
			No (data) fishing allowed
		Getting the Big Picture: An Overview of Stats II
			Population parameter
			Sample statistic
			Confidence interval
			Hypothesis test
			Analysis of variance (ANOVA)
			Multiple comparisons
			Interaction effects
			Correlation
			Linear regression
			Chi-square tests
	Chapter 2 Finding the Right Analysis for the Job
		Categorical versus Quantitative Variables
		Statistics for Categorical Variables
			Estimating a proportion
			Comparing proportions
			Looking for relationships between categorical variables
			Building models to make predictions
		Statistics for Quantitative Variables
			Making estimates
			Making comparisons
			Exploring relationships
			Predicting y using x
		Avoiding Bias
		Measuring Precision with Margin of Error
		Knowing Your Limitations
	Chapter 3 Having the Normal and Sampling Distributions in Your Back Pocket
		Recognizing the VIP Distribution — the Normal
			Characterizing the normal
			Standardizing to the standard normal (Z-) distribution
			Using the normal table
			Finding probabilities for the normal distribution
		Finally Getting Comfortable with Sampling Distributions
			The mean and standard error of a sampling distribution
			Sampling distribution of 
			Sampling distribution of 
		Heads Up! Building Confidence Intervals and Hypothesis Tests
			Confidence interval for the population mean
			Confidence interval for the population proportion
			Hypothesis test for population mean
			Hypothesis test for the population proportion
	Chapter 4 Reviewing Confidence Intervals and Hypothesis Tests
		Estimating Parameters by Using Confidence Intervals
			Getting the basics: The general form of a confidence interval
			Finding the confidence interval for a population mean
			What changes the margin of error?
			Interpreting a confidence interval
		What’s the Hype about Hypothesis Tests?
			What Ho and Ha really represent
			Gathering your evidence into a test statistic
			Determining strength of evidence with a p-value
			False alarms and missed opportunities: Type I and II errors
			The power of a hypothesis test
Part 2 Using Different Types of Regression to Make Predictions
	Chapter 5 Getting in Line with Simple Linear Regression
		Exploring Relationships with Scatterplots and Correlations
			Using scatterplots to explore relationships
			Collating the information by using the correlation coefficient
		Building a Simple Linear Regression Model
			Finding the best-fitting line to model your data
			The y-intercept of the regression line
			The slope of the regression line
			Making point estimates by using the regression line
		No Conclusion Left Behind: Tests and Confidence Intervals for Regression
			Scrutinizing the slope
			Inspecting the y-intercept
			Building confidence intervals for the average response
			Making the band with prediction intervals
		Checking the Model’s Fit (The Data, Not the Clothes!)
			Defining the conditions
			Finding and exploring the residuals
			Using r2 to measure model fit
			Scoping for outliers
		Knowing the Limitations of Your Regression Analysis
			Avoiding slipping into cause-and-effect mode
			Extrapolation: The ultimate no-no
			Sometimes you need more than one variable
	Chapter 6 Multiple Regression with Two X Variables
		Getting to Know the Multiple Regression Model
			Discovering the uses of multiple regression
			Looking at the general form of the multiple regression model
			Stepping through the analysis
		Looking at x’s and y’s
		Collecting the Data
		Pinpointing Possible Relationships
			Making scatterplots
			Correlations: Examining the bond
		Checking for Multicolinearity
		Finding the Best-Fitting Model for Two x Variables
			Getting the multiple regression coefficients
			Interpreting the coefficients
			Testing the coefficients
		Predicting y by Using the x Variables
		Checking the Fit of the Multiple Regression Model
			Noting the conditions
			Plotting a plan to check the conditions
			Checking the three conditions
	Chapter 7 How Can I Miss You If You Won’t Leave? Regression Model Selection
		Getting a Kick out of Estimating Punt Distance
			Brainstorming variables and collecting data
			Examining scatterplots and correlations
		Just Like Buying Shoes: The Model Looks Nice, But Does It Fit?
			Assessing the fit of multiple regression models
			Model selection procedures
	Chapter 8 Getting Ahead of the Learning Curve with Nonlinear Regression
		Anticipating Nonlinear Regression
		Starting Out with Scatterplots
		Handling Curves in the Road with Polynomials
			Bringing back polynomials
			Searching for the best polynomial model
			Using a second-degree polynomial to pass the quiz
			Assessing the fit of a polynomial model
			Making predictions
		Going Up? Going Down? Go Exponential!
			Recollecting exponential models
			Searching for the best exponential model
			Spreading secrets at an exponential rate
	Chapter 9 Yes, No, Maybe So: Making Predictions by Using Logistic Regression
		Understanding a Logistic Regression Model
			How is logistic regression different from other regressions?
			Using an S-curve to estimate probabilities
			Interpreting the coefficients of the logistic regression model
			The logistic regression model in action
		Carrying Out a Logistic Regression Analysis
			Running the analysis in Minitab
			Finding the coefficients and making the model
			Estimating p
			Checking the fit of the model
			Fitting the movie model
Part 3 Analyzing Variance with ANOVA
	Chapter 10 Testing Lots of Means? Come On Over to ANOVA!
		Comparing Two Means with a t-Test
		Evaluating More Means with ANOVA
			Spitting seeds: A situation just waiting for ANOVA
			Walking through the steps of ANOVA
		Checking the Conditions
			Verifying independence
			Looking for what’s normal
			Taking note of spread
		Setting Up the Hypotheses
		Doing the F-Test
			Running ANOVA in Minitab
			Breaking down the variance into sums of squares
			Locating those mean sums of squares
			Figuring the F-statistic
			Making conclusions from ANOVA
			What’s next?
		Checking the Fit of the ANOVA Model
	Chapter 11 Sorting Out the Means with Multiple Comparisons
		Following Up after ANOVA
			Comparing cellphone minutes: An example
			Setting the stage for multiple comparison procedures
		Pinpointing Differing Means with Fisher and Tukey
			Fishing for differences with Fisher’s LSD
			Separating the turkeys with Tukey’s test
		Examining the Output to Determine the Analysis
		So Many Other Procedures, So Little Time!
			Controlling for baloney with the Bonferroni adjustment
			Comparing combinations by using Scheffé’s method
			Finding out whodunit with Dunnett’s test
			Staying cool with Student Newman-Keuls
			Duncan’s multiple range test
	Chapter 12 Finding Your Way through Two-Way ANOVA
		Setting Up the Two-Way ANOVA Model
			Determining the treatments
			Stepping through the sums of squares
		Understanding Interaction Effects
			What is interaction, anyway?
			Interacting with interaction plots
		Testing the Terms in Two-Way ANOVA
		Running the Two-Way ANOVA Table
			Interpreting the results: Numbers and graphs
		Are Whites Whiter in Hot Water? Two-Way ANOVA Investigates
	Chapter 13 Regression and ANOVA: Surprise Relatives!
		Seeing Regression through the Eyes of Variation
			Spotting variability and finding an “x-planation”
			Getting results with regression
			Assessing the fit of the regression model
		Regression and ANOVA: A Meeting of the Models
			Comparing sums of squares
			Dividing up the degrees of freedom
			Bringing regression to the ANOVA table
			Relating the F- and t-statistics: The final frontier
Part 4 Building Strong Connections with Chi-Square Tests and Nonparametrics
	Chapter 14 Forming Associations with Two-Way Tables
		Breaking Down a Two-Way Table
			Organizing data into a two-way table
			Filling in the cell counts
			Making marginal totals
		Breaking Down the Probabilities
			Marginal probabilities
			Joint probabilities
			Conditional probabilities
		Trying To Be Independent
			Checking for independence between two categories
			Checking for independence between two variables
		Demystifying Simpson’s Paradox
			Experiencing Simpson’s Paradox
			Figuring out why Simpson’s Paradox occurs
			Keeping one eye open for Simpson’s Paradox
	Chapter 15 Being Independent Enough for the Chi-Square Test
		The Chi-Square Test for Independence
			Collecting and organizing the data
			Determining the hypotheses
			Figuring expected cell counts
			Checking the conditions for the test
			Calculating the Chi-square test statistic
			Finding your results on the Chi-square table
			Drawing your conclusions
			Putting the Chi-square to the test
		Comparing Two Tests for Comparing Two Proportions
	Chapter 16 Using Chi-Square Tests for Goodness-of-Fit (Your Data, Not Your Jeans)
		Finding the Goodness-of-Fit Statistic
			What’s observed versus what’s expected
			Calculating the goodness-of-fit statistic
		Interpreting the Goodness-of-Fit Statistic Using a Chi-Square
			Checking the conditions before you start
			The steps of the Chi-square goodness-of-fit test
	Chapter 17 Rebels Without a Distribution — Nonparametric Procedures
		Arguing for Nonparametric Statistics
			No need to fret if conditions aren’t met
			The median’s in the spotlight for a change
			So, what’s the catch?
		Mastering the Basics of Nonparametric Statistics
			Sign
	Chapter 18 All Signs Point to the Sign Test
		Reading the Signs: The Sign Test
			Testing the median in real estate
			Estimating the median
			Testing matched pairs
Part 5 Putting it All Together: Multi-Stage Analysis of a Large Data Set
	Chapter 19 Conducting a Multi-Stage Analysis of a Large Data Set
		Steps Involved in Working with a Large Data Set
		Wrangling Data
			Discovery
			Structuring
			Cleaning
			Enriching
			Validating
			Publishing
		Visualizing Data
		Exploring the Data
		Looking for Relationships
		Building Models and Making Inferences
		Sharing the Story
			Who is the audience?
			Make an outline
			Include an executive summary
			Check your writing
	Chapter 20 A Statistician Watches the Movies
		Examining the Movie Variables and Asking Questions
		Visualizing the Movie Data
			Categorical movie variables
			Quantitative movie variables
		Doing Descriptive Dirty Work
		Looking for Relationships
			Relationships between quantitative movie variables
			Relationships between two categorical variables
			Relationships between quantitative and categorical variables
		Building a Model for Predicting U.S. Revenue
		Writing It Up
	Chapter 21 Looking Inside the Refrigerator
		Refrigerator Data — The Variables
		Exploring the Data
		Analyzing the Data
		Writing It Up
Part 6 The Part of Tens
	Chapter 22 Ten Common Errors in Statistical Conclusions
		Claiming These Statistics Prove . . .
		It’s Not Technically Statistically Significant, But . . .
		Concluding That x Causes y
		Assuming the Data Was Normal
		Only Reporting “Important” Results
		Assuming a Bigger Sample Is Always Better
		It’s Not Technically Random, But . . .
		Assuming That 1,000 Responses Is 1,000 Responses
		Of Course the Results Apply to the General Population
		Deciding Just to Leave It Out
	Chapter 23 Ten Ways to Get Ahead by Knowing Statistics
		Asking the Right Questions
		Being Skeptical
		Collecting and Analyzing Data Correctly
		Calling for Help
		Retracing Someone Else’s Steps
		Putting the Pieces Together
		Checking Your Answers
		Explaining the Output
		Making Convincing Recommendations
		Establishing Yourself as the Statistics Go-To Person
	Chapter 24 Ten Cool Jobs That Use Statistics
		Pollster
		Data Scientist
		Ornithologist (Bird Watcher)
		Sportscaster or Sportswriter
		Journalist
		Crime Fighter
		Medical Professional
		Marketing Executive
		Lawyer
Appendix Reference Tables
Index
EULA