Statistics II for Dummies

ISBN-10: 0470466464

ISBN-13: 9780470466469

Edition: 2009

List price: $14.99 Buy it from $8.10
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Description: The ideal supplement and study guide for students preparing for advanced statisticsPacked with fresh and practical examples appropriate for a range of degree-seeking students, Statistics II For Dummies helps any reader succeed in an upper-level statistics course. It picks up with data analysis where Statistics For Dummies left off, featuring new and updated examples, real-world applications, and test-taking strategies for success. This easy-to-understand guide covers such key topics as sorting and testing models, using regression to make predictions, performing variance analysis (ANOVA), drawing test conclusions with chi-squares, and making comparisons with the Rank Sum Test.Deborah Rumsey, PhD, is a Statistics Education Specialist and Auxiliary Faculty Member in the Department of Statistics at The Ohio State University. She is the author of Statistics For Dummies (978-0-7645-5423-0), Statistics Workbook For Dummies (978-0-7645-8466-4), and Probability For Dummies (978-0-471-75141-0).

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Book details

List price: $14.99
Copyright year: 2009
Publisher: John Wiley & Sons, Limited
Publication date: 9/1/2009
Binding: Paperback
Pages: 408
Size: 7.25" wide x 9.25" long x 1.00" tall
Weight: 1.408

About This Book
Conventions Used in This Book
What You're Not to Read
Foolish Assumptions
How This Book Is Organized
Tackling Data Analysis and Model-Building Basics
Using Different Types of Regression to Make Predictions
Analyzing Variance with ANOVA
Building Strong Connections with Chi-Square Tests
Nonparametric Statistics: Rebels without a Distribution
The Part of Tens
Icons Used in This Book
Where to Go from Here
Tackling Data Analysis and Model-Building Basics
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
Linear regression
Chi-square tests
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
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
Using Different Types of Regression to Make Predictions
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
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
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
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
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
Analyzing Variance with Anova
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
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
Using Fisher's new and improved 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 Scheffe's method
Finding out whodunit with Dunnett's test
Staying cool with Student Newman-Keuls
Duncan's multiple range test
Going nonparametric with the Kruskal-Wallis test
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
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
Building Strong Connections with Chi-Square Tests
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
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
Getting reacquainted with the Z-test for two population proportions
Equating Chi-square tests and Z-tests for a two-by-two table
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
Nonparametric Statistics: Rebels without a Distribution
Going Nonparametric
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
Signed rank
Rank sum
All Signs Point to the Sign test and Signed Rank Test
Reading the Signs: The Sign Test
Testing the median
Estimating the median
Testing matched pairs
Going a Step Further with the Signed Rank Test
A limitation of the sign test
Stepping through the signed rank test
Losing weight with signed ranks
Pulling Rank with the Rank Sum Test
Conducting the Rank Sum Test
Checking the conditions
Stepping through the test
Stepping up the sample size
Performing a Rank Sum Test: Which Real Estate Agent Sells Homes Faster?
Checking the conditions for this test
Testing the hypotheses
Do the Kruskal-Wallis and Rank the Sums with the Wilcoxon
Doing the Kruskal-Wallis Test to Compare More than Two Populations
Checking the conditions
Setting up the test
Conducting the test step by step
Pinpointing the Differences: The Wilcoxon Rank Sum Test
Pairing off with pariwise comparisons
Carrying out comparison tests to see who's different
Examining the medians to see how they're different
Pointing Out Correlations with Spearman's Rank
Pickin' On Pearson and His Precious Conditions
Scoring with Spearman's Rank Correlation
Figuring Spearman's rank correlation
Watching Spearman at work: Relating aptitude to performance
The Part of Tens
Ten Common Errors in Statistical Conclusions
Ten Ways to Get Ahead by Knowing Statistics
Ten Cool Jobs That Use Statistics
Appendix: Reference Tables
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