Statistics II For Dummies, 2nd Edition
- Length: 448 pages
- Edition: 2
- Language: English
- Publisher: For Dummies
- Publication Date: 2021-11-09
- ISBN-10: 1119827396
- ISBN-13: 9781119827399
- Sales Rank: #4363953 (See Top 100 Books)
Continue your statistics journey with this all-encompassing reference
Completed Statistics through standard deviations, confidence intervals, and hypothesis testing? Then you’re ready for the next step: Statistics II. And there’s no better way to tackle this challenging subject than with Statistics II For Dummies! Get a brief overview of Statistics I in case you need to brush up on earlier topics, and then dive into a full explanation of all Statistic II concepts, including multiple regression, analysis of variance (ANOVA), Chi-square tests, nonparametric procedures, and analyzing large data sets. By the end of the book, you’ll know how to use all the statistics tools together to create a great story about your data.
For each Statistics II technique in the book, you get an overview of when and why it’s used, how to know when you need it, step-by-step directions on how to do it, and tips and tricks for working through the solution. You also find:
- What makes each technique distinct and what the results say
- How to apply techniques in real life
- An interpretation of the computer output for data analysis purposes
- Instructions for using Minitab to work through many of the calculations
- Practice with a lot of examples
With Statistics II For Dummies, you will find even more techniques to analyze a set of data. Get a head start on your Statistics II class, or use this in conjunction with your textbook to help you thrive in statistics!
Cover Title Page Copyright 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 Getting the Big Picture: An Overview of Stats II Chapter 2: Finding the Right Analysis for the Job Categorical versus Quantitative Variables Statistics for Categorical Variables Statistics for Quantitative Variables 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 Finally Getting Comfortable with Sampling Distributions Heads Up! Building Confidence Intervals and Hypothesis Tests Chapter 4: Reviewing Confidence Intervals and Hypothesis Tests Estimating Parameters by Using Confidence Intervals What’s the Hype about Hypothesis Tests? 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 Building a Simple Linear Regression Model No Conclusion Left Behind: Tests and Confidence Intervals for Regression Checking the Model’s Fit (The Data, Not the Clothes!) Knowing the Limitations of Your Regression Analysis Chapter 6: Multiple Regression with Two X Variables Getting to Know the Multiple Regression Model Looking at x’s and y’s Collecting the Data Pinpointing Possible Relationships Checking for Multicolinearity Finding the Best-Fitting Model for Two x Variables Predicting y by Using the x Variables Checking the Fit of the Multiple Regression Model Chapter 7: How Can I Miss You If You Won’t Leave? Regression Model Selection Getting a Kick out of Estimating Punt Distance Just Like Buying Shoes: The Model Looks Nice, But Does It Fit? 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 Going Up? Going Down? Go Exponential! Chapter 9: Yes, No, Maybe So: Making Predictions by Using Logistic Regression Understanding a Logistic Regression Model Carrying Out a Logistic Regression Analysis 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 Checking the Conditions Setting Up the Hypotheses Doing the F-Test Checking the Fit of the ANOVA Model Chapter 11: Sorting Out the Means with Multiple Comparisons Following Up after ANOVA Pinpointing Differing Means with Fisher and Tukey Examining the Output to Determine the Analysis So Many Other Procedures, So Little Time! Chapter 12: Finding Your Way through Two-Way ANOVA Setting Up the Two-Way ANOVA Model Understanding Interaction Effects Testing the Terms in Two-Way ANOVA Running the Two-Way ANOVA Table Are Whites Whiter in Hot Water? Two-Way ANOVA Investigates Chapter 13: Regression and ANOVA: Surprise Relatives! Seeing Regression through the Eyes of Variation Regression and ANOVA: A Meeting of the Models 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 Breaking Down the Probabilities Trying To Be Independent Demystifying Simpson’s Paradox Chapter 15: Being Independent Enough for the Chi-Square Test The Chi-Square Test for Independence 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 Interpreting the Goodness-of-Fit Statistic Using a Chi-Square Chapter 17: Rebels Without a Distribution — Nonparametric Procedures Arguing for Nonparametric Statistics Mastering the Basics of Nonparametric Statistics Chapter 18: All Signs Point to the Sign Test Reading the Signs: The Sign Test 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 Visualizing Data Exploring the Data Looking for Relationships Building Models and Making Inferences Sharing the Story Chapter 20: A Statistician Watches the Movies Examining the Movie Variables and Asking Questions Visualizing the Movie Data Doing Descriptive Dirty Work Looking for Relationships 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 A: Reference Tables t-Table Binomial Table Chi-Square Table F-Table Z-Table Index About the Author Advertisement Page Connect with Dummies End User License Agreement
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