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!

Autorentext

Deborah J. Rumsey, PhD, is a Statistics Education Specialist and Associated Professor in the Department of Statistics at The Ohio State University. She is also a Fellow of the American Statistical Association and has received the Presidential Teaching Award from Kansas State University. Dr. Rumsey has published numerous papers and given many professional presentations on the subject of statistics education.

Klappentext

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!

Inhalt

Introduction 1

About This Book 1

Foolish Assumptions 3

Icons Used in This Book 3

Beyond the Book 4

Where to Go from Here 4

Part 1: Tackling Data Analysis and Model-Building Basics 7

Chapter 1: Beyond Number Crunching: The Art and Science of Data Analysis 9

Data Analysis: Looking before You Crunch 9

Nothing (not even a straight line) lasts forever 10

Data snooping isn't cool 11

No (data) fishing allowed 12

Getting the Big Picture: An Overview of Stats II 13

Population parameter 13

Sample statistic 13

Confidence interval 14

Hypothesis test 14

Analysis of variance (ANOVA) 15

Multiple comparisons 15

Interaction effects 16

Correlation 16

Linear regression 17

Chi-square tests 18

Chapter 2: Finding the Right Analysis for the Job 21

Categorical versus Quantitative Variables 22

Statistics for Categorical Variables 23

Estimating a proportion 23

Comparing proportions 24

Looking for relationships between categorical variables 25

Building models to make predictions 26

Statistics for Quantitative Variables 27

Making estimates 27

Making comparisons 28

Exploring relationships 28

Predicting y using x 30

Avoiding Bias 31

Measuring Precision with Margin of Error 33

Knowing Your Limitations 35

Chapter 3: Having the Normal and Sampling Distributions in Your Back Pocket 37

Recognizing the VIP Distribution - the Normal 38

Characterizing the normal 38

Standardizing to the standard normal (Z-) distribution 38

Using the normal table 40

Finding probabilities for the normal distribution 41

Finally Getting Comfortable with Sampling Distributions 42

The mean and standard error of a sampling distribution 42

Sampling distribution of X 43

Sampling distribution of p 44

Heads Up! Building Confidence Intervals and Hypothesis Tests 45

Confidence interval for the population mean 45

Confidence interval for the population proportion 46

Hypothesis test for population mean 46

Hypothesis test for the population proportion 47

Chapter 4: Reviewing Confidence Intervals and Hypothesis Tests 49

Estimating Parameters by Using Confidence Intervals 50

Getting the basics: The general form of a confidence interval 50

Finding the confidence interval for a population mean 51

What changes the margin of error? 52

Interpreting a confidence interval 55

What's the Hype about Hypothesis Tests? 56

What Ho and Ha really represent 56

Gathering your evidence into a test statistic 57

Determining strength of evidence with a p-value 57

False alarms and missed opportunities: Type I and II errors 58

The power of a hypothesis test 60

Part 2: Using Different Types of Regression to Make Predictions 65

Chapter 5: Getting in Line with Simple Linear Regression 67

Exploring Relationships with Scatterplots and Correlations 68

Using scatterplots to explore relationships 69

Collating the information by using the correlation coefficient 70

Building a Simple Linear Regression Model 71

Finding the best-fitting line to model your data 72

The y-intercept of the regression line 73

The slope of the regression line 74

Making point estimates by using the regression line 75

No Conclusion Left Behind: Tests and Confidence Intervals for Regression 75

Scrutinizing the slope 76

Inspecting the y-intercept 78

Building confidence intervals for the average response 80

Making the band with prediction intervals 81

Checking the Model's Fit (The Data, Not the Clothes!) 83

Defining the conditions 84

Finding and exploring the residuals 85

Using r2 to measure model fit 89

Scoping for outliers 90

Knowing the Limitations of Your Regression Analysis 92

Avoiding slipping into cause-and-effect mode 92

Extrapolation: The ultimate no-no 93

Sometimes you need more than one variable 94

Chapter 6: Multiple Regression with Two X Variables 95

Getting to Know the Multiple Regression Model 96

Discovering the uses of multiple regression 96

Looking at the general form of the multiple regression model 96

Stepping through the analysis 97

Looking at x's and y's 97

Collecting the Data 98

Pinpointing Possible Relationships 100

Making scatterplots 100

Correlations: Examining the bond 101

Checking for Multicolinearity 104

Finding the Best-Fitting Model for Two x Variables 105

Getting the multiple regression coefficients 106

Interpreting the coefficients 107

Testing the coefficients 108

Predicting y by Using the x Variab…