Statistics II For Dummies

Name: Statistics II For Dummies
Brand: Wiley
Price: 16.99 EUR
Availability: OnlineOnly

Deborah J. Rumsey(Autor*in)

Wiley (Verlag)

2. Auflage

Erschienen am 7. Oktober 2021

448 Seiten

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Inhalt

Intro
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

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