MARC details
| 000 -LEADER |
|---|
| fixed length control field |
15774nam a22002057a 4500 |
| 005 - DATE AND TIME OF LATEST TRANSACTION |
|---|
| control field |
20220125123219.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION |
|---|
| fixed length control field |
220107b ||||| |||| 00| 0 eng d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER |
|---|
| International Standard Book Number |
9781119827399 |
| 082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER |
|---|
| Classification number |
519.5 |
| Item number |
RUM |
| 100 ## - MAIN ENTRY--PERSONAL NAME |
|---|
| Personal name |
Rumsey, Deborah J. |
| 245 ## - TITLE STATEMENT |
|---|
| Title |
Statistics II for dummies |
| 250 ## - EDITION STATEMENT |
|---|
| Edition statement |
2nd |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT) |
|---|
| Name of publisher, distributor, etc. |
Wiley Publishing, Inc. |
| Place of publication, distribution, etc. |
New Jersey |
| Date of publication, distribution, etc. |
2022 |
| 300 ## - PHYSICAL DESCRIPTION |
|---|
| Extent |
xv, 423 p. |
| 365 ## - TRADE PRICE |
|---|
| Price type code |
USD |
| Price amount |
24.95 |
| 504 ## - BIBLIOGRAPHY, ETC. NOTE |
|---|
| Bibliography, etc. note |
TABLE OF CONTENTS<br/>Introduction 1<br/><br/>About This Book 1<br/><br/>Foolish Assumptions 3<br/><br/>Icons Used in This Book 3<br/><br/>Beyond the Book 4<br/><br/>Where to Go from Here 4<br/><br/>Part 1: Tackling Data Analysis and Model-Building Basics 7<br/><br/>Chapter 1: Beyond Number Crunching: The Art and Science of Data Analysis 9<br/><br/>Data Analysis: Looking before You Crunch 9<br/><br/>Nothing (not even a straight line) lasts forever 10<br/><br/>Data snooping isn’t cool 11<br/><br/>No (data) fishing allowed 12<br/><br/>Getting the Big Picture: An Overview of Stats II 13<br/><br/>Population parameter 13<br/><br/>Sample statistic 13<br/><br/>Confidence interval 14<br/><br/>Hypothesis test 14<br/><br/>Analysis of variance (ANOVA) 15<br/><br/>Multiple comparisons 15<br/><br/>Interaction effects 16<br/><br/>Correlation 16<br/><br/>Linear regression 17<br/><br/>Chi-square tests 18<br/><br/>Chapter 2: Finding the Right Analysis for the Job 21<br/><br/>Categorical versus Quantitative Variables 22<br/><br/>Statistics for Categorical Variables 23<br/><br/>Estimating a proportion 23<br/><br/>Comparing proportions 24<br/><br/>Looking for relationships between categorical variables 25<br/><br/>Building models to make predictions 26<br/><br/>Statistics for Quantitative Variables 27<br/><br/>Making estimates 27<br/><br/>Making comparisons 28<br/><br/>Exploring relationships 28<br/><br/>Predicting y using x 30<br/><br/>Avoiding Bias 31<br/><br/>Measuring Precision with Margin of Error 33<br/><br/>Knowing Your Limitations 35<br/><br/>Chapter 3: Having the Normal and Sampling Distributions in Your Back Pocket 37<br/><br/>Recognizing the VIP Distribution — the Normal 38<br/><br/>Characterizing the normal 38<br/><br/>Standardizing to the standard normal (Z-) distribution 38<br/><br/>Using the normal table 40<br/><br/>Finding probabilities for the normal distribution 41<br/><br/>Finally Getting Comfortable with Sampling Distributions 42<br/><br/>The mean and standard error of a sampling distribution 42<br/><br/>Sampling distribution of X 43<br/><br/>Sampling distribution of ˆp 44<br/><br/>Heads Up! Building Confidence Intervals and Hypothesis Tests 45<br/><br/>Confidence interval for the population mean 45<br/><br/>Confidence interval for the population proportion 46<br/><br/>Hypothesis test for population mean 46<br/><br/>Hypothesis test for the population proportion 47<br/><br/>Chapter 4: Reviewing Confidence Intervals and Hypothesis Tests 49<br/><br/>Estimating Parameters by Using Confidence Intervals 50<br/><br/>Getting the basics: The general form of a confidence interval 50<br/><br/>Finding the confidence interval for a population mean 51<br/><br/>What changes the margin of error? 52<br/><br/>Interpreting a confidence interval 55<br/><br/>What’s the Hype about Hypothesis Tests? 56<br/><br/>What Ho and Ha really represent 56<br/><br/>Gathering your evidence into a test statistic 57<br/><br/>Determining strength of evidence with a p-value 57<br/><br/>False alarms and missed opportunities: Type I and II errors 58<br/><br/>The power of a hypothesis test 60<br/><br/>Part 2: Using Different Types of Regression to Make Predictions 65<br/><br/>Chapter 5: Getting in Line with Simple Linear Regression 67<br/><br/>Exploring Relationships with Scatterplots and Correlations 68<br/><br/>Using scatterplots to explore relationships 69<br/><br/>Collating the information by using the correlation coefficient 70<br/><br/>Building a Simple Linear Regression Model 71<br/><br/>Finding the best-fitting line to model your data 72<br/><br/>The y-intercept of the regression line 73<br/><br/>The slope of the regression line 74<br/><br/>Making point estimates by using the regression line 75<br/><br/>No Conclusion Left Behind: Tests and Confidence Intervals for Regression 75<br/><br/>Scrutinizing the slope 76<br/><br/>Inspecting the y-intercept 78<br/><br/>Building confidence intervals for the average response 80<br/><br/>Making the band with prediction intervals 81<br/><br/>Checking the Model’s Fit (The Data, Not the Clothes!) 83<br/><br/>Defining the conditions 84<br/><br/>Finding and exploring the residuals 85<br/><br/>Using r2 to measure model fit 89<br/><br/>Scoping for outliers 90<br/><br/>Knowing the Limitations of Your Regression Analysis 92<br/><br/>Avoiding slipping into cause-and-effect mode 92<br/><br/>Extrapolation: The ultimate no-no 93<br/><br/>Sometimes you need more than one variable 94<br/><br/>Chapter 6: Multiple Regression with Two X Variables 95<br/><br/>Getting to Know the Multiple Regression Model 96<br/><br/>Discovering the uses of multiple regression 96<br/><br/>Looking at the general form of the multiple regression model 96<br/><br/>Stepping through the analysis 97<br/><br/>Looking at x’s and y’s 97<br/><br/>Collecting the Data 98<br/><br/>Pinpointing Possible Relationships 100<br/><br/>Making scatterplots 100<br/><br/>Correlations: Examining the bond 101<br/><br/>Checking for Multicolinearity 104<br/><br/>Finding the Best-Fitting Model for Two x Variables 105<br/><br/>Getting the multiple regression coefficients 106<br/><br/>Interpreting the coefficients 107<br/><br/>Testing the coefficients 108<br/><br/>Predicting y by Using the x Variables 110<br/><br/>Checking the Fit of the Multiple Regression Model 111<br/><br/>Noting the conditions 112<br/><br/>Plotting a plan to check the conditions 112<br/><br/>Checking the three conditions 114<br/><br/>Chapter 7: How Can I Miss You If You Won’t Leave? Regression Model Selection 117<br/><br/>Getting a Kick out of Estimating Punt Distance 118<br/><br/>Brainstorming variables and collecting data 118<br/><br/>Examining scatterplots and correlations 120<br/><br/>Just Like Buying Shoes: The Model Looks Nice, But Does It Fit? 123<br/><br/>Assessing the fit of multiple regression models 124<br/><br/>Model selection procedures 125<br/><br/>Chapter 8: Getting Ahead of the Learning Curve with Nonlinear Regression 129<br/><br/>Anticipating Nonlinear Regression 130<br/><br/>Starting Out with Scatterplots 131<br/><br/>Handling Curves in the Road with Polynomials 133<br/><br/>Bringing back polynomials 134<br/><br/>Searching for the best polynomial model 136<br/><br/>Using a second-degree polynomial to pass the quiz 138<br/><br/>Assessing the fit of a polynomial model 141<br/><br/>Making predictions 143<br/><br/>Going Up? Going Down? Go Exponential! 145<br/><br/>Recollecting exponential models 145<br/><br/>Searching for the best exponential model 146<br/><br/>Spreading secrets at an exponential rate 148<br/><br/>Chapter 9: Yes, No, Maybe So: Making Predictions by Using Logistic Regression 153<br/><br/>Understanding a Logistic Regression Model 154<br/><br/>How is logistic regression different from other regressions? 154<br/><br/>Using an S-curve to estimate probabilities 155<br/><br/>Interpreting the coefficients of the logistic regression model 156<br/><br/>The logistic regression model in action 157<br/><br/>Carrying Out a Logistic Regression Analysis 158<br/><br/>Running the analysis in Minitab 158<br/><br/>Finding the coefficients and making the model 160<br/><br/>Estimating p 161<br/><br/>Checking the fit of the model 162<br/><br/>Fitting the movie model 162<br/><br/>Part 3: Analyzing Variance with Anova 167<br/><br/>Chapter 10: Testing Lots of Means? Come On Over to ANOVA! 169<br/><br/>Comparing Two Means with a t-Test 170<br/><br/>Evaluating More Means with ANOVA 171<br/><br/>Spitting seeds: A situation just waiting for ANOVA 172<br/><br/>Walking through the steps of ANOVA 173<br/><br/>Checking the Conditions 174<br/><br/>Verifying independence 174<br/><br/>Looking for what’s normal 174<br/><br/>Taking note of spread 176<br/><br/>Setting Up the Hypotheses 178<br/><br/>Doing the F-Test 179<br/><br/>Running ANOVA in Minitab 180<br/><br/>Breaking down the variance into sums of squares 180<br/><br/>Locating those mean sums of squares 182<br/><br/>Figuring the F-statistic 183<br/><br/>Making conclusions from ANOVA 184<br/><br/>What’s next? 186<br/><br/>Checking the Fit of the ANOVA Model 186<br/><br/>Chapter 11: Sorting Out the Means with Multiple Comparisons 189<br/><br/>Following Up after ANOVA 190<br/><br/>Comparing cellphone minutes: An example 190<br/><br/>Setting the stage for multiple comparison procedures 192<br/><br/>Pinpointing Differing Means with Fisher and Tukey .193<br/><br/>Fishing for differences with Fisher’s LSD 194<br/><br/>Separating the turkeys with Tukey’s test 197<br/><br/>Examining the Output to Determine the Analysis 198<br/><br/>So Many Other Procedures, So Little Time! 199<br/><br/>Controlling for baloney with the Bonferroni adjustment 200<br/><br/>Comparing combinations by using Scheffé’s method 201<br/><br/>Finding out whodunit with Dunnett’s test 202<br/><br/>Staying cool with Student Newman-Keuls 202<br/><br/>Duncan’s multiple range test 202<br/><br/>Chapter 12: Finding Your Way through Two-Way ANOVA 205<br/><br/>Setting Up the Two-Way ANOVA Model 206<br/><br/>Determining the treatments 206<br/><br/>Stepping through the sums of squares 207<br/><br/>Understanding Interaction Effects 209<br/><br/>What is interaction, anyway? 209<br/><br/>Interacting with interaction plots 210<br/><br/>Testing the Terms in Two-Way ANOVA .213<br/><br/>Running the Two-Way ANOVA Table 214<br/><br/>Interpreting the results: Numbers and graphs 214<br/><br/>Are Whites Whiter in Hot Water? Two-Way ANOVA Investigates 217<br/><br/>Chapter 13: Regression and ANOVA: Surprise Relatives! 221<br/><br/>Seeing Regression through the Eyes of Variation 222<br/><br/>Spotting variability and finding an “x-planation” 222<br/><br/>Getting results with regression 223<br/><br/>Assessing the fit of the regression model 225<br/><br/>Regression and ANOVA: A Meeting of the Models 226<br/><br/>Comparing sums of squares 226<br/><br/>Dividing up the degrees of freedom 228<br/><br/>Bringing regression to the ANOVA table 229<br/><br/>Relating the F- and t-statistics: The final frontier 230<br/><br/>Part 4: Building Strong Connections with Chi-Square Tests and Nonparametrics 233<br/><br/>Chapter 14: Forming Associations with Two-Way Tables 235<br/><br/>Breaking Down a Two-Way Table 236<br/><br/>Organizing data into a two-way table 236<br/><br/>Filling in the cell counts 237<br/><br/>Making marginal totals 238<br/><br/>Breaking Down the Probabilities 239<br/><br/>Marginal probabilities 239<br/><br/>Joint probabilities 241<br/><br/>Conditional probabilities 242<br/><br/>Trying To Be Independent 247<br/><br/>Checking for independence between two categories 247<br/><br/>Checking for independence between two variables 249<br/><br/>Demystifying Simpson’s Paradox 250<br/><br/>Experiencing Simpson’s Paradox 250<br/><br/>Figuring out why Simpson’s Paradox occurs 253<br/><br/>Keeping one eye open for Simpson’s Paradox 254<br/><br/>Chapter 15: Being Independent Enough for the Chi-Square Test 257<br/><br/>The Chi-Square Test for Independence 258<br/><br/>Collecting and organizing the data 259<br/><br/>Determining the hypotheses 261<br/><br/>Figuring expected cell counts 261<br/><br/>Checking the conditions for the test 262<br/><br/>Calculating the Chi-square test statistic 263<br/><br/>Finding your results on the Chi-square table 266<br/><br/>Drawing your conclusions 269<br/><br/>Putting the Chi-square to the test 271<br/><br/>Comparing Two Tests for Comparing Two Proportions 272<br/><br/>Getting reacquainted with the Z-test for two population proportions 273<br/><br/>Equating Chi-square tests and Z-tests for a two-by-two table 274<br/><br/>Chapter 16: Using Chi-Square Tests for Goodness-of-Fit (Your Data, Not Your Jeans) 279<br/><br/>Finding the Goodness-of-Fit Statistic 280<br/><br/>What’s observed versus what’s expected 280<br/><br/>Calculating the goodness-of-fit statistic 282<br/><br/>Interpreting the Goodness-of-Fit Statistic Using a Chi-Square 284<br/><br/>Checking the conditions before you start 285<br/><br/>The steps of the Chi-square goodness-of-fit test 286<br/><br/>Chapter 17: Rebels Without a Distribution — Nonparametric Procedures 291<br/><br/>Arguing for Nonparametric Statistics 292<br/><br/>No need to fret if conditions aren’t met 292<br/><br/>The median’s in the spotlight for a change 293<br/><br/>So, what’s the catch? 295<br/><br/>Mastering the Basics of Nonparametric Statistics 296<br/><br/>Sign 296<br/><br/>Chapter 18: All Signs Point to the Sign Test 299<br/><br/>Reading the Signs: The Sign Test 300<br/><br/>Testing the median in real estate 302<br/><br/>Estimating the median 304<br/><br/>Testing matched pairs 306<br/><br/>Part 5: Putting it all Together: Multi-Stage Analysis of A Large Data Set 309<br/><br/>Chapter 19: Conducting a Multi-Stage Analysis of a Large Data Set 311<br/><br/>Steps Involved in Working with a Large Data Set 311<br/><br/>Wrangling Data 313<br/><br/>Discovery 313<br/><br/>Structuring 314<br/><br/>Cleaning 315<br/><br/>Enriching 315<br/><br/>Validating 316<br/><br/>Publishing 317<br/><br/>Visualizing Data 317<br/><br/>Exploring the Data 319<br/><br/>Looking for Relationships 319<br/><br/>Building Models and Making Inferences 320<br/><br/>Sharing the Story 321<br/><br/>Who is the audience? 322<br/><br/>Make an outline 322<br/><br/>Include an executive summary 323<br/><br/>Check your writing 323<br/><br/>Chapter 20: A Statistician Watches the Movies 325<br/><br/>Examining the Movie Variables and Asking Questions 326<br/><br/>Visualizing the Movie Data 327<br/><br/>Categorical movie variables 328<br/><br/>Quantitative movie variables 329<br/><br/>Doing Descriptive Dirty Work 332<br/><br/>Looking for Relationships 333<br/><br/>Relationships between quantitative movie variables 333<br/><br/>Relationships between two categorical variables 337<br/><br/>Relationships between quantitative and categorical variables 338<br/><br/>Building a Model for Predicting U.S Revenue 340<br/><br/>Writing It Up 343<br/><br/>Chapter 21: Looking Inside the Refrigerator 347<br/><br/>Refrigerator Data — The Variables 348<br/><br/>Exploring the Data 348<br/><br/>Analyzing the Data 350<br/><br/>Writing It Up 358<br/><br/>Part 6: The Part of Tens 361<br/><br/>Chapter 22: Ten Common Errors in Statistical Conclusions 363<br/><br/>Claiming These Statistics Prove 363<br/><br/>It’s Not Technically Statistically Significant, But 364<br/><br/>Concluding That x Causes y 365<br/><br/>Assuming the Data Was Normal 366<br/><br/>Only Reporting “Important” Results 366<br/><br/>Assuming a Bigger Sample Is Always Better 367<br/><br/>It’s Not Technically Random, But 369<br/><br/>Assuming That 1,000 Responses Is 1,000 Responses 369<br/><br/>Of Course the Results Apply to the General Population 371<br/><br/>Deciding Just to Leave It Out 372<br/><br/>Chapter 23: Ten Ways to Get Ahead by Knowing Statistics 375<br/><br/>Asking the Right Questions 375<br/><br/>Being Skeptical 376<br/><br/>Collecting and Analyzing Data Correctly 377<br/><br/>Calling for Help 378<br/><br/>Retracing Someone Else’s Steps 379<br/><br/>Putting the Pieces Together 379<br/><br/>Checking Your Answers 380<br/><br/>Explaining the Output 381<br/><br/>Making Convincing Recommendations 382<br/><br/>Establishing Yourself as the Statistics Go-To Person 383<br/><br/>Chapter 24: Ten Cool Jobs That Use Statistics 385<br/><br/>Pollster 386<br/><br/>Data Scientist 387<br/><br/>Ornithologist (Bird Watcher) 387<br/><br/>Sportscaster or Sportswriter 388<br/><br/>Journalist 390<br/><br/>Crime Fighter 390<br/><br/>Medical Professional 391<br/><br/>Marketing Executive 392<br/><br/>Lawyer 393<br/><br/>Appendix A: Reference Tables 395<br/><br/>Index 409 |
| 520 ## - SUMMARY, ETC. |
|---|
| Summary, etc. |
DESCRIPTION<br/>Continue your statistics journey with this all-encompassing reference <br/><br/>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. <br/><br/>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: <br/><br/>What makes each technique distinct and what the results say <br/>How to apply techniques in real life <br/>An interpretation of the computer output for data analysis purposes <br/>Instructions for using Minitab to work through many of the calculations <br/>Practice with a lot of examples <br/>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! |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name as entry element |
Statistics |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) |
|---|
| Source of classification or shelving scheme |
Dewey Decimal Classification |
| Koha item type |
Book |