{"product_id":"statistics-ii-for-dummies-2e-9781119827399","title":"Statistics II For Dummies 2e","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e\u003cp\u003eIntroduction 1\u003c\/p\u003e \u003cp\u003eAbout This Book 1\u003c\/p\u003e \u003cp\u003eFoolish Assumptions 3\u003c\/p\u003e \u003cp\u003eIcons Used in This Book 3\u003c\/p\u003e \u003cp\u003eBeyond the Book 4\u003c\/p\u003e \u003cp\u003eWhere to Go from Here 4\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart 1: Tackling Data Analysis and Model-Building Basics\u003c\/b\u003e\u003cb\u003e 7\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 1: Beyond Number Crunching: The Art and Science of Data Analysis\u003c\/b\u003e \u003cb\u003e9\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eData Analysis: Looking before You Crunch 9\u003c\/p\u003e \u003cp\u003eNothing (not even a straight line) lasts forever 10\u003c\/p\u003e \u003cp\u003eData snooping isn’t cool 11\u003c\/p\u003e \u003cp\u003eNo (data) fishing allowed 12\u003c\/p\u003e \u003cp\u003eGetting the Big Picture: An Overview of Stats II 13\u003c\/p\u003e \u003cp\u003ePopulation parameter 13\u003c\/p\u003e \u003cp\u003eSample statistic 13\u003c\/p\u003e \u003cp\u003eConfidence interval 14\u003c\/p\u003e \u003cp\u003eHypothesis test 14\u003c\/p\u003e \u003cp\u003eAnalysis of variance (ANOVA) 15\u003c\/p\u003e \u003cp\u003eMultiple comparisons 15\u003c\/p\u003e \u003cp\u003eInteraction effects 16\u003c\/p\u003e \u003cp\u003eCorrelation 16\u003c\/p\u003e \u003cp\u003eLinear regression 17\u003c\/p\u003e \u003cp\u003eChi-square tests 18\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 2: Finding the Right Analysis for the Job\u003c\/b\u003e \u003cb\u003e21\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eCategorical versus Quantitative Variables 22\u003c\/p\u003e \u003cp\u003eStatistics for Categorical Variables 23\u003c\/p\u003e \u003cp\u003eEstimating a proportion 23\u003c\/p\u003e \u003cp\u003eComparing proportions 24\u003c\/p\u003e \u003cp\u003eLooking for relationships between categorical variables 25\u003c\/p\u003e \u003cp\u003eBuilding models to make predictions 26\u003c\/p\u003e \u003cp\u003eStatistics for Quantitative Variables 27\u003c\/p\u003e \u003cp\u003eMaking estimates 27\u003c\/p\u003e \u003cp\u003eMaking comparisons 28\u003c\/p\u003e \u003cp\u003eExploring relationships 28\u003c\/p\u003e \u003cp\u003ePredicting y using x 30\u003c\/p\u003e \u003cp\u003eAvoiding Bias 31\u003c\/p\u003e \u003cp\u003eMeasuring Precision with Margin of Error 33\u003c\/p\u003e \u003cp\u003eKnowing Your Limitations 35\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 3: Having the Normal and Sampling Distributions in Your Back Pocket\u003c\/b\u003e \u003cb\u003e37\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eRecognizing the VIP Distribution — the Normal 38\u003c\/p\u003e \u003cp\u003eCharacterizing the normal 38\u003c\/p\u003e \u003cp\u003eStandardizing to the standard normal (Z-) distribution 38\u003c\/p\u003e \u003cp\u003eUsing the normal table 40\u003c\/p\u003e \u003cp\u003eFinding probabilities for the normal distribution 41\u003c\/p\u003e \u003cp\u003eFinally Getting Comfortable with Sampling Distributions 42\u003c\/p\u003e \u003cp\u003eThe mean and standard error of a sampling distribution 42\u003c\/p\u003e \u003cp\u003eSampling distribution of \u003ci\u003eX\u003c\/i\u003e 43\u003c\/p\u003e \u003cp\u003eSampling distribution of ˆ\u003ci\u003ep\u003c\/i\u003e 44\u003c\/p\u003e \u003cp\u003eHeads Up! Building Confidence Intervals and Hypothesis Tests 45\u003c\/p\u003e \u003cp\u003eConfidence interval for the population mean 45\u003c\/p\u003e \u003cp\u003eConfidence interval for the population proportion 46\u003c\/p\u003e \u003cp\u003eHypothesis test for population mean 46\u003c\/p\u003e \u003cp\u003eHypothesis test for the population proportion 47\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 4: Reviewing Confidence Intervals and Hypothesis Tests\u003c\/b\u003e \u003cb\u003e49\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eEstimating Parameters by Using Confidence Intervals 50\u003c\/p\u003e \u003cp\u003eGetting the basics: The general form of a confidence interval 50\u003c\/p\u003e \u003cp\u003eFinding the confidence interval for a population mean 51\u003c\/p\u003e \u003cp\u003eWhat changes the margin of error? 52\u003c\/p\u003e \u003cp\u003eInterpreting a confidence interval 55\u003c\/p\u003e \u003cp\u003eWhat’s the Hype about Hypothesis Tests? 56\u003c\/p\u003e \u003cp\u003eWhat Ho and Ha really represent 56\u003c\/p\u003e \u003cp\u003eGathering your evidence into a test statistic 57\u003c\/p\u003e \u003cp\u003eDetermining strength of evidence with a p-value 57\u003c\/p\u003e \u003cp\u003eFalse alarms and missed opportunities: Type I and II errors 58\u003c\/p\u003e \u003cp\u003eThe power of a hypothesis test 60\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart 2: Using Different Types of Regression to Make Predictions\u003c\/b\u003e \u003cb\u003e65\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 5: Getting in Line with Simple Linear Regression\u003c\/b\u003e \u003cb\u003e67\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eExploring Relationships with Scatterplots and Correlations 68\u003c\/p\u003e \u003cp\u003eUsing scatterplots to explore relationships 69\u003c\/p\u003e \u003cp\u003eCollating the information by using the correlation coefficient 70\u003c\/p\u003e \u003cp\u003eBuilding a Simple Linear Regression Model 71\u003c\/p\u003e \u003cp\u003eFinding the best-fitting line to model your data 72\u003c\/p\u003e \u003cp\u003eThe y-intercept of the regression line 73\u003c\/p\u003e \u003cp\u003eThe slope of the regression line 74\u003c\/p\u003e \u003cp\u003eMaking point estimates by using the regression line 75\u003c\/p\u003e \u003cp\u003eNo Conclusion Left Behind: Tests and Confidence Intervals for Regression 75\u003c\/p\u003e \u003cp\u003eScrutinizing the slope 76\u003c\/p\u003e \u003cp\u003eInspecting the y-intercept 78\u003c\/p\u003e \u003cp\u003eBuilding confidence intervals for the average response 80\u003c\/p\u003e \u003cp\u003eMaking the band with prediction intervals 81\u003c\/p\u003e \u003cp\u003eChecking the Model’s Fit (The Data, Not the Clothes!) 83\u003c\/p\u003e \u003cp\u003eDefining the conditions 84\u003c\/p\u003e \u003cp\u003eFinding and exploring the residuals 85\u003c\/p\u003e \u003cp\u003eUsing r2 to measure model fit 89\u003c\/p\u003e \u003cp\u003eScoping for outliers 90\u003c\/p\u003e \u003cp\u003eKnowing the Limitations of Your Regression Analysis 92\u003c\/p\u003e \u003cp\u003eAvoiding slipping into cause-and-effect mode 92\u003c\/p\u003e \u003cp\u003eExtrapolation: The ultimate no-no 93\u003c\/p\u003e \u003cp\u003eSometimes you need more than one variable 94\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 6: Multiple Regression with Two X Variables\u003c\/b\u003e \u003cb\u003e95\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eGetting to Know the Multiple Regression Model 96\u003c\/p\u003e \u003cp\u003eDiscovering the uses of multiple regression 96\u003c\/p\u003e \u003cp\u003eLooking at the general form of the multiple regression model 96\u003c\/p\u003e \u003cp\u003eStepping through the analysis 97\u003c\/p\u003e \u003cp\u003eLooking at x’s and y’s 97\u003c\/p\u003e \u003cp\u003eCollecting the Data 98\u003c\/p\u003e \u003cp\u003ePinpointing Possible Relationships 100\u003c\/p\u003e \u003cp\u003eMaking scatterplots 100\u003c\/p\u003e \u003cp\u003eCorrelations: Examining the bond 101\u003c\/p\u003e \u003cp\u003eChecking for Multicolinearity 104\u003c\/p\u003e \u003cp\u003eFinding the Best-Fitting Model for Two x Variables 105\u003c\/p\u003e \u003cp\u003eGetting the multiple regression coefficients 106\u003c\/p\u003e \u003cp\u003eInterpreting the coefficients 107\u003c\/p\u003e \u003cp\u003eTesting the coefficients 108\u003c\/p\u003e \u003cp\u003ePredicting y by Using the x Variables 110\u003c\/p\u003e \u003cp\u003eChecking the Fit of the Multiple Regression Model 111\u003c\/p\u003e \u003cp\u003eNoting the conditions 112\u003c\/p\u003e \u003cp\u003ePlotting a plan to check the conditions 112\u003c\/p\u003e \u003cp\u003eChecking the three conditions 114\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 7: How Can I Miss You If You Won’t Leave? Regression Model Selection\u003c\/b\u003e \u003cb\u003e117\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eGetting a Kick out of Estimating Punt Distance 118\u003c\/p\u003e \u003cp\u003eBrainstorming variables and collecting data 118\u003c\/p\u003e \u003cp\u003eExamining scatterplots and correlations 120\u003c\/p\u003e \u003cp\u003eJust Like Buying Shoes: The Model Looks Nice, But Does It Fit? 123\u003c\/p\u003e \u003cp\u003eAssessing the fit of multiple regression models 124\u003c\/p\u003e \u003cp\u003eModel selection procedures 125\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 8: Getting Ahead of the Learning Curve with Nonlinear Regression\u003c\/b\u003e \u003cb\u003e129\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eAnticipating Nonlinear Regression 130\u003c\/p\u003e \u003cp\u003eStarting Out with Scatterplots 131\u003c\/p\u003e \u003cp\u003eHandling Curves in the Road with Polynomials 133\u003c\/p\u003e \u003cp\u003eBringing back polynomials 134\u003c\/p\u003e \u003cp\u003eSearching for the best polynomial model 136\u003c\/p\u003e \u003cp\u003eUsing a second-degree polynomial to pass the quiz 138\u003c\/p\u003e \u003cp\u003eAssessing the fit of a polynomial model 141\u003c\/p\u003e \u003cp\u003eMaking predictions 143\u003c\/p\u003e \u003cp\u003eGoing Up? Going Down? Go Exponential! 145\u003c\/p\u003e \u003cp\u003eRecollecting exponential models 145\u003c\/p\u003e \u003cp\u003eSearching for the best exponential model 146\u003c\/p\u003e \u003cp\u003eSpreading secrets at an exponential rate 148\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 9: Yes, No, Maybe So: Making Predictions by Using Logistic Regression\u003c\/b\u003e \u003cb\u003e153\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eUnderstanding a Logistic Regression Model 154\u003c\/p\u003e \u003cp\u003eHow is logistic regression different from other regressions? 154\u003c\/p\u003e \u003cp\u003eUsing an S-curve to estimate probabilities 155\u003c\/p\u003e \u003cp\u003eInterpreting the coefficients of the logistic regression model 156\u003c\/p\u003e \u003cp\u003eThe logistic regression model in action 157\u003c\/p\u003e \u003cp\u003eCarrying Out a Logistic Regression Analysis 158\u003c\/p\u003e \u003cp\u003eRunning the analysis in Minitab 158\u003c\/p\u003e \u003cp\u003eFinding the coefficients and making the model 160\u003c\/p\u003e \u003cp\u003eEstimating p 161\u003c\/p\u003e \u003cp\u003eChecking the fit of the model 162\u003c\/p\u003e \u003cp\u003eFitting the movie model 162\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart 3: Analyzing Variance with Anova \u003c\/b\u003e\u003cb\u003e167\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 10: Testing Lots of Means? Come On Over to ANOVA!\u003c\/b\u003e\u003cb\u003e 169\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eComparing Two Means with a t-Test 170\u003c\/p\u003e \u003cp\u003eEvaluating More Means with ANOVA 171\u003c\/p\u003e \u003cp\u003eSpitting seeds: A situation just waiting for ANOVA 172\u003c\/p\u003e \u003cp\u003eWalking through the steps of ANOVA 173\u003c\/p\u003e \u003cp\u003eChecking the Conditions 174\u003c\/p\u003e \u003cp\u003eVerifying independence 174\u003c\/p\u003e \u003cp\u003eLooking for what’s normal 174\u003c\/p\u003e \u003cp\u003eTaking note of spread 176\u003c\/p\u003e \u003cp\u003eSetting Up the Hypotheses 178\u003c\/p\u003e \u003cp\u003eDoing the F-Test 179\u003c\/p\u003e \u003cp\u003eRunning ANOVA in Minitab 180\u003c\/p\u003e \u003cp\u003eBreaking down the variance into sums of squares 180\u003c\/p\u003e \u003cp\u003eLocating those mean sums of squares 182\u003c\/p\u003e \u003cp\u003eFiguring the F-statistic 183\u003c\/p\u003e \u003cp\u003eMaking conclusions from ANOVA 184\u003c\/p\u003e \u003cp\u003eWhat’s next? 186\u003c\/p\u003e \u003cp\u003eChecking the Fit of the ANOVA Model 186\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 11: Sorting Out the Means with Multiple Comparisons \u003c\/b\u003e\u003cb\u003e189\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eFollowing Up after ANOVA 190\u003c\/p\u003e \u003cp\u003eComparing cellphone minutes: An example 190\u003c\/p\u003e \u003cp\u003eSetting the stage for multiple comparison procedures 192\u003c\/p\u003e \u003cp\u003ePinpointing Differing Means with Fisher and Tukey       .193\u003c\/p\u003e \u003cp\u003eFishing for differences with Fisher’s LSD 194\u003c\/p\u003e \u003cp\u003eSeparating the turkeys with Tukey’s test 197\u003c\/p\u003e \u003cp\u003eExamining the Output to Determine the Analysis 198\u003c\/p\u003e \u003cp\u003eSo Many Other Procedures, So Little Time! 199\u003c\/p\u003e \u003cp\u003eControlling for baloney with the Bonferroni adjustment 200\u003c\/p\u003e \u003cp\u003eComparing combinations by using Scheffé’s method 201\u003c\/p\u003e \u003cp\u003eFinding out whodunit with Dunnett’s test 202\u003c\/p\u003e \u003cp\u003eStaying cool with Student Newman-Keuls 202\u003c\/p\u003e \u003cp\u003eDuncan’s multiple range test 202\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 12: Finding Your Way through Two-Way ANOVA\u003c\/b\u003e \u003cb\u003e205\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eSetting Up the Two-Way ANOVA Model 206\u003c\/p\u003e \u003cp\u003eDetermining the treatments 206\u003c\/p\u003e \u003cp\u003eStepping through the sums of squares 207\u003c\/p\u003e \u003cp\u003eUnderstanding Interaction Effects 209\u003c\/p\u003e \u003cp\u003eWhat is interaction, anyway? 209\u003c\/p\u003e \u003cp\u003eInteracting with interaction plots 210\u003c\/p\u003e \u003cp\u003eTesting the Terms in Two-Way ANOVA             .213\u003c\/p\u003e \u003cp\u003eRunning the Two-Way ANOVA Table 214\u003c\/p\u003e \u003cp\u003eInterpreting the results: Numbers and graphs 214\u003c\/p\u003e \u003cp\u003eAre Whites Whiter in Hot Water? Two-Way ANOVA Investigates 217\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 13: Regression and ANOVA: Surprise Relatives!\u003c\/b\u003e \u003cb\u003e221\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eSeeing Regression through the Eyes of Variation 222\u003c\/p\u003e \u003cp\u003eSpotting variability and finding an “x-planation” 222\u003c\/p\u003e \u003cp\u003eGetting results with regression 223\u003c\/p\u003e \u003cp\u003eAssessing the fit of the regression model 225\u003c\/p\u003e \u003cp\u003eRegression and ANOVA: A Meeting of the Models 226\u003c\/p\u003e \u003cp\u003eComparing sums of squares 226\u003c\/p\u003e \u003cp\u003eDividing up the degrees of freedom 228\u003c\/p\u003e \u003cp\u003eBringing regression to the ANOVA table 229\u003c\/p\u003e \u003cp\u003eRelating the F- and t-statistics: The final frontier 230\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart 4: Building Strong Connections with Chi-Square Tests and Nonparametrics\u003c\/b\u003e \u003cb\u003e233\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 14: Forming Associations with Two-Way Tables\u003c\/b\u003e \u003cb\u003e235\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eBreaking Down a Two-Way Table 236\u003c\/p\u003e \u003cp\u003eOrganizing data into a two-way table 236\u003c\/p\u003e \u003cp\u003eFilling in the cell counts 237\u003c\/p\u003e \u003cp\u003eMaking marginal totals 238\u003c\/p\u003e \u003cp\u003eBreaking Down the Probabilities 239\u003c\/p\u003e \u003cp\u003eMarginal probabilities 239\u003c\/p\u003e \u003cp\u003eJoint probabilities 241\u003c\/p\u003e \u003cp\u003eConditional probabilities 242\u003c\/p\u003e \u003cp\u003eTrying To Be Independent 247\u003c\/p\u003e \u003cp\u003eChecking for independence between two categories 247\u003c\/p\u003e \u003cp\u003eChecking for independence between two variables 249\u003c\/p\u003e \u003cp\u003eDemystifying Simpson’s Paradox 250\u003c\/p\u003e \u003cp\u003eExperiencing Simpson’s Paradox 250\u003c\/p\u003e \u003cp\u003eFiguring out why Simpson’s Paradox occurs 253\u003c\/p\u003e \u003cp\u003eKeeping one eye open for Simpson’s Paradox 254\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 15: Being Independent Enough for the Chi-Square Test\u003c\/b\u003e \u003cb\u003e257\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eThe Chi-Square Test for Independence 258\u003c\/p\u003e \u003cp\u003eCollecting and organizing the data 259\u003c\/p\u003e \u003cp\u003eDetermining the hypotheses 261\u003c\/p\u003e \u003cp\u003eFiguring expected cell counts 261\u003c\/p\u003e \u003cp\u003eChecking the conditions for the test 262\u003c\/p\u003e \u003cp\u003eCalculating the Chi-square test statistic 263\u003c\/p\u003e \u003cp\u003eFinding your results on the Chi-square table 266\u003c\/p\u003e \u003cp\u003eDrawing your conclusions 269\u003c\/p\u003e \u003cp\u003ePutting the Chi-square to the test 271\u003c\/p\u003e \u003cp\u003eComparing Two Tests for Comparing Two Proportions 272\u003c\/p\u003e \u003cp\u003eGetting reacquainted with the Z-test for two population proportions 273\u003c\/p\u003e \u003cp\u003eEquating Chi-square tests and Z-tests for a two-by-two table 274\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 16: Using Chi-Square Tests for Goodness-of-Fit (Your Data, Not Your Jeans) 279\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eFinding the Goodness-of-Fit Statistic 280\u003c\/p\u003e \u003cp\u003eWhat’s observed versus what’s expected 280\u003c\/p\u003e \u003cp\u003eCalculating the goodness-of-fit statistic 282\u003c\/p\u003e \u003cp\u003eInterpreting the Goodness-of-Fit Statistic Using a Chi-Square 284\u003c\/p\u003e \u003cp\u003eChecking the conditions before you start 285\u003c\/p\u003e \u003cp\u003eThe steps of the Chi-square goodness-of-fit test 286\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 17: Rebels Without a Distribution — Nonparametric Procedures\u003c\/b\u003e \u003cb\u003e291\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eArguing for Nonparametric Statistics 292\u003c\/p\u003e \u003cp\u003eNo need to fret if conditions aren’t met 292\u003c\/p\u003e \u003cp\u003eThe median’s in the spotlight for a change 293\u003c\/p\u003e \u003cp\u003eSo, what’s the catch? 295\u003c\/p\u003e \u003cp\u003eMastering the Basics of Nonparametric Statistics 296\u003c\/p\u003e \u003cp\u003eSign 296\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 18: All Signs Point to the Sign Test\u003c\/b\u003e \u003cb\u003e299\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eReading the Signs: The Sign Test 300\u003c\/p\u003e \u003cp\u003eTesting the median in real estate 302\u003c\/p\u003e \u003cp\u003eEstimating the median 304\u003c\/p\u003e \u003cp\u003eTesting matched pairs 306\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart 5: Putting it all Together: Multi-Stage Analysis of A Large Data Set\u003c\/b\u003e \u003cb\u003e309\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 19: Conducting a Multi-Stage Analysis of a Large Data Set\u003c\/b\u003e \u003cb\u003e311\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eSteps Involved in Working with a Large Data Set 311\u003c\/p\u003e \u003cp\u003eWrangling Data 313\u003c\/p\u003e \u003cp\u003eDiscovery 313\u003c\/p\u003e \u003cp\u003eStructuring 314\u003c\/p\u003e \u003cp\u003eCleaning 315\u003c\/p\u003e \u003cp\u003eEnriching 315\u003c\/p\u003e \u003cp\u003eValidating 316\u003c\/p\u003e \u003cp\u003ePublishing 317\u003c\/p\u003e \u003cp\u003eVisualizing Data 317\u003c\/p\u003e \u003cp\u003eExploring the Data 319\u003c\/p\u003e \u003cp\u003eLooking for Relationships 319\u003c\/p\u003e \u003cp\u003eBuilding Models and Making Inferences 320\u003c\/p\u003e \u003cp\u003eSharing the Story 321\u003c\/p\u003e \u003cp\u003eWho is the audience? 322\u003c\/p\u003e \u003cp\u003eMake an outline 322\u003c\/p\u003e \u003cp\u003eInclude an executive summary 323\u003c\/p\u003e \u003cp\u003eCheck your writing 323\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 20: A Statistician Watches the Movies\u003c\/b\u003e \u003cb\u003e325\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eExamining the Movie Variables and Asking Questions 326\u003c\/p\u003e \u003cp\u003eVisualizing the Movie Data 327\u003c\/p\u003e \u003cp\u003eCategorical movie variables 328\u003c\/p\u003e \u003cp\u003eQuantitative movie variables 329\u003c\/p\u003e \u003cp\u003eDoing Descriptive Dirty Work 332\u003c\/p\u003e \u003cp\u003eLooking for Relationships 333\u003c\/p\u003e \u003cp\u003eRelationships between quantitative movie variables 333\u003c\/p\u003e \u003cp\u003eRelationships between two categorical variables 337\u003c\/p\u003e \u003cp\u003eRelationships between quantitative and categorical variables 338\u003c\/p\u003e \u003cp\u003eBuilding a Model for Predicting U.S Revenue 340\u003c\/p\u003e \u003cp\u003eWriting It Up 343\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 21: Looking Inside the Refrigerator\u003c\/b\u003e \u003cb\u003e347\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eRefrigerator Data — The Variables 348\u003c\/p\u003e \u003cp\u003eExploring the Data 348\u003c\/p\u003e \u003cp\u003eAnalyzing the Data 350\u003c\/p\u003e \u003cp\u003eWriting It Up 358\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart 6: The Part of Tens\u003c\/b\u003e \u003cb\u003e361\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 22: Ten Common Errors in Statistical Conclusions\u003c\/b\u003e\u003cb\u003e 363\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eClaiming These Statistics Prove 363\u003c\/p\u003e \u003cp\u003eIt’s Not Technically Statistically Significant, But 364\u003c\/p\u003e \u003cp\u003eConcluding That x Causes y 365\u003c\/p\u003e \u003cp\u003eAssuming the Data Was Normal 366\u003c\/p\u003e \u003cp\u003eOnly Reporting “Important” Results 366\u003c\/p\u003e \u003cp\u003eAssuming a Bigger Sample Is Always Better 367\u003c\/p\u003e \u003cp\u003eIt’s Not Technically Random, But 369\u003c\/p\u003e \u003cp\u003eAssuming That 1,000 Responses Is 1,000 Responses 369\u003c\/p\u003e \u003cp\u003eOf Course the Results Apply to the General Population 371\u003c\/p\u003e \u003cp\u003eDeciding Just to Leave It Out 372\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 23: Ten Ways to Get Ahead by Knowing Statistics\u003c\/b\u003e \u003cb\u003e375\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eAsking the Right Questions 375\u003c\/p\u003e \u003cp\u003eBeing Skeptical 376\u003c\/p\u003e \u003cp\u003eCollecting and Analyzing Data Correctly 377\u003c\/p\u003e \u003cp\u003eCalling for Help 378\u003c\/p\u003e \u003cp\u003eRetracing Someone Else’s Steps 379\u003c\/p\u003e \u003cp\u003ePutting the Pieces Together 379\u003c\/p\u003e \u003cp\u003eChecking Your Answers 380\u003c\/p\u003e \u003cp\u003eExplaining the Output 381\u003c\/p\u003e \u003cp\u003eMaking Convincing Recommendations 382\u003c\/p\u003e \u003cp\u003eEstablishing Yourself as the Statistics Go-To Person 383\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 24: Ten Cool Jobs That Use Statistics\u003c\/b\u003e \u003cb\u003e385\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003ePollster 386\u003c\/p\u003e \u003cp\u003eData Scientist 387\u003c\/p\u003e \u003cp\u003eOrnithologist (Bird Watcher) 387\u003c\/p\u003e \u003cp\u003eSportscaster or Sportswriter 388\u003c\/p\u003e \u003cp\u003eJournalist 390\u003c\/p\u003e \u003cp\u003eCrime Fighter 390\u003c\/p\u003e \u003cp\u003eMedical Professional 391\u003c\/p\u003e \u003cp\u003eMarketing Executive 392\u003c\/p\u003e \u003cp\u003eLawyer 393\u003c\/p\u003e \u003cp\u003eAppendix A: Reference Tables 395\u003c\/p\u003e \u003cp\u003eIndex 409\u003c\/p\u003e","brand":"John Wiley \u0026 Sons Inc","offers":[{"title":"Default Title","offer_id":48866418655575,"sku":"9781119827399","price":16.14,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9781119827399.jpg?v=1722278550","url":"https:\/\/bookcurl.com\/products\/statistics-ii-for-dummies-2e-9781119827399","provider":"Book Curl","version":"1.0","type":"link"}