{"product_id":"applied-statistics-9780470571255","title":"Applied Statistics","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eThis work has been thoughtfully designed so that it serves equally well as a reference for the practitioner and as a self-contained textbook for the advanced student.\u003cbr\u003e * Rewritten to maintain clarity and brevity while expanding the coverage of previous editions.\u003cbr\u003e * Changes to design-related topics include increased discussion of mixed models and random effects, greater emphasis on regression and data screening, and more use of graphs throughout.\u003cbr\u003e * Includes both graded and challenging exercises.\u003cbr\u003e * Liberal computer discussions now supplemented with SAS and SPSS.\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003ePreface.  \u003cp\u003e\u003cb\u003e1. Data Screening.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e1.1 Variables and Their Classification.\u003c\/p\u003e \u003cp\u003e1.2 Describing the Data.\u003c\/p\u003e \u003cp\u003e1.3 Departures from Assumptions.\u003c\/p\u003e \u003cp\u003e1.4 Summary.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e2. One-Way Analysis of Variance Design.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e2.1 One-Way Analysis of Variance with Fixed Effects.\u003c\/p\u003e \u003cp\u003e2.2 One-Way Analysis of Variance with Random Effects.\u003c\/p\u003e \u003cp\u003e2.3 Designing an Observational Study or Experiment.\u003c\/p\u003e \u003cp\u003e2.4 Checking if the Data Fit the One-Way ANOVA Model.\u003c\/p\u003e \u003cp\u003e2.5 What to Do if the Data Do Not Fit the Model.\u003c\/p\u003e \u003cp\u003e2.6 Presentation and Interpretation of Results.\u003c\/p\u003e \u003cp\u003e2.7 Summary.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e3. Estimation and Simultaneous Inference.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e3.1 Estimation for Single Population Means.\u003c\/p\u003e \u003cp\u003e3.2 Estimation for Linear Combinations of Population Means.\u003c\/p\u003e \u003cp\u003e3.3 Simultaneous Statistical Inference.\u003c\/p\u003e \u003cp\u003e3.4 Inference for Variance Components.\u003c\/p\u003e \u003cp\u003e3.5 Presentation and Interpretation of Results.\u003c\/p\u003e \u003cp\u003e3.6 Summary.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e4. Hierarchical or Nested Design.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e4.1 Example.\u003c\/p\u003e \u003cp\u003e4.2 The Model.\u003c\/p\u003e \u003cp\u003e4.3 Analysis of Variance Table and F Tests.\u003c\/p\u003e \u003cp\u003e4.4 Estimation of Parameters.\u003c\/p\u003e \u003cp\u003e4.5 Inferences with Unequal Sample Sizes.\u003c\/p\u003e \u003cp\u003e4.6 Checking If the Data Fit the Model.\u003c\/p\u003e \u003cp\u003e4.7 What to Do If the Data Don't Fit the Model.\u003c\/p\u003e \u003cp\u003e4.8 Designing a Study.\u003c\/p\u003e \u003cp\u003e4.9 Summary.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e5. Two Crossed Factors: Fixed Effects and Equal Sample Sizes.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e5.1 Example.\u003c\/p\u003e \u003cp\u003e5.2 The Model.\u003c\/p\u003e \u003cp\u003e5.3 Interpretation of Models and Interaction.\u003c\/p\u003e \u003cp\u003e5.4 Analysis of Variance and F Tests.\u003c\/p\u003e \u003cp\u003e5.5 Estimates of Parameters and Confidence Intervals.\u003c\/p\u003e \u003cp\u003e5.6 Designing a Study.\u003c\/p\u003e \u003cp\u003e5.7 Presentation and Interpretation of Results.\u003c\/p\u003e \u003cp\u003e5.8 Summary.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e6 Randomized Complete Block Design.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e6.1 Example.\u003c\/p\u003e \u003cp\u003e6.2 The Randomized Complete Block Design.\u003c\/p\u003e \u003cp\u003e6.3 The Model.\u003c\/p\u003e \u003cp\u003e6.4 Analysis of Variance Table and F Tests.\u003c\/p\u003e \u003cp\u003e6.5 Estimation of Parameters and Confidence Intervals.\u003c\/p\u003e \u003cp\u003e6.6 Checking If the Data Fit the Model.\u003c\/p\u003e \u003cp\u003e6.7 What to Do if the Data Don't Fit the Model.\u003c\/p\u003e \u003cp\u003e6.8 Designing a Randomized Complete Block Study.\u003c\/p\u003e \u003cp\u003e6.9 Model Extensions.\u003c\/p\u003e \u003cp\u003e6.10 Summary.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e7. Two Crossed Factors: Fixed Effects and Unequal Sample Sizes.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e7.1 Example.\u003c\/p\u003e \u003cp\u003e7.2 The Model.\u003c\/p\u003e \u003cp\u003e7.3 Analysis of Variance and F Tests.\u003c\/p\u003e \u003cp\u003e7.4 Estimation of Parameters and Confidence Intervals.\u003c\/p\u003e \u003cp\u003e7.5 Checking If the Data Fit the Two-Way Model.\u003c\/p\u003e \u003cp\u003e7.6 What To Do If the Data Don't Fit the Model.\u003c\/p\u003e \u003cp\u003e7.7 Summary.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e8. Crossed Factors: Mixed Models.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e8.1 Example.\u003c\/p\u003e \u003cp\u003e8.2 The Mixed Model.\u003c\/p\u003e \u003cp\u003e8.3 Estimation of Fixed Effects.\u003c\/p\u003e \u003cp\u003e8.4 Analysis of Variance.\u003c\/p\u003e \u003cp\u003e8.5 Estimation of Variance Components.\u003c\/p\u003e \u003cp\u003e8.6 Hypothesis Testing.\u003c\/p\u003e \u003cp\u003e8.7 Confidence Intervals for Means and Variance Components.\u003c\/p\u003e \u003cp\u003e8.8 Comments on Available Software.\u003c\/p\u003e \u003cp\u003e8.9 Extensions of the Mixed Model.\u003c\/p\u003e \u003cp\u003e8.10 Summary.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e9. Repeated Measures Designs.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e9.1 Repeated Measures for a Single Population.\u003c\/p\u003e \u003cp\u003e9.2 Repeated Measures with Several Populations.\u003c\/p\u003e \u003cp\u003e9.3 Checking if the Data Fit the Repeated Measures Model.\u003c\/p\u003e \u003cp\u003e9.4 What to Do if the Data Don't Fit the Model.\u003c\/p\u003e \u003cp\u003e9.5 General Comments on Repeated Measures Analyses.\u003c\/p\u003e \u003cp\u003e9.6 Summary.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e10. Linear Regression: Fixed X Model.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e10.1 Example.\u003c\/p\u003e \u003cp\u003e10.2 Fitting a Straight Line.\u003c\/p\u003e \u003cp\u003e10.3 The Fixed X Model.\u003c\/p\u003e \u003cp\u003e10.4 Estimation of Model Parameters and Standard Errors.\u003c\/p\u003e \u003cp\u003e10.5 Inferences for Model Parameters: Confidence Intervals.\u003c\/p\u003e \u003cp\u003e10.6 Inference for Model Parameters: Hypothesis Testing.\u003c\/p\u003e \u003cp\u003e10.7 Checking if the Data Fit the Regression Model.\u003c\/p\u003e \u003cp\u003e10.8 What to Do if the Data Don't Fit the Model.\u003c\/p\u003e \u003cp\u003e10.9 Practical Issues in Designing a Regression Study.\u003c\/p\u003e \u003cp\u003e10.10 Comparison with One-Way ANOVA.\u003c\/p\u003e \u003cp\u003e10.11 Summary.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e11. Linear Regression: Random X Model and Correlation.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e11.1 Example.\u003c\/p\u003e \u003cp\u003e11.2 Summarizing the Relationship Between X and Y.\u003c\/p\u003e \u003cp\u003e11.3 Inferences for the Regression of Y and X.\u003c\/p\u003e \u003cp\u003e11.4 The Bivariate Normal Model.\u003c\/p\u003e \u003cp\u003e11.5 Checking if the Data Fit the Random X Regression Model.\u003c\/p\u003e \u003cp\u003e11.6 What to Do if the Data Don't Fit the Random X Model.\u003c\/p\u003e \u003cp\u003e11.7 Summary.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e12. Multiple Regression.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e12.1 Example.\u003c\/p\u003e \u003cp\u003e12.2 The Sample Regression Plane.\u003c\/p\u003e \u003cp\u003e12.3 The Multiple Regression Model.\u003c\/p\u003e \u003cp\u003e12.4 Parameters Standard Errors, and Confidence Intervals.\u003c\/p\u003e \u003cp\u003e12.5 Hypothesis Testing.\u003c\/p\u003e \u003cp\u003e12.6 Checking If the Data Fit the Multiple Regression Model.\u003c\/p\u003e \u003cp\u003e12.7 What to Do If the Data Don't Fit the Model.\u003c\/p\u003e \u003cp\u003e12.8 Summary.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e13. Multiple and Partial Correlation.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e13.1 Example.\u003c\/p\u003e \u003cp\u003e13.2 The Sample Multiple Correlation Coefficient.\u003c\/p\u003e \u003cp\u003e13.3 The Sample Partial Correlation Coefficient.\u003c\/p\u003e \u003cp\u003e13.4 The Joint Distribution Model.\u003c\/p\u003e \u003cp\u003e13.5 Inferences for the Multiple Correlation Coefficient.\u003c\/p\u003e \u003cp\u003e13.6 Inferences for Partial Correlation Coefficients.\u003c\/p\u003e \u003cp\u003e13.7 Checking If the Data Fit the Joint Normal Model.\u003c\/p\u003e \u003cp\u003e13.8 What to Do If the Data Don't Fit the Model.\u003c\/p\u003e \u003cp\u003e13.9 Summary.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e14. Miscellaneous Topics in Regression.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e14.1 Models with Dummy Variables.\u003c\/p\u003e \u003cp\u003e14.2 Models with Interaction Terms.\u003c\/p\u003e \u003cp\u003e14.3 Models with Polynomial Terms.\u003c\/p\u003e \u003cp\u003e14.4 Variable Selection.\u003c\/p\u003e \u003cp\u003e14.5 Summary.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e15. Analysis of Covariance.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e15.1 Example.\u003c\/p\u003e \u003cp\u003e15.2 The ANCOVA Model.\u003c\/p\u003e \u003cp\u003e15.3 Estimation of Model Parameters.\u003c\/p\u003e \u003cp\u003e15.4 Hypothesis Tests.\u003c\/p\u003e \u003cp\u003e15.5 Adjusted Means.\u003c\/p\u003e \u003cp\u003e15.6 Checking If the Data Fit the ANCOVA Model.\u003c\/p\u003e \u003cp\u003e15.7 What to Do if the Data Don't Fit the Model.\u003c\/p\u003e \u003cp\u003e15.8 ANCOVA in Observational Studies.\u003c\/p\u003e \u003cp\u003e15.9 What Makes a Good Covariate.\u003c\/p\u003e \u003cp\u003e15.10 Measurement Error.\u003c\/p\u003e \u003cp\u003e15.11 ANCOVA versus Other Methods of Adjustment.\u003c\/p\u003e \u003cp\u003e15.12 Comments on Statistical Software.\u003c\/p\u003e \u003cp\u003e15.13 Summary.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e16. Summaries, Extensions, and Communication.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e16.1 Summaries and Extensions of Models.\u003c\/p\u003e \u003cp\u003e16.2 Communication of Statistics in the Context of Research Project.\u003c\/p\u003e \u003cp\u003e\u003cb\u003eAppendix A.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eA.1 Expected Values and Parameters.\u003c\/p\u003e \u003cp\u003eA.2 Linear Combinations of Variables and Their Parameters.\u003c\/p\u003e \u003cp\u003eA.3 Balanced One-Way ANOVA, Expected Mean Squares.\u003c\/p\u003e \u003cp\u003eA.4 Balanced One-Way ANOVA, Random Effects.\u003c\/p\u003e \u003cp\u003eA.5 Balanced Nested Model.\u003c\/p\u003e \u003cp\u003eA.6 Mixed Model.\u003c\/p\u003e \u003cp\u003eA.7 Simple Linear Regression—Derivation of Least Squares Estimators.\u003c\/p\u003e \u003cp\u003eA.8 Derivation of Variance Estimates from Simple Linear Regression.\u003c\/p\u003e \u003cp\u003e\u003cb\u003eAppendix B.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eIndex.\u003c\/p\u003e","brand":"John Wiley \u0026 Sons Inc","offers":[{"title":"Default Title","offer_id":49402368983383,"sku":"9780470571255","price":92.66,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9780470571255.jpg?v=1730480185","url":"https:\/\/bookcurl.com\/products\/applied-statistics-9780470571255","provider":"Book Curl","version":"1.0","type":"link"}