Applied Regression Analysis and Other Multivariable Methods Hardc
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Applied Regression Analysis and Other Multivariable Methods Hardc
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- Book Title
- Applied Regression Analysis and Other Multivariable Methods Hardc
- ISBN
- 9781285051086
Über dieses Produkt
Product Identifiers
Publisher
Brooks/Cole
ISBN-10
1285051084
ISBN-13
9781285051086
eBay Product ID (ePID)
159805676
Product Key Features
Number of Pages
1072 Pages
Publication Name
Applied Regression Analysis and Other Multivariable Methods
Language
English
Subject
Probability & Statistics / Regression Analysis, Probability & Statistics / Multivariate Analysis, Probability & Statistics / General
Publication Year
2013
Features
Revised
Type
Textbook
Subject Area
Mathematics
Format
Hardcover
Dimensions
Item Height
9.2 in
Item Weight
57.6 Oz
Item Length
1.7 in
Item Width
7.7 in
Additional Product Features
Edition Number
5
Intended Audience
College Audience
LCCN
2013-946149
Dewey Edition
23
Illustrated
Yes
Dewey Decimal
519.535
Edition Description
Revised edition
Table Of Content
1. CONCEPTS AND EXAMPLES OF RESEARCH.Concepts. Examples. Concluding Remarks. References.2. CLASSIFICATION OF VARIABLES AND THE CHOICE OF ANALYSIS.Classification of Variables. Overlapping of Classification Schemes. Choice of Analysis. References.3. BASIC STATISTICS: A REVIEW.Preview. Descriptive Statistics. Random Variables and Distributions. Sampling Distributions of t, ?Ó2, and F. Statistical Inference: Estimation. Statistical Inference: Hypothesis Testing. Error Rate, Power, and Sample Size. Problems. References.4. INTRODUCTION TO REGRESSION ANALYSIS.Preview. Association versus Causality. Statistical versus Deterministic Models. Concluding Remarks. References.5. STRAIGHT-LINE REGRESSION ANALYSIS.Preview. Regression with a Single Independent Variable. Mathematical Properties of a Straight Line. Statistical Assumptions for a Straight-line Model. Determining the Best-fitting Straight Line. Measure of the Quality of the Straight-line Fit and Estimate ?ã2. Inferences About the Slope and Intercept. Interpretations of Tests for Slope and Intercept. Inferences About the Regression Line ?ÝY X = ?Ò0 + ?Ò1X . Prediction of a New Value of Y at X0. Problems. References.6. THE CORRELATION COEFFICIENT AND STRAIGHT-LINE REGRESSION ANALYSIS.Definition of r. r as a Measure of Association. The Bivariate Normal Distribution. r and the Strength of the Straight-line Relationship. What r Does Not Measure. Tests of Hypotheses and Confidence Intervals for the Correlation Coefficient. Testing for the Equality of Two Correlations. Problems. References.7. THE ANALYSIS-OF-VARIANCE TABLE.Preview. The ANOVA Table for Straight-line Regression. Problems.8. MULTIPLE REGRESSION ANALYSIS: GENERAL CONSIDERATIONS.Preview. Multiple Regression Models. Graphical Look at the Problem. Assumptions of Multiple Regression. Determining the Best Estimate of the Multiple Regression Equation. The ANOVA Table for Multiple Regression. Numerical Examples. Problems. References.9. TESTING HYPOTHESES IN MULTIPLE REGRESSION.Preview. Test for Significant Overall Regression. Partial F Test. Multiple Partial F Test. Strategies for Using Partial F Tests. Tests Involving the Intercept. Problems. References.10. CORRELATIONS: MULTIPLE, PARTIAL, AND MULTIPLE PARTIAL.Preview. Correlation Matrix. Multiple Correlation Coefficient. Relationship of RY X1, X2, ¡KXk to the Multivariate Normal Distribution. Partial Correlation Coefficient. Alternative Representation of the Regression Model. Multiple Partial Correlation. Concluding Remarks. Problems. References.11. CONFOUNDING AND INTERACTION IN REGRESSION.Preview. Overview. Interaction in Regression. Confounding in Regression. Summary and Conclusions. Problems. References.12. DUMMY VARIABLES IN REGRESSION.Preview. Definitions. Rule for Defining Dummy Variables. Comparing Two Straight-line Regression Equations: An Example. Questions for Comparing Two Straight Lines. Methods of Comparing Two Straight Lines. Method I: Using Separate Regression Fits to Compare Two Straight Lines. Method II: Using a Single Regression Equation to Compare Two Straight Lines. Comparison of Methods I and II. Testing Strategies and Interpretation: Comparing Two Straight Lines. Other Dummy Variable Models. Comparing Four Regression Equations. Comparing Several Regression Equations Involving Two Nominal Variables. Problems. References.13. ANALYSIS OF COVARIANCE AND OTHER METHODS FOR ADJUSTING CONTINUOUS DATA.Preview. Adjustment Problem. Analysis of Covariance. Assumption of Parallelism: A Potential Drawback. Analysis of Covariance: Several Groups and Several Covariates. Comments and Cautions. Summary Problems. Reference.14. REGRESSION DIAGNOSTICS.Preview. Simple Approaches to Diagnosing Problems in Data. Residual Analysis: Detecting Outliers and Violations of Model Assumptions. Strategies of Analysis. Collinearity. Scaling Problems. Diagnostics Example. An Important Caution. Problems. References.15. POLYNOMIAL REGRESSION.Preview. Polynomial Model
Synopsis
This bestseller will help you learn regression-analysis methods that you can apply to real-life problems. It highlights the role of the computer in contemporary statistics with numerous printouts and exercises that you can solve using the computer. The authors continue to emphasize model development, the intuitive logic and assumptions that underlie the techniques covered, the purposes, advantages, and disadvantages of the techniques, and valid interpretations of those techniques. Available with InfoTrac Student Collections http://gocengage.com/infotrac., This bestseller will help you learn regression-analysis methods that you can apply to real-life problems. It highlights the role of the computer in contemporary statistics with numerous printouts and exercises that you can solve using the computer. The authors continue to emphasize model development, the intuitive logic and assumptions that underlie the techniques covered, the purposes, advantages, and disadvantages of the techniques, and valid interpretations of those techniques. Available with InfoTrac Student Collections http: //gocengage.com/infotrac., This bestseller will help you learn regression-analysis methods that you can apply to real-life problems. It highlights the role of the computer in contemporary statistics with numerous printouts and exercises that you can solve using the computer. The authors continue to emphasize model development, the intuitive logic and assumptions that underlie the techniques covered, the purposes, advantages, and disadvantages of the techniques, and valid interpretations of those techniques. Available with InfoTrac® Student Collections http://gocengage.com/infotrac.
LC Classification Number
QA278
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