This book presents a thorough treatment of the methods of regression and analysis of variance. Applied Statistics requires an understanding of introductory statistics courses and is suitable for both junior and senior undergraduate students.
This book presents a thorough treatment of the methods of regression and analysis of variance. Applied Statistics requires an understanding of introductory statistics courses and is suitable for both junior and senior undergraduate students.
1: Introduction 2: Simple Linear Regression 3: Inferences on Parameter Estimates 4: Mutiple Linear Regression 5: Regression Diagnostics and Remedial Methods 6: Multiple and Partial Correlations 7: Model Selection Strategies 8: Use of Dummy Variables in Regression Analysis 9: Polynomial Regression 10: Logistic Regression 11: Count Data Regression Models 12: Regression with Censored of Truncated Data 13: Nonlinear Regression 14: One-Way Analysis of Variance 15: Two-Factor Analysis of Variance 16: Analysis of Covariance 17: Randomized Complete Block Design 18: Non Orthogonal Classification
1: Introduction 2: Simple Linear Regression 3: Inferences on Parameter Estimates 4: Mutiple Linear Regression 5: Regression Diagnostics and Remedial Methods 6: Multiple and Partial Correlations 7: Model Selection Strategies 8: Use of Dummy Variables in Regression Analysis 9: Polynomial Regression 10: Logistic Regression 11: Count Data Regression Models 12: Regression with Censored of Truncated Data 13: Nonlinear Regression 14: One-Way Analysis of Variance 15: Two-Factor Analysis of Variance 16: Analysis of Covariance 17: Randomized Complete Block Design 18: Non Orthogonal Classification
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