Linear Regression Models and Assumptions
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Regression Models for Categorical Dependent Variables Using Stata, Second Edition
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Forecasting with Dynamic Regression Models: 236 (Wiley Series in Probability and Statistics)
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Applied Regression Models in the Social Sciences
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Regression Analysis: An Intuitive Guide for Using and Interpreting Linear Models
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Microeconometrics Using Stata, Second Edition, Volume I: Cross-Sectional and Panel Regression Models: 1
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Regression Modeling Strategies: With Applications to Linear Models, Logistic Regression, and Survival Analysis
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Regression Models for Categorical and Count Data (The SAGE Quantitative Research Kit)
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Regression: Linear Models in Statistics – Springer Undergraduate Mathematics Series
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Fixed Effects Regression Models: 160 (Quantitative Applications in the Social Sciences)
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Beyond Multiple Linear Regression: Applied Generalized Linear Models And Multilevel Models in R (Chapman & Hall/CRC Texts in Statistical Science)
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Regression Analysis: An Intuitive Guide for Using and Interpreting Linear Models
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Regression Modeling Strategies: With Applications to Linear Models, Logistic and Ordinal Regression, and Survival Analysis (Springer Series in Statistics)
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Fitting Models to Biological Data: Linear & Nonlinear Regression – Practical Guide to Curve Fitting
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Analysis of Three Different Regression Models to Estimate the Ballistic Performance of New and Environmentally Conditioned Body Armor
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Data Analysis Using Regression and Multilevel/Hierarchical Models
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Logistic Regression Models for Ordinal Response Variables: 146 (Quantitative Applications in the Social Sciences)
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MP Applied Linear Regression Models-Revised Edition with Student CD (Irwin/McGraw Hill Series, Operations and Decision Sciences)
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Linear Regression Models: Applications in R (Chapman & Hall/CRC Statistics in the Social and Behavioral Sciences)
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Building Statistical Models in Python: Develop useful models for regression, classification, time series, and survival analysis
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Applied Regression Analysis and Generalized Linear Models
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Extending the Linear Model with R: Generalized Linear, Mixed Effects and Nonparametric Regression Models, Second Edition (Chapman & Hall/CRC Texts in Statistical Science)
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Spatial Regression Models for the Social Sciences (Advanced Quantitative Techniques in the Social Sciences)
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Data Analysis Using Regression and Multilevel/Hierarchical Models (Analytical Methods for Social Research)
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Model Discrimination for Nonlinear Regression Models: 101 (Statistics: A Series of Textbooks and Monographs)
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Statistics for Financial Analysts: Probability, Regression, Forecasting, and Risk Models for Applied Finance
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Estimating and Diagnosing Binary Logistic Regression Models: Wald Tests, Likelihood-Ratio Tests, Model Fit, and Estimation Stability
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R Machine Learning Projects with tidymodels: Hands-On Regression, Classification & Clustering - Build, Evaluate & Deploy Models for Data Scientists (R programming)
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Regression Models for Categorical and Limited Dependent Variables (Advanced Quantitative Techniques in the Social Sciences)
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In-depth Before the Regression: A Complete Guide to Validating Data for Linear Models
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In-depth Before the Regression: A Complete Guide to Validating Data for Linear Models: 4
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Regression for Health and Social Science: Applied Linear Models with R
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Statistics for Financial Analysts: Probability, Regression, Forecasting, and Risk Models for Applied Finance
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Linear Models and Least Squares Regression
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Log-Linear Models and Logistic Regression (Springer Texts in Statistics)
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