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Regression
Regression analysis is a statistical technique used to examine the relationship between a dependent variable and one or more independent variables....
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Logistic Regression
This chapter covers logistic regression, which is a widely used method in analytics projects for predicting binary outcomes. The chapter begins by...
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Building Multiple Regression Models
Explanatory multiple regression models are used to accomplish two complementary goals: identification of drivers of performance and prediction of...
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Logistic Regression
This chapter covers a type of generalized linear model, logistic regression, that is applied to settings in which the outcome variable is not...
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Linear Regression
This chapter covers one of the most valuable tools for people analytics professionals: linear regression. Concepts, assumptions, and step-by-step...
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Multiple Regression Analysis
In the previous chapter we discussed that usually one independent variable is not sufficient to describe the dependent variable. Usually, several...
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Logistic Regression
Logistic regression is an algorithm for classification in two classes. We discuss the interpretation of the coefficients, prediction, and estimation....
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Ordinary Least Squares Regression
This chapter discusses least squares regression, one of the most widely used analytics tools for building predictive models. The chapter begins by...
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Two-stage regression spline modeling based on local polynomial kernel regression
This paper introduces a new nonparametric estimator of the regression based on local quasi-interpolation spline method. This model combines a...
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Correlation and Regression
The test procedures introduced across the preceding chapters were tailored to testing difference hypotheses. This chapter turns to the complementary...
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Multivariate Reduced-Rank Regression Theory, Methods and Applications
This book provides an account of multivariate reduced-rank regression, a tool of multivariate analysis that enjoys a broad array of applications. In...
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Multinomial Regression
Multinomial regression modeling of correlated sets of polytomous outcomes using the generalized logit link function is addressed allowing for...
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Ordinal Regression
Ordinal regression modeling of correlated sets of polytomous outcomes using the cumulative logit link function based on either individual outcomes or...
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Sharp Lower Bound for Regression with Measurement Errors and Its Implication for Ill-Posedness of Functional Regression
AbstractNonparametric regression estimation with Gaussian measurement errors in predictors is a classical statistical problem. It is well known that...
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Correlation and Regression Analysis
To investigate the relationship between quantitative variables, the most commonly used statistical techniques are correlation and regression analysis.
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Kernel regression for estimating regression function and its derivatives with unknown error correlations
In practice, it is common that errors are correlated in the nonparametric regression model. Although many methods have been developed for addressing...
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Linear Models for Regression
The goal of regression is to predict the target value y as a function f(x) of the d-dimensional input variables x
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Discrete Regression
Discrete regression of outcomes with a discrete number of possible numeric values is addressed, as an alternative to multinomial and ordinal...
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Classification and Regression Trees
This chapter discusses Classification and Regression Trees, widely used in data mining for predictive analytics. The chapter starts by explaining the...
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Regression Models, Methods and Applications
Now in its second edition, this textbook provides an applied and unified introduction to parametric, nonparametric and semiparametric regression that...
