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What is Regression Analysis
In statistical modeling, regression analysis is a set of statistical processes for estimating the relationships between a dependent variable and one or more independent variables. The most common form of regression analysis is linear regression, in which one finds the line that most closely fits the data according to a specific mathematical criterion. For example, the method of ordinary least squares computes the unique line that minimizes the sum of squared differences between the true data and that line. For specific mathematical reasons, this allows the researcher to estimate the conditional expectation of the dependent variable when the independent variables take on a given set of values. Less common forms of regression use slightly different procedures to estimate alternative location parameters or estimate the conditional expectation across a broader collection of non-linear models.
How you will benefit
(I) Insights, and validations about the following topics:
Chapter 1: Regression analysis
Chapter 2: Least squares
Chapter 3: Gauss-Markov theorem
Chapter 4: Nonlinear regression
Chapter 5: Coefficient of determination
Chapter 6: Instrumental variables estimation
Chapter 7: Omitted-variable bias
Chapter 8: Ordinary least squares
Chapter 9: Residual sum of squares
Chapter 10: Simple linear regression
Chapter 11: Generalized least squares
Chapter 12: Heteroskedasticity-consistent standard errors
Chapter 13: Variance inflation factor
Chapter 14: Non-linear least squares
Chapter 15: Principal component regression
Chapter 16: Lack-of-fit sum of squares
Chapter 17: Leverage (statistics)
Chapter 18: Polynomial regression
Chapter 19: Errors-in-variables models
Chapter 20: Linear least squares
Chapter 21: Linear regression
(II) Answering the public top questions about regression analysis.
(III) Real world examples for the usage of regression analysis in many fields.
Who this book is for
Professionals, undergraduate and graduate students, enthusiasts, hobbyists, and those who want to go beyond basic knowledge or information for any kind of Regression Analysis.
In statistical modeling, regression analysis is a set of statistical processes for estimating the relationships between a dependent variable and one or more independent variables. The most common form of regression analysis is linear regression, in which one finds the line that most closely fits the data according to a specific mathematical criterion. For example, the method of ordinary least squares computes the unique line that minimizes the sum of squared differences between the true data and that line. For specific mathematical reasons, this allows the researcher to estimate the conditional expectation of the dependent variable when the independent variables take on a given set of values. Less common forms of regression use slightly different procedures to estimate alternative location parameters or estimate the conditional expectation across a broader collection of non-linear models.
How you will benefit
(I) Insights, and validations about the following topics:
Chapter 1: Regression analysis
Chapter 2: Least squares
Chapter 3: Gauss-Markov theorem
Chapter 4: Nonlinear regression
Chapter 5: Coefficient of determination
Chapter 6: Instrumental variables estimation
Chapter 7: Omitted-variable bias
Chapter 8: Ordinary least squares
Chapter 9: Residual sum of squares
Chapter 10: Simple linear regression
Chapter 11: Generalized least squares
Chapter 12: Heteroskedasticity-consistent standard errors
Chapter 13: Variance inflation factor
Chapter 14: Non-linear least squares
Chapter 15: Principal component regression
Chapter 16: Lack-of-fit sum of squares
Chapter 17: Leverage (statistics)
Chapter 18: Polynomial regression
Chapter 19: Errors-in-variables models
Chapter 20: Linear least squares
Chapter 21: Linear regression
(II) Answering the public top questions about regression analysis.
(III) Real world examples for the usage of regression analysis in many fields.
Who this book is for
Professionals, undergraduate and graduate students, enthusiasts, hobbyists, and those who want to go beyond basic knowledge or information for any kind of Regression Analysis.
1,277 printed pages
- Original publication
- 2024
- Publication year
- 2024
- Publisher
- One Billion Knowledgeable
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