REGRESSION ANALYSIS (SIMPLE LINEAR REGRESSION)
MS - MANAGEMENT SCIENCES, 2nd SEMESTER
ASSISTANT: PROFESSOR, SUIT
Of Science And Information Technology
TABLE OF CONTENTS
|S. No. |Subjects |Page No. | |1 | |Introduction |1 | |2 | |Historical Perspective Of Regression Analysis |3 | |3 | |Types Of Regression |4 | | |3.1 |Simple Liner Regression |4 | | |3.2 |Elements of a Regression Equation |4 | |4 | |Steps In Linear Regression |6 | |5 | |Solved Example |7 | |6 | |Data Interpretation |8 | |7 | |Assumptions Of Linear Regression |9 | |8 | |Citations |10 |
In statistics, regression analysis includes many techniques for modeling and analyzing several variables, when the focus is on the relationship between a dependent variable and one or more independent. More specifically, regression analysis helps one understand how the typical value of the dependent variable changes when any one of the independent variables is varied, while the other independent variables are held fixed. Most commonly, regression analysis estimates the conditional expectation of the dependent variable given the independent variables — that is, the average value of the dependent variable when the independent variables are fixed. Less commonly, the focus is on a quantile, or other location parameter of the conditional distribution of the dependent variable given the independent variables. In all cases, the estimation target is a function of the independent variables called the regression function. In regression analysis, it is also of interest to characterize the variation of the dependent variable around the regression function, which can be described by a probability distribution. (Wikipedia, 2012) Regression analysis is widely used for prediction and forecasting, where its use has substantial overlap with the field of machine learning. Regression analysis is also used to understand which among the independent variables are related to the dependent variable, and to explore the forms of these relationships. In restricted circumstances, regression analysis can be used to infer causal relationships between the independent and dependent variables. However this can lead to illusions or false relationships, so caution is advisable: A large body of techniques for carrying out regression analysis has been developed. Familiar methods such as linear regression and ordinary least squares regression are parametric, in that the regression function is defined in terms of a finite number of unknown parameters that are estimated from the data. Nonparametric regression refers to techniques that allow the regression function to lie in a specified set of functions, which may be infinite-dimensional. (Wikipedia, 2012) The performance of regression analysis...
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