How expected change is estimated? when the population regression function is a nonlinear function of X?
Correlation
Correlation defines a relationship between two independent variables. It tells the degree to which variables move in relation to each other. When two sets of data are related to each other, there is a correlation between them.
Linear Correlation
A correlation is used to determine the relationships between numerical and categorical variables. In other words, it is an indicator of how things are connected to one another. The correlation analysis is the study of how variables are related.
Regression Analysis
Regression analysis is a statistical method in which it estimates the relationship between a dependent variable and one or more independent variable. In simple terms dependent variable is called as outcome variable and independent variable is called as predictors. Regression analysis is one of the methods to find the trends in data. The independent variable used in Regression analysis is named Predictor variable. It offers data of an associated dependent variable regarding a particular outcome.
How expected change is estimated? when the population regression
Introduction:
Consider the response variable as Y. Suppose f (X) be a function of the predictor variable X.
Suppose that the population regression function of Y is the function of X, and ε is the random error term with expectation 0 and a constant variance; then the population regression function is:
Y = f (X) + ε.
The above regression equation can be written in the form of a conditional expectation of Y for given value of X = x, as:
E (Y | X = x) = f (x).
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