In simple linear regression the object of the researcher's interest is the population regression- the regression that describes the true relationship between the dependent variable Y and the independent variable X.
In an effort to reach a decision regarding the likely form of this relationship, the researcher draws a sample from the population of interest and, using the resulting data, computes a sample regression equation that forms the basis for reaching conclusions regarding the unknown population regression equation.
Steps in Regression Analysis
In the absence of extensive information regarding the nature of the variables of interest, a frequently employed strategy is to assume initially that they are linearly related. Subsequently analysis, then, involves the following steps;
1- Determine whether or not the assumptions underlying a linear relationship are met in the data available for analysis.
2- Obtain the equation for the line that best fits the sample data.
3- Evaluate the equation to obtain some idea of the strength of relationship and the usefulness of the equation for predicting and estimating.
4- If the data appear to conform satisfactorily to the linear model, use the equation obtained from
1- Determine whether or not the assumptions underlying a linear relationship are met in the data available for analysis.
2- Obtain the equation for the line that best fits the sample data.
3- Evaluate the equation to obtain some idea of the strength of relationship and the usefulness of the equation for predicting and estimating.
4- If the data appear to conform satisfactorily to the linear model, use the equation obtained from
the sample data to predict and to estimate.
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