# Linear Regression

Statistics Solutions provides a data analysis plan template for the linear regression analysis.  You can use this template to develop the data analysis section of your dissertation or research proposal.

The template includes research questions stated in statistical language, analysis justification and assumptions of the analysis.  Simply edit the blue text to reflect your research information and you will have the data analysis plan for your dissertation or research proposal.

Data Analysis Plan: Linear Regression

Copy and paste the following into a word document to use as your data analysis plan template.

Research Question:

RQ: Does independent variable predict dependent variable?

HoIndependent variable does not predict dependent variable.

HaIndependent variable predicts dependent variable.

Data Analysis

To examine the research question, a linear regression will be conducted to investigate whether or not independent variable predicts dependent variable.  A linear regression is an appropriate analysis when the goal of research is to assess the extent of a relationship between a dichotomous or interval/ratio predictor variable on an interval/ratio criterion variable.  In this case, the predictor variable is the independent variable and the criterion variable(s) is the dependent variable.  The following regression equation will be used: y = b1*x + c; where y = estimated dependent variable, c = constant, b = regression coefficient and x = independent variable .  The F-test will be used to assess whether the independent variable predicts the dependent variable.  R-squared will be reported and used to determine how much variance in the dependent variable can be accounted for by the independent variable.  The t-test will be used to determine the significance of the predictor and beta coefficients will be used to determine the magnitude and direction of the relationship.  For statistically significant models, for every one unit increase in the predictor, the dependent variable will increase or decrease by the number of unstandardized beta coefficients.  The assumptions of a linear regression —linearity and homoscedasticity—will be assessed.  Linearity assumes a straight line relationship between the predictor variables and the criterion variable and homoscedasticity assumes that scores are normally distributed about the regression line.  Linearity and homoscedasticity will be assessed by examination of a scatter plots.

Reference