Create a scenario that would require the use of simple linear regression. This means both variables must be interval or ratio scale data. Examples include looking at:
the relationship between the number of calories burned in a workout and the rating of hunger after the workout
the relationship between people’s satisfaction with life scores and their number of hours per year of volunteer work
The relationship between the number of hours of sleep people gets per night and your choice of one test of cognitive function or ratings of general physical wellness (use validated measures for examples like these). Google for “validated test of…” to find examples of real scales or measurement tools.
write your research question and matching hypothesis (What is the relationship between X and Y.” Null hypothesis, “there is no relationship between X and Y”.)
generate fake/mock data, enter that correctly into SPSS – use at least 30 data points per variable.
calculate the useful descriptive statistics, histograms for each variable if you like, a scatterplot of your data that includes the regression line, and report both the Pearson correlation coefficient (r) and regression coefficient (coefficient of determination, R2.), and the associated p-values.
Verify that your data meet the assumptions of using regression (normally distributed, no outliers) by looking at your descriptive statistics.
Verify that the “residuals”