Week 6: Mean of Residuals

Hello everyone, welcome to my week 6 blog. This week’s goal was to continue performing linear regression on our data checking for the following Mean of Residuals Assumptions.

The mean of residuals is typically a horizontal line crossing through the residuals plot, checking for the two following assumptions: Bias and Symmetry. A non-zero residual plot means that the model is biased. This indicates that the model can either be overestimating or underestimating towards both dependent and independent variables. This error could be caused by missing significant data entries or incorrect model forms, and with other errors. On the other hand, symmetry checks for the spread of the data. If you see a graph that evenly spreads above and below the mean of the residuals line, this shows that the model’s residuals are randomly distributed and contain no bias.

In my data, I successfully inserted my mean of the residuals function into my residuals graph, marked in a dotted red. This line spreads horizontally across the graph at 0, while containing a good amount of data both above and below. Therefore, we can conclude that my data has no significant bias. I was thinking of using Levene’s statistical test to check the symmetry and variance of the data. However, as I was importing the statistical package in Python, I got into a systematic error that I have not fixed yet. Therefore, I will continue fixing the error and hopefully get the result of Levene’s test by next week as well as starting to analyze equal variance.