Week 7: Equal variance/Homoscedasticity

Hello everyone, welcome to my week 7 blog post. This week’s goal was to continue performing linear regression through my lake datasets, checking for Equal variance/Homoscedasticity. This is the second to last assumption, which means that my senior project is coming to an end. I decided to use Levene’s test to display the result of equal variance of residuals/Homoscedasticity assumption.

Levene’s test is a statistical test that allows me to see the variance of the residuals, whether my graph is above, equal or under variance by looking at the p-value. The p-value is a unit for Levene’s test that shows the strength of your data, which also means the probability to observe from existing data points.

After a few days of fixing errors and rereading documentation, I finally imported a statistics package in Python for me to run Levene’s test. My p-value result was 1.0183, which is well above 0.05, demonstrating that my residuals are in equal variance.

On Thursday, I started working on importing Quantile Quantile-plots (QQ-plots) to analyze the normal distributions of my data sets. I first started understanding the structure and its specific functions on Stackoverflow, and then I began to connect it to my datasets. Hopefully, I can finish next week and start to wrap everything up!