This week focused on working further with linear models.

This week we worked with logistic regression model.

In this week, we added more explanatory variables and tested our model using diagnostic plots to check for assumptions of linear regression.

We did ANOVA test and a linear regression test on our dataset.

In this week, we were supposed to work with our dataset and derive two null hypotheses. After that we needed to run hypothesis test using appropriate alpha and power levels. We used two methods of testing: Neyman-Pearson and Fisher's Significance Testing.

This week's assignment's goal was to build new column pairs and create confidence intervals which gives us statistical rigor behind our conclusions.

This file helps set up the discussion for further data discovery.

In this week, we focused on knowing the importance of documentation while we do our analyis on a dataset. Many confusions can be cleared out reading the provided documentation.

This week we practiced random sampling data from a dataset. We learned how conclusions can be inaccurate if sampling is not done in a correct way.

This week on our sports dataset, we performed probability analysis and visualized some insights using group_by function.

Our dataset includes colleges and their sports revenue. We analysed which college generates how much revenue and how their revenue changes over time. Other details include difference men and women revenue.