=====Forecasting Team Meeting - September 14, 2020=====
**Attended: Josh, Keola**

=====Updates=====
  * Started Section 7 of the course: Multiple Linear Regression
  * Topics learned: Multiple Linear Regression intuition, understanding p-values, null and alternative hypothesis, statistical significance, backwards elimination, forward selection, implementation in python

=====Problems=====
  * COVID-19 forced today's lab hours to be online


=====Reminders=====
  * P-values indicate the probability that your results would appear IF you were to take the null hypothesis to be true
  * Low p-value means we can reject the null hypothesis (no correlation) and accept the alternate hypothesis (correlation exists)
  * We use p-values to decide which independent variables (predictors) really matter when it comes to creating a prediction model for our data
  * In backwards elimination, we get rid of all of the highest p-value predictors until we are left with predictors who's p-values are small enough to be considered statistically significant. 
  * In forward selection, we start with the lowest p-value predictors and make our way up until we hit our threshold of statistical significance.