11th Sept 2023

In today’s class on Simple Linear Regression, several key concepts were covered to better understand the analysis of regression models. Firstly, the discussion delved into skewness, which assesses the asymmetry in the distribution of residuals. Identifying skewness is crucial for assessing the reliability of a regression model and making necessary adjustments. Secondly, kurtosis was highlighted as a metric to assess the distribution of residuals and detect whether they deviate from a normal distribution. Severe kurtosis can impact the validity of regression results. Lastly, heteroscedasticity was discussed, emphasizing how it relates to the varying spread of residuals as independent variables change. Heteroscedasticity can lead to incorrect assessments of statistical significance, affecting parameter estimations and statistical power. The class also introduced the fundamentals of linear regression, a simple supervised learning approach used to predict a continuous dependent variable based on one or more independent variables.