My first Mth 522 post which I learned examining residuals in linear models highlights the importance of assessing the reliability of statistical models used in analyzing health data. This can help researchers and analysts ensure the validity of their findings and make informed decisions based on the results. I found the importance of examining residuals in any linear model, highlighting the need to assess the reliability of the linear model used in the analysis. The residuals versus predicted values from a linear model are plotted to assess heteroscedasticity, indicating the reliability of the linear model. The heteroscedasticity of the linear model is used to analyze the relationship between inactivity and diabetes, indicating that the linear model may not be reliable. I found the importance of examining residuals in linear models to assess the reliability of the model and the linear model used in the analysis may not be reliable due to heteroscedasticity. The relationship between inactivity and diabetes suggests alternative methods for testing heteroscedasticity when the residuals are not normally distributed. Descriptive statistics such as median, mean, standard deviation, skewness, and kurtosis are calculated for the inactivity data. Quantile-quantile plots are also used to assess deviation from normality. Professor talks about kurtosis as a measure of the shape of the distribution of the inactivity data, The kurtosis of the inactivity data is about 2, which is somewhat lower than the value of 3 for a normal distribution. The kurtosis of a distribution measures the heaviness of the tails and the peakedness of the distribution. A kurtosis value of 3 indicates a normal distribution, while values less than 3 indicate a less peaked distribution. I learned that kurtosis is one of the descriptive statistics to assess the deviation of the inactivity data from normality.