I have analyzed the given data set of CDC Diabetes 2018 which consist of variables % obesity , %Inactivity and %diabetes and I have eliminated the not null values and I got a new dataset with that dataset we can build a model to predict % diabetes using both %inactivity and %obesity as factor
I have learnt about
Skewness :A statistical metric known as skewness is used to assess how asymmetrical a probability distribution is. Finding out if a dataset is more concentrated on one side of the mean than the other is made easier by this. An upward tail is suggested by positive skewness and a downward tail by negative skewness. Perfect symmetry is indicated by a skewness value of zero.
Kurtosis: This statistical measure determines the “tailedness” of a probability distribution, indicating whether the data show heavy or light tails in comparison to a normal distribution. A high peak and heavy tails are indicated by positive kurtosis , whereas a flat peak and light tails are indicated by negative kurtosis (platykurtic). What defines a distribution is a zero kurtosis.
Heteroscedasticity: Non-constant variance in the residuals is referred to as heteroscedasticity in regression analysis. To put it another way, it shows that there are differences in the dispersion of errors between the levels of the independent variable. Since heteroscedasticity contradicts the concept of continuous variance, detecting and treating it is essential to maintaining the reliability of regression models. Robust model interpretation and accurate statistical analysis depend on this understanding.