In the today’s lecture we have discussed regarding the concept’s linear regression and the concepts related to work on dataset CDC Diabetes 2018.
It is a fundamental statistical method used to model the relationship between a dependent variable and one or more independent variables. It is a powerful tool for understanding and predicting how changes in the independent variables affect the dependent variable.
To study dataset given i.e., CDC Diabetes 2018, we have discussed regarding some statistical methods Median, Standard Deviation, Skewness, Kurtosis. The Dataset consists of three variables obesity, inactivity and
diabetes and There are 354 rows of data that contain information on all 3
variables. Generated a description of the %diabetes,
and inactivity data for these 1370 common data points. By this step in statistical analysis, we can understand and analyze the data.
Skewness at 0.658616 suggests that our data is slightly skewed. Imagine the data as a bell curve – if it’s perfectly symmetrical, the skewness would be 0. Positive skewness means the data is stretched out to the right. A kurtosis of about 4 indicates that our data is somewhat more peaked and has heavier tails compared to a normal distribution (which has a kurtosis of 3). This means it has more extreme values.
These concepts were discussed in the class, and they help to gain more insights in statistical analysis and to solve these CDC Diabetes 2018 kind of datasets.