In R please provide the code and explanation for the following: * the first three parts were previously answered *      iv. Create 4 different data frames, one for the data corresponding to each of the 4factors. How many observations are there for each diet?   v. Check the normality assumption for each subset by creating qq plots. Makesure each plot has an appropriate title.   vi. What is the sample variance for each diet? Do you think that the assumption ofcommon variance holds? Why or why not? How could you formally test this?

In this assignment you will implement linear regression model and evaluate their performance on the California house price data set. (housing.csv) Apply the codes and save them in a seperate .py or .ipynb file DO NOT PUT THE CODE IN YOUR REPORT DOCUMENT, only present your output metrics as well as requested graphs and personal comments in the report. Name the report and code files with surname_studentID_section. You will submit a report and a .py/.ipynb file. Only use the data set version provided with the assignment do not download other versions or use the ready made version in google colab. In the assignment you will do the following: - apply linear regression on each individual numerical feature (drop features : 'ocean_proximity' ‘longitude', 'latitude') - output the coefficients and your self implemented error measures: sum of squared error SSE, mean squared error MSE, use split percentage cross validation with 30% test size and shuffle as True refer to documentations during…

Using R programming: Create a function that randomly picks up two columns from a data frame and does the following: if both columns are of character values, test if they are independent; if both columns are numeric, create a data plot to show their relationship trends with a regression line attached; if one is numeric and the other character, test if the numeric column is related to the character column