ISOM $2600-$ Assignment $2$

In this assignment, you are given a dataset about bodyfat measure collected from a clinic. As

a nutritionist, you want to use regression model to check whether your patients are in danger

of high bodyfat. The data contains physical measurements of patients:

$$ explantory variables: Age $($in year$),$ Weight $($in pounds$),$ Height $($in inches$),$ and BMI.

$$ response variable: bodyfat $($in $\%)$

Complete the tasks. $($Notice each of the missing code to be filled is a single line

command, more than one command line will be downgraded$)$

Task $1$: Compute the correlation matrix; which variable has the strongest correlation with

bodyfat? $[5$pts$]$

Task $2$: Split the data into train and test set by random $($use $25$ as the random seed$/$state$)$

$[5$pts$]$

Task $3$: Using the train set, fit a simple regression model for bodyfat by the variable from

Task$1($the one with max correlation$)[5$pts$]$

Task $4$: Using the train set, fit a multiple regression model for bodyfat by all of the

explanatory variables $($i$.$e$.$ age, weight, height and bmi$)[5$pts$]$

Task $5$: Multicollinearity is reflected from above. Does bmi cause the problem? Briefly

explain using your understanding about bmi. $[5$pts$]$

Task $6$: Using the train set, fit a multiple regression model for bodyfat by all of the

explanatory variables except bmi. $[5$pts$]$

Task $7$: Compute the RMSE for MLR$.[5$pts$]$

Task $8$: Refit the better model from Task$7$ using the full dataset. $[5$pts$]$

Task $9$: Predict the body fat for a new patient $($using the final model from Task $8)$: age$=35,$

weight$=170$ pounds. Height$=72$ inches, BMI$=23.[5$pts$]$

Task $10$: by using residuals $($from final model$)$ outlier analysis, report those patients are in

danger $($i$.$e$.\%$ of fat is much higher than what we expected from the final model$)[5$pts$]$

After completion of all task and questions,

$$ For Jupyter Notebook$/$Jupyter lab user: Once you finish implementing all the codes $($Please DO

NOT clear the outputs$),$ please export the notebook as HTML $($see instruction in next page$)$ and

submit both your notebook and HTML to Canvas.

$$ For Google Colab user: Once you finish implementing all the codes $($Please DO NOT clear the

outputs$),$ use the link below to convert the ipynb file to HTML and submit both your notebook and

HTML to Canvas.