In the `Credit` data in the `ISLR` package it contains 400 customers and information on their credit history. Build a model using all the predictors (11 variables) to better understand factors that...

Solution
1) All independent variables
ID – Identity number
Income – Income of an individual in $1000’s
Limit – Card limit
Rating – Credit Rating which is given by the bank
Cards – Number of credit cards a person is holding
Age – Age of an individual (in years)
Education – Number of years spend in education
Gender – A factor with levels Male and Female
Student - A factor with No and Yes indicating whether the individual was a student if yes then labelled as yes or vice versa.
Married – A factor with levels No and yes indicating whether the individual was married if yes then labelled as yes or vice versa.
Ethnicity –African American, Asian and Caucasian indicating the individual’s ethnicity that is from which a person belongs.
2) Income, limit, Rating, Age, student are chosen as variables that will be included in the regression model. These variables originate from the demographic dataset and are not implied to be used in the dataset. The person coming from a foreign country does not tell us what its average credit card balance is. We don't have to include these two variables in our model because they don't make sense. The issue of gender inequality on a pay scale is a very sensitive one, and we have seen a lot of discussion around it. Nevertheless, we should not make this discussion part of our analysis. Based on the facts, the prediction will be made. The same goes for education, which is also part of demographic data. An individual's number of years of education does not determine their level of skill. Therefore, we should not include that in the model.  We cannot determine the average balance of each card based on the number of cards a person holds. Income and the limit of the credit card holder also important factor to decide the balance of the credit card. The people with more income tend to have more credit card balance.
3) The above variables are chosen because these variables have strong impact on the response variable that is the average credit card balance. The Age of the person would help us to tell the relationship between the credit balance and the age, the rating of the credit card is we know the important and helps the bank to know the person usage of the card. If the credit card holder is a student then and is having a good credit card balance this seems a contrary scenario. To know what type of relationship presence between the two variables it is important to take the variable.
4) The linear regression...