Use the train.csv file to complete this case study which contains home sales data for Ames, Iowa in 2009. You will be using this dataset as you learn multiple regression (and later, regularization) to predict home sales prices from home features (predictors) in a competition against other MGSC 291 students. To find the best model for prediction, you need to explore and understand the data available to you. This purpose of this case is to begin this exploration in preparation for using the data later for predictions. data set file https://drive.google.com/file/d/1NjWzEM-vURReFKqQ22cS5SSYailgaR00/view?usp=sharing

# Q6 # There are 4 rating categories for kitchen quality:

###### fair, average, good, and excellent.

###### Use the sum() function on a logical vector to count

###### how many homes have kitchen quality rated as "Excellent"?

###### Hint: use the summary() function on the kitchen quality column

###### to see how this quality category is spelled.

sum(train$KitchenQual == "Excellent")

# Q7 # Use the sum() function on a logical vector to count

###### how many homes sold for more than $350,000

###### and have "Excellent" as their kitchen quality.

sum((train$KitchenQual == "Excellent") & (train$SalePrice > 350000))

# Q8 # Use the mean() function to find the average sales price

###### for a home with kitchen quality rating of "Excellent".

mean(train$SalePrice[train$KitchenQual == "Excellent"])

# Q9 # Use the mean() function to find the average sales price

###### for a home with kitchen quality rating of "Excellent"

###### or has a garage for more than 2 cars.

mean(train$SalePrice[(train$KitchenQual == "Excellent") | (train$GarageCars > 2)])

# Q10 # Use the which() function on a logical vector to find

###### the observations number(s) for a home with a

###### kitchen quality rating of "Excellent"

###### and sales price of at most $195,000.

which((train$KitchenQual == "Excellent") & (train$SalePrice <= 195000))

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