Read in the MIS470housingtesting(460x25.csv file into an R testing data frame. The MIS470housingtesting(460x25 ).csv file contains 460 records and 25 quantitative explanatory variables describing many aspects of residential homes in Ames, IA.

1. Calculate the summary statistics of minimum, maximum, mean, median, and standard deviation for the sales price variable of the testing data set.
2. Plot a histogram for the distribution of the sales price variable of the testing data set. Combine the two data sets (training and testing) into a single data set. This can be done in R by using the function combine(). Create a histogram of sale prices for the combined data set.
3. Using only the training data set, fit a linear regression model using all the explanatory variables and SalePrice as the response variable.
4. Remove all the rows with missing values (NA) from the testing data set. The function is complete. cases() can be used. Using only the first 20 rows from the testing data set, predict the sale price. The R function predicts () can perform this task. You should have 20 predicted sale prices.