Claude Monet ( 1840 - 1926 ) was one of the founders of French Impressionist painting , and his artwork remains highly prized today . He was also the favorite artist of my Uncle Ben and Ben would often take me as a young child to the Museum of Modern Art in NYC to see his paintings . Monet's paintings have sold for record amounts ; for example , ' Grainstack ' was sold at auction for $81 . 4 Million in 2016 ( not bought by the Harvard Endowment ) . The goal of this problem is for you to develop a regression model that predicts the price ( dependent variable ) of a Monet painting based on its attributes ( independent variables ) . The data can be loaded into R as follows :" my data =read . CSV ( " http : / / www . datadescant . com / stat 1 04 / monet . CSV " ) a ) Create and examine a histogram of the PRICE variable . What would be the effect of taking the log of this variable ? From now on we will work with [PRICE = 108 ( PRICE ) . b ) What is the correlation between the WIDTH and HEIGHT variable . Do you think multi - collinearity is an issue ? What is multicollinearity by the way ?" C ) If you plot WIDTH ( * axis ) against HEIGHT ( Y axis ) it looks like it wants to be a strong* positive relationship but there are a few unusual points in this graph . Regress HEIGHT against WIDTH. Is there a significant relationship ?" d ) Let's incorporate a variable that combines these two variables . Define AREA = HEIGHT* WIDTH . Regress [ PRICE against AREA . Summarize the results . Is the slope significant ?* @ ) Comment on the following residual diagnostic plot . area = my dataSHE IGHT* my datasWIDTH fit = I'm ( 108 ( PRICE ) ~ area , data = my data ) plot ( fit , which = 1 ) Residuals vs Fitted 80 CV O Residuals 8 O O 298009 O O - 4 270 2 3 4 5 Fitted values Im ( log ( PRICE ) ~ area) f) Maybe we should have plotted the data first . What does a graph of [ PRICE versus AREA look like ?*