make predictions using the predict function passing in the model2 and the predictors dataframe you just made

INSTRUCTIONS TO CANDIDATES

ANSWER ALL QUESTIONS

Here is some data for Diamonds

Run this first to set nice print outsoptions("scipen"=100, "digits"=4)

carat<-c(0.71,0.72,0.33,1.52,1.23,0.54,0.32,1.31,1.11,1.03,0.56,1.29,0.53,1.7, 0.3) clarity<-c(3,5,4,4,4,6,5,3,2,2,5,2,5,5,4)

price<-c(2554,2691,854,9576,9116,1829,828,8767,5648,4283,1131,5601,1577,11257,615)

df<-data.frame(CODE)

print out info for the dataframe using str

CODE

print out the first few rows in the dataframe use head

CODE

create scatterplots of the columns plot(df, upper.panel=NULL, pch=19)

CODE

create correlation matrix for df using the code cor(df)

CODE

run a linear regression model using carat and clarity to predict price

model1<-lm(CODE,data=df) summary(model1)

Test for overall significant of linear relationship:

overall p-value = ????

Clarity:

p-value for clarity = ???

Carat:

p-value for carat = ???

Eliminate Clarity from the model:

run a new linear regression model using carat to predict price model2<-lm(CODE,data=df) summary(model2)

Fill in the ??? below with the right value:

Test for overall significant:

overall p = ???

The regression equation is this:

price =???(carat) + ??? The standard error is: std error = ???

Make predictions when:

carat is 1.3

carat is 0.6

carat is 1.8

 

To do this:

assign a vector carat to hold the predictors above (replace CODE below) create a dataframe called predictors using the carat vector

print out the predictors dataframe

 

carat<-CODE

predictors <- data.frame(carat) predictors

 

Now lets do the predictions:

make predictions using the predict function passing in the model2 and the predictors data frame you just made (pass 2 arguments where you see CODE) print out the predictions

predictions<-predict(CODE) predictions

Fill in the correct answers below:

Prediction when carat is 1.3: ??? Prediction when carat is 0.6: ??? Prediction when carat is 1.8: ???

Attachments:

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