The Prostate Dataset The prostate dataset comes from a study on 97 men with pros

The Prostate Dataset
The prostate dataset comes from a study on 97 men with pros

The Prostate Dataset
The prostate dataset comes from a study on 97 men with prostate cancer who were due to receive a radical prostatectomy.
You can access the data as follows: data(prostate, package = “faraway”)
The data contain the following variables:
lcavol: log(cancer volume)
lweight: log(prostate weight)
age: age
lbph: log(benign prostatic hyperplasia amount)
svi: seminal vesicle invasion
lcp: log(capsular penetration)
gleason: Gleason score
pgg45: percentage Gleason scores 4 or 5
lpsa: log(prostate specific antigen)
Question 1
Show the first observations of the data.
Hint: use the head() function. See Lesson 8, Slide 49.
Question 2
Produce summary statistics for every variable.
Are there any missing values?
Hint: use the summary() function. See Lesson 1, Slides 50, 51
Question 3
Validate that the data contains 97 observations.
Hint: Use the nrow() function. Example: nrow(cars)
Question 4
Create a model with lpsa as the outcome variable and all the rest of the variables as predictors.
Use the AIC to select a model.
Which model did you select (mention the predictors)?
Show the summary of the selected model.
Hint: Go to Lesson 9, Slides 36 – 38.
Question 5
Start again with all the predictors and use the BIC to select a model.
Which model did you select (mention the predictors)?
Show the summary of the selected model.
Hint: Go to Lesson 9, Slide 40.
Question 6
Start again with all the predictors and use the adjusted R2 to select a model.
Which model did you select (mention the predictors)?
Show the summary of the selected model.
Hint: Go to Lesson 9, Slide 42.
Question 7
Start again with all the predictors and use the step() function to choose a model.
Which model did you select (mention the predictors)?
Show a summary of the selected model.
Hint: Go to Lesson 9, Slide 43.
please help me with these question according to the document (ppt) provided below. Thank you!

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