This question uses R. I am pasting the data below and a screenshot of the questi

Question

This question uses R. I am pasting the data below and a screenshot of the question below that.

Height Weight FG FT APPG
1 6.8 225 0.442 0.672 9.2
2 6.3 180 0.435 0.797 11.7
3 6.4 190 0.456 0.761 15.8
4 6.2 180 0.416 0.651 8.6
5 6.9 205 0.449 0.900 23.2
6 6.4 225 0.431 0.780 27.4
7 6.3 185 0.487 0.771 9.3
8 6.8 235 0.469 0.750 16.0
9 6.9 235 0.435 0.818 4.7
10 6.7 210 0.480 0.825 12.5
11 6.9 245 0.516 0.632 20.1
12 6.9 245 0.493 0.757 9.1
13 6.3 185 0.374 0.709 8.1
14 6.1 185 0.424 0.782 8.6
15 6.2 180 0.441 0.775 20.3
16 6.8 220 0.503 0.880 25.0
17 6.5 194 0.503 0.833 19.2
18 7.6 225 0.425 0.571 3.3
19 6.3 210 0.371 0.816 11.2
20 7.1 240 0.504 0.714 10.5
21 6.8 225 0.400 0.765 10.1
22 7.3 263 0.482 0.655 7.2
23 6.4 210 0.475 0.244 13.6
24 6.8 235 0.428 0.728 9.0
25 7.2 230 0.559 0.721 24.6
26 6.4 190 0.441 0.757 12.6
27 6.6 220 0.492 0.747 5.6
28 6.8 210 0.402 0.739 8.7
29 6.1 180 0.415 0.713 7.7
30 6.5 235 0.492 0.742 24.1
31 6.4 185 0.484 0.861 11.7
32 6.0 175 0.387 0.721 7.7
33 6.0 192 0.436 0.785 9.6
34 7.3 263 0.482 0.655 7.2
35 6.1 180 0.340 0.821 12.3
36 6.7 240 0.516 0.728 8.9
37 6.4 210 0.475 0.846 13.6
38 5.8 160 0.412 0.813 11.2
39 6.9 230 0.411 0.595 2.8
40 7.0 245 0.407 0.573 3.2
41 7.3 228 0.445 0.726 9.4
42 5.9 155 0.291 0.707 11.9
43 6.2 200 0.449 0.804 15.4
44 6.8 235 0.546 0.784 7.4
45 7.0 235 0.480 0.744 18.9
46 5.9 105 0.359 0.839 7.9
47 6.1 180 0.528 0.790 12.2
48 5.7 185 0.352 0.701 11.0
49 7.1 245 0.414 0.778 2.8
50 5.8 180 0.425 0.872 11.8
51 7.4 240 0.599 0.713 17.1
52 6.8 225 0.482 0.701 11.6
53 6.8 215 0.457 0.734 5.8
54 7.0 230 0.435 0.764 8.3

The NBA is the National Basketball Association and is the premier pro basketball league on the planet. NBA scouts and coaches make a living on identifying talented players in high school and college so that teams can draft these players before their competition. Assume you are the new data scientist for the Dallas Mavericks and have been asked to build a model that takes easily acquired statistics (height, weight, 96 field goals and % free throws) and will provide predictions on a players average points in a game. Perform a 6 step MLR on the basketball data (NBA.csv on Canvas) with the aim of predicting the average points per game for the Mavericks 3 current prospects: Mohamed Bamba Height-7ft,Weights 207 lbs % Field Goal-.50 % Free Throw : .733 Joel Berry 11 Heights 6ft weights 195 lbs % Field Goal = .426 % Free Throw-.774 Rawle Alkins Height : 6.5ftweight-220 lbs % Field Goal : .463 % Free Throw-733 Compare at least two competing models and make sure and provide prediction intervals for your predictions. Clearly label each step. If scoring is your objective, which player would you select? The data: The following data are for each player. Height = height in feet weight = weight in pounds FG- percent of successful field goals (out of 100 attempted) FT = percent of successful free throws (out of 100 attempted) APPG = average points scored per game Reference: The official NBA basketball Encyclopedia, Villard Books

Explanation / Answer

Using R

>Data=read.table(file.choose(),header=TRUE,sep=",")
>View(Data)
>names(Data)

> ##Seprate each variable
> Y=Data$APPG
> X1=Data$Height
> X2=Data$Weight
> X3=Data$FG
> X4=Data$FT
> ## Fitted model
> Model=lm( Y ~X1+X2+X3+X4)
> Model

Call:
lm(formula = Y ~ X1 + X2 + X3 + X4)

Coefficients:
(Intercept) X1 X2 X3 X4  
4.148707 -3.690499 0.009458 47.940199 11.371019  

>
> ##Prediction Interval For Mohmbad_Bamba
>
> newdata=data.frame(X1=7,X2=207 ,X3=0.50 ,X4=0.733)
> Mohmbad_Bamba=predict(Model, newdata, interval="predict")
> Mohmbad_Bamba
fit wr upr
1 12.57817 1.207806 23.94854

Prediction Interval For Mohmbad_Bamba

##Prediction Interval For Joel Berry

> newdata=data.frame(X1=6,X2=195 ,X3=0.426 ,X4=0.774)
> Joel_Berry=predict(Model, newdata, interval="predict")
> Joel_Berry
fit lwr upr
1 13.07381 1.825059 24.32255

Prediction Interval For Joel Berry is (1.825059 , 24.32255)

Prediction Interval For Rowley Alkins
> newdata=data.frame(X1=6.5,X2=220 ,X3=0.463 ,X4=0.733)
> Rowley_Alkins=predict(Model, newdata, interval="predict")
> Rowley_Alkins
fit lwr upr
1 12.77259 1.710366 23.83482
Prediction Interval For Rowley Alkins is (1.71036 , 23.83482) .

>>>>>>>>>>>>>>>> Best of luck :) <<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<

Related Questions

Navigate

• Browse (All)
• Browse T
• Subjects

---

---

Integrity-first tutoring: explanations and feedback only — we do not complete graded work. Learn more.