forward_aic <- step (1m (mpg 1, data = mtcars), direction = "forward", scope formula (fit.full), k= 2, trace= 0) In the command above, what does k mean?

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Chapter4: Equations Of Linear Functions
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A statistician is employed by a car industry. They wish to establish the relationship between a set of
predictors and an outcome variable, which is mpg (miles per gallon) in this case. The original dataset
is a data frame of 32 observations and 11 variables. Below is a snippet of the dataset.
4
Mazda RX4
Mazda RX4 Wag
Datsun 710
mpg cyl disp hp drat
wt qsec vs am gear carb
6 160.0 110 3.90 2.620 16.46 01
6 160.0 110 3.90 2.875 17.02 01
4 108.0 93 3.85 2.320 18.61 1
4
4
1
4
1
Hornet 4 Drive
3
1
3
2
Hornet Sportabout
Valiant
0
3 1
21.0
21.0
22.8
21.4 6 258.0 110 3.08 3.215 19.44 1 0
18.7 8 360.0 175 3.15 3.440 17.02 0 0
18.1 6 225.0 105 2.76 3.460 20.22 1
14.3 8 360.0 245 3.21 3.570 15.84 0
24.4 4 146.7 62 3.69 3.190 20.00 1
22.8 4 140.8 95 3.92 3.150 22.90
19.2
17.8
16.4
Duster 360
3
4
Merc 240D
Merc 230
1
4
2
6 167.6 123 3.92 3.440 18.30
1
4
4
Merc 280
Merc 280C
6 167.6 123
4
3.92 3.440 18.90 1
3.07 4.070 17.40 0
Merc 450SE
8 275.8 180
0
3
Merc 450SL
17.3
8 275.8 180
3.07 3.730 17.60 0
0
Merc 450SLC
15.2 8 275.8 180 3.07 3.780 18.00 0
0
3
In the R output below, mpg is miles per gallon, wt is weight, cyl is number of
cylinders, hp is gross horsepower.
Call:
1m (formula mpg wt cyl hp, data
mtcars)
Coefficients:
(Intercept)
wt
cyl
38.75179
-3.16697
-0.94162
-0.01804
Call:
1m (formula mpg wt + cyl, data = mtcars)
Coefficients:
wt
(Intercept)
39.686
cyl
-3.191
-1.508
By looking at the R output above, state which case corresponds to the AIC which
to the BIC. Explain your reasoning.
Write down the model that corresponds to the R command below.
forward_aic <- step(1m (mpg 1, data = mtcars), direction = "forward",
scope = formula (fit.full), k = 2, trace = 0)
In the command above, what does k mean?
hp
oooo0
ooo
4
3
Transcribed Image Text:A statistician is employed by a car industry. They wish to establish the relationship between a set of predictors and an outcome variable, which is mpg (miles per gallon) in this case. The original dataset is a data frame of 32 observations and 11 variables. Below is a snippet of the dataset. 4 Mazda RX4 Mazda RX4 Wag Datsun 710 mpg cyl disp hp drat wt qsec vs am gear carb 6 160.0 110 3.90 2.620 16.46 01 6 160.0 110 3.90 2.875 17.02 01 4 108.0 93 3.85 2.320 18.61 1 4 4 1 4 1 Hornet 4 Drive 3 1 3 2 Hornet Sportabout Valiant 0 3 1 21.0 21.0 22.8 21.4 6 258.0 110 3.08 3.215 19.44 1 0 18.7 8 360.0 175 3.15 3.440 17.02 0 0 18.1 6 225.0 105 2.76 3.460 20.22 1 14.3 8 360.0 245 3.21 3.570 15.84 0 24.4 4 146.7 62 3.69 3.190 20.00 1 22.8 4 140.8 95 3.92 3.150 22.90 19.2 17.8 16.4 Duster 360 3 4 Merc 240D Merc 230 1 4 2 6 167.6 123 3.92 3.440 18.30 1 4 4 Merc 280 Merc 280C 6 167.6 123 4 3.92 3.440 18.90 1 3.07 4.070 17.40 0 Merc 450SE 8 275.8 180 0 3 Merc 450SL 17.3 8 275.8 180 3.07 3.730 17.60 0 0 Merc 450SLC 15.2 8 275.8 180 3.07 3.780 18.00 0 0 3 In the R output below, mpg is miles per gallon, wt is weight, cyl is number of cylinders, hp is gross horsepower. Call: 1m (formula mpg wt cyl hp, data mtcars) Coefficients: (Intercept) wt cyl 38.75179 -3.16697 -0.94162 -0.01804 Call: 1m (formula mpg wt + cyl, data = mtcars) Coefficients: wt (Intercept) 39.686 cyl -3.191 -1.508 By looking at the R output above, state which case corresponds to the AIC which to the BIC. Explain your reasoning. Write down the model that corresponds to the R command below. forward_aic <- step(1m (mpg 1, data = mtcars), direction = "forward", scope = formula (fit.full), k = 2, trace = 0) In the command above, what does k mean? hp oooo0 ooo 4 3
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