Using test data on 43 vehicles, an analyst fitted a regression to predict CityMPG (miles per gallon in city driving) using as predictors Length (length of car in inches). Width (width of car in inches), and Weight (weight of car in pounds). 8.682 Adjusted R2 R 8.658 43 k 3 CityMPG 8.826 Std. Error 2.558 Dep. Var. ANOVA table Source Regression Residual df p-value 8.35E-10 SS MS 547.3722 182.4574 27.90 255.0929 6.5408 882.4651 42 Regression output variables Intercept Length (in) Width (in) Weight (1bs) confidence interval Lipper 95% 55.9701 e.e982 Coefficients Std. Error t Stat p-Value Lower 95% VIF 39.4492 8.1678 4.830 e.ee0e 22.9283 e.8454 -0.035 e.9725 e.7379 -e.a016 -e.0934 2.669 e.1373 e.e008 -e.8463 -0.337 -0.3239 e.2314 2.552 -e.0043 -5.166 e.eeee -e.006e -e.ee26 2.836 (a) Referring to the Fstatistic and its p-value, what do you conclude about the overall fit of this model? The regression is significant O based on the Fcalc and pvalue (b) Do you see evidence that some predictors were unhelpful? (You may select more than one answer. Single cllck the box with the questlon mark to produce a check mark for a correct answer and double click the box with the question mark to empty the box for a wrong answer.) 2 Length Wiath 2 Weight (e) Do you suspect that multi-collinearity is a problem? O Yes No
Using test data on 43 vehicles, an analyst fitted a regression to predict CityMPG (miles per gallon in city driving) using as predictors Length (length of car in inches). Width (width of car in inches), and Weight (weight of car in pounds). 8.682 Adjusted R2 R 8.658 43 k 3 CityMPG 8.826 Std. Error 2.558 Dep. Var. ANOVA table Source Regression Residual df p-value 8.35E-10 SS MS 547.3722 182.4574 27.90 255.0929 6.5408 882.4651 42 Regression output variables Intercept Length (in) Width (in) Weight (1bs) confidence interval Lipper 95% 55.9701 e.e982 Coefficients Std. Error t Stat p-Value Lower 95% VIF 39.4492 8.1678 4.830 e.ee0e 22.9283 e.8454 -0.035 e.9725 e.7379 -e.a016 -e.0934 2.669 e.1373 e.e008 -e.8463 -0.337 -0.3239 e.2314 2.552 -e.0043 -5.166 e.eeee -e.006e -e.ee26 2.836 (a) Referring to the Fstatistic and its p-value, what do you conclude about the overall fit of this model? The regression is significant O based on the Fcalc and pvalue (b) Do you see evidence that some predictors were unhelpful? (You may select more than one answer. Single cllck the box with the questlon mark to produce a check mark for a correct answer and double click the box with the question mark to empty the box for a wrong answer.) 2 Length Wiath 2 Weight (e) Do you suspect that multi-collinearity is a problem? O Yes No
MATLAB: An Introduction with Applications
6th Edition
ISBN:9781119256830
Author:Amos Gilat
Publisher:Amos Gilat
Chapter1: Starting With Matlab
Section: Chapter Questions
Problem 1P
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Question
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solve a,b and c given in the image provided.
Transcribed Image Text:Using test data on 43 vehicles, an analyst fitted a regression to predict CityMPG (miles per gallon in city driving) using as predictors
Length (length of car in inches). Width (width of car in inches), and Weight (weight of car in pounds).
R2
8.682
Adjusted R2
R
8.658
n
43
0.826
2.558
k
3
Std. Error
Dep. Var.
CityMPG
ANOVA table
Source
Regression
Residual
df
MS
p-value
8.35E-10
547.3722
182.4574
27.90
255.0929
39
6.5408
802.4651
42
Regression output
variables
confidence interval
Upper 95%
Lower 95%
22.9283
-0.0934
Std. Error
Coefficients
39.4492
t Stat
4.830
-0.035
p-Value
e.0000
VIF
Intercept
Length (in)
Width (in)
Weight (1bs)
8.1678
55.9701
e.0454
0.1373
e.0902
8.2314
-0.0016
8.9725
8.7379
2.669
2.552
-0.0463
-0.337
-8.3239
-0.0043
0.0008
-5.166
0.0e0e
-0.0060
-e.0026
2.836
(a) Referring to the Fstatistic and its p-value, what do you conclude about the overall fit of this model?
The regression is significant
based on the Fcalc and p-value
(b) Do you see evidence that some predictors were unhelpful? (You may select more than one answer. Single click the box with the
questlon mark to produce a check mark for a correct answer and double cllck the box with the question mark to empty the box for
a wrong answer.)
7 Length
2 width
2 Welght
(e) Do you suspect that multi-collinearity is a problem?
Yes
No
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