The TIME output corresponding to HP can be found as follows:  SUMMARY OUTPUT

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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Background Information:
OBS MAKE MODEL TIME SPEED WEIGHT HP   HP TIME
1 Audi TT Roadster 8.9 133 1335 150   150 8.9
2 Mini  Cooper S 7.4 134 1240 168   168 7.4
3 Volvo C70 T5 Sport 7.4 150 1711 220   220 7.4
4 Saab Nine-Three 7.9 149 1680 247   247 7.9
5 Mercedes-Benz SL350 6.6 155 1825 268   268 6.6
6 Jaguar XK8 6.7 154 1703 290   290 6.7
7 Bugatti Veyron 16.4 2.4 253 1950 1000   1000 2.4
8 Lotus Exige 4.9 147 875 189   189 4.9
9 BMW M3 (E30) 6.7 144 1257 220   220 6.7
10 BMW 330i Sport 5.9 155 1510 231   231 5.9
11 Porsche Cayman S 5.3 171 1350 291   291 5.3
12 Nissan Skyline GT-R (R34) 4.7 165 1560 276   276 4.7
13 Porsche 911 RS 4.7 172 1270 300   300 4.7
14 Ford Shelby GT 5 150 1584 319   319 5
15 Mitsubishi Evo VII RS Sprint 4.4 150 1260 320   320 4.4
16 Aston Martin V8 Vantage 5.2 175 1630 380   380 5.2
17 Mercedes-Benz SLK55 AMG 4.8 155 1540 355   355 4.8
18 Maserati Quattroporte Sport GT 5.1 171 1930 394   394 5.1
19 Spyker C8 4.5 187 1275 400   400 4.5
20 Ferrari 288GTO 4.9 189 1161 400   400 4.9
21 Mosler MT900 3.9 190 1130 435   435 3.9
22 Lamborghini Countach QV 4.9 180 1447 455   455 4.9
23 Chrysler Viper GTS-R 4 190 1290 460   460 4
24 Bentley Arnage T 5.2 179 2585 500   500 5.2
25 Ferrari 430 Scuderia 3.5 198 1350 503   503 3.5
26 Saleen S7 3.3 240 1247 550   550 3.3
27 Lamborghini Murcielago 4 205 1650 570   570 4
28 Pagani Zonda F 3.6 214 1230 602   602 3.6
29 McLaren F1 3.2 240 1140 627   627 3.2
30 Koenigsegg  CCR 3.2 242 1180 806   806 3.2

 

The TIME output corresponding to HP can be found as follows: 

SUMMARY OUTPUT 

  

  

Regression Statistics 

Multiple R 

0.797539769 

R Square 

0.636069683 

Adjusted R Square 

0.623072172 

Standard Error 

0.945077588 

Observations 

30 

 
Coefficients 

Standard Error 

t Stat 

P-value 

Lower 95% 

Upper 95% 

 

Intercept 

7.630122482 

0.40417 

18.87848641 

1.83E-17 

6.802217 

8.458028 

HP 

-0.00643163 

0.000919 

-6.99555614 

1.32E-07 

-0.00831 

-0.00455 

The fitted regression equation is: 

TIMEi=7.6301-0.0064 x HPi + ei 

 

Estimate the regression TIMEi = β0 + β1HPi + β2WEIGHTi + ε  

 
Coefficients 

Standard Error 

t Stat 

P-value 

Lower 95% 

Upper 95% 

 

Intercept 

3.936870335 

1.29084 

3.049851421 

0.004964 

1.292704 

6.581036 

WEIGHT 

0.000776715 

0.000861 

0.902190335 

0.374652 

-0.00099 

0.00254 

 The fitted regression equation is: 

TIME= 3.9369-0.00078 x WEIGHTi - ei 

In an effort to determine whether going to class improved student academic performance, David Romer1 developed the following equation: ?? = ?(???? ,??? ) + ??  

Where: Gi = the grade of the ith student in Romer’s class (A=4, B=3, etc).  

ATTi = the percent of class lectures that the ith student attended  

PSi = the percent of the problem sets that the ith student completed  

1. What signs do you expect for the coefficients of the independent variables in this equation? Explain your reasoning.

2. Romer then estimated the equation:  

?? ̂ = 1.07 + 1.74???? + 0.60???  

? = 195, ? 2 = 0.33 

Do the estimated results agree with your expectations? 

Expert Solution
Step 1

Given information:

The data represents the values of the variables time, weight, Hp and etc.

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