Question: Your are given the following linear regression: Si = β₀+β₁T_i+β₂E_i+β₃P_i+β₄H_i+u_i

Where

Si = the number of second it takes for the ith car to accelerate from 0 to 100 km per hour

Ti= a dummy equal to 1 if the ith car has a manual transmission and 0 otherwise

Ei= the coefficient of the drag of the ith car

Pi= the curb weight (in kgs) of the ith car

Hi= the bhp horsepower of the ith car

The expected signs of the coefficients are: β₁<0,β₂>0,β₃>0,β₄<0, you run the model and you obtain the following output:

Dependent Variable: S

Method: Least Squares

Included observations: 38

Variable Coefficient Std. Error t-statistic Prob.

C 9.223923 1.935467

T -O.786568 0.590256

E 7.906074 3.660143 2.160045

P 0.000408 0.000495

H -0.018971 0.0002674  -7.094425

R-squared 0.709460 Mean dependent var 8.438421

Adjusted R-squared 0.674243 S.D dependent var 2.621777

S.E. of regression 1.496382 Akaika info criterion

Sum squared resid 73.89229 Schwarz criterion

Log likelihood -66.55509 Hannan-Quinn criter

F-statistic Durbin-Watson stat 2.146911

Prob(F-statistic)   0.000000

1. Some of the t-statistics you obtained are missing from the output. Calculate them (be careful about the signs). Remember that the Null Hypothesis is:

H₀: β_i =0. Test in ∝=5% significance level. Your t-critical is given as 2.03.

2. The F value you obtained is missing. Calculate it and test if for 5% significance level. The F critical is given as 2.65.