price: Selling price in thousand dollars

baths: Number of bathrooms

bedrooms: Number of bedrooms

sqft: Total living area (total size) of the house in hundreds of square feet

age: age of each house in years

(price)i = 1 + 2(sqft)i + 3(age)i + 4(baths)i + (e)i

Therefore from table estimate of model using least sqaures = (price)i = -62.4614 + 7.9166(sqft)i -0.5772(age)i + 23.1307(baths)i

My question is how would I go about answering this question:

Based on the model above, test at the 5% level of significance the null hypothesis that a decrease in total living area by 100 square feet has the same effect on house price as a 10 year increase in the house age, other things being constant. Use a t-statistic approach and write down all the steps used to conduct your test. You can use Eviews to calculate the test statistic and obtain the t-critical value.

I was thinking setting the null, H(0): -b2 = 10b3 OR H(0): 10b3 +b2 = 0, and alterative to H(1): 10b3 + b2 ~= 0 (not equal to zero). Is this correct, am I missing anything?

What do I doing about the other variable b4, when testing only b2 and b3? You help will be much appreciated. Thanks

Dependent Variable: PRICE Method: Least Squares Date: 10/13/21 Time: 01:26 Sample: 1 900 Included observations: 900 Variable Coefficient Std. Error t-Statistic Prob. C -62.46140 9.182401 -6.802295 0.0000 SQFT 7.916557 0.348160 22.73831 0.0000 AGE -0.577210 0.138497 -4.167680 0.0000 BATHS 23.13072 5.660916 4.086038 0.0000 R-squared 0.629735 Mean dependent var 151.2452 Adjusted R-squared 0.628495 S.D. dependent var 111.4407 S.E. of regression 67.92448 Akaike info criterion 11.27910 Sum squared resid 4133906. Schwarz criterion 11.30045 Log likelihood -5071.597 Hannan-Quinn criter. 11.28726 F-statistic 507.9623 Durbin-Watson stat 1.979329 Prob(F-statistic) 0.000000