Instructions: The Mayor is interested in promoting transition to sustainable energy sources. She asked her office of economic development to analyzed buildings in Chifferton that convert from fossil fuels to sustainable energy sources. The partial table below reports a logistic regression analysis predicting such conversions from the square feet of the building (divided by 1000), a dummy code for residential buildings, a dummy code for retail buildings, and the age of the building expressed in years. Use your knowledge of logistic regression and and below table to answer the questions that follow.
Dependent Variable: Conversion from fossil fuels to renewable energy
Predictor Regression Weight Standard Error t p
Intercept 7.331 3.1424 2.333 0.019
Square Feet 0.5793 0.1393 4.16 <.001
Residential -3.9453 3.9897
Retail 9.3085 3.2029
Building Age -2.4147 0.5817
N = 1219
Variable Mean SD Min Max
Conversion 0.15 0.35 0.00 1.00
Square Feet* 25.87 40.80 0.50 159.67
Residential 0.45 0.50 0.00 1.00
Retail 0.30 0.46 0.00 1.00
Age 25.08 10.03 0.00 59.00
* 1/1000 time Square Feet
5 How many parameters were estimated in the logistic regression?
1219 What is the sample size?
Y Is the intercept statistically significantly different from zero? (Y/N)
What is the t value for the Residential variable?
What is the p value for the Residential variable?
What is the t value for the Retail variable?
0 What is the p value for the Retail variable?
Y Is the weight for the Building Age variable statistically significantly different from zero?
What is the degrees of freedom for the t statistics?
What is the Wald chi-square value for the Retail variable?
1 What is the degrees of freedom for the Wald chi-square?
0 What is the p value for the Wald chi-square above?