ASSIGNMENT 2 - Statistics Project Example

ii) To the simple regression model in part (i), add the variables , rooms, baths, and age, where intst is distance from the home to the interstate, area is square footage of the house, land is the lot size in square feet, rooms is total number of rooms, baths is number of bathrooms, and age is age of the house in years. Now, what do you conclude about the effects of the incinerator? Explain why (i) and (ii) give conflicting results. The coefficient for the incinerator is 0.05539 showing a decrease from the previous value of 0.

36488; addition of more relevant variables to the model results to a decrease in the coefficient value of the initial variable. Yes the square of  is significant when you add it to the model from part (iii); this is because we observe an increase in the value of adjusted R-squared from 0.7475 in part (ii) to 0.7642 in part (iii) a) Most households use air conditioning powered by electricity to cool down the house but use other forms of energy (gas, oil, etc.) to warm up the house. Given this piece of information, what would you predict for the sign and significance of the coefficients if electricity usage were regressed on a households heating requirements and cooling requirements?

Run a regression of electricity usage (kwh) on heating degree-days (hd16) and cooling degree-days (cd16). Are your results consistent with your predictions? Test whether the coefficients are statistically different than each other at 1%. We would expect the electricity usage to go up during cooling as such the sign for the cooling would be positive. On the contrary, we would expect the electricity usage to come down during the heating and as such the sign for the heating is expected to be negative.

The p-value for the test is 0.000 (a value less than 1% significance level), we therefore reject the null hypothesis and conclude that