these results.
a. The regression says that if a city’s average low temperature is 1 degree higher than
some other city’s, their average high temperatures are also likely to be 1 degree
different. The high R2 and low RMSE are inconsistent and suggest a serial correlation
problem.
b. The regression says that if a city’s average low temperature is 10 degrees higher than
some other city’s, their average high temperatures are also likely to be 10 degrees
different and that the average difference between average daily high temperatures and
average daily low temperatures is 21 degrees. The model explains 98% of the variation
in average daily high temperatures, and the standard error of the estimate is quite small.
c. The regression says that if a city’s average low temperature is 1 degree higher than
some other city’s, their average high temperatures are also likely to be 1 degree
different. The high R2 and low RMSE are inconsistent and suggest a multicollinearity
problem.
d. The regression says that if a city’s average low temperature is 10 degrees higher than
some other city’s, their average high temperatures are also likely to be 10 degrees
different and that the average difference between average daily high temperatures and
average daily low temperatures is 12 degrees. The regression explains all the variation
in average daily high temperatures.
e. None of the above.
If a payoff is equally likely to be $1, $2, $3, $4, or $5, the coefficient of variation is:
a. 0
b. 21/2/3.
c. 2/3.
d. 2
e. 10/3.