The managers of an electric utility wish to service region during the summ sh to examine the relationship between temperature day from the maxim e summer months. In particular, the m temperature and electricity use in the utility maximum temperature that day. Fahrenheit) and the electricity use (in r, the managers wish to be able to predict total electricity . The bivariate data below give the maximum te ty use for thousands of kilowatt hours) . m temperature ture (in degrees electricity generated and sold for a random sample of sixteen summer days. A best-fitting line for the data, obtained from least-squares regression, is given by the Figure 1 scatter plot. 6 + 2.61x , in which x denotes the maximum temperature and y denotes the electricity use. This line is shown in Temperature, x (in degrees Electricity use, y BE Fahrenheit) (in thousands of kilowatt hours) 74.4 287.1 95.1 349.0 76.9 292.3 94.3 339.8 97.8 340 . 1 98.6 384.3 83.1 322.4 83.5 259.3 70.3 308.4 73.8 236.8 96.4 302.9 250- 69.5 267.9 81.6 301.4 83.6 363.1 90.8 316.4 90.3 Figure 1 Send data to Excel Based on this information, answer the following: 1. Fill in the blank: For these data, temperature values that are greater than the mean of the temperature values tend to be paired with values for electricity use that are_ _the mean of the values for electricity Choose one V use. 2. Fill in the blank: According to the regression equation, for an increase of one degree Fahrenheit in temperature, there is a corresponding _ of 2.61 thousands of kilowatt hours in electricity Choose one use. 3. From the regression equation, what is the predicted electricity use (in thousands of kilowatt hours) when the temperature is 83.1 degrees Fahrenheit? (Round your answer to at least one decimal place. ) 4. What was the observed electricity use (in thousands of kilowatt