The managers of an electric utility wish to examine the relationship between temperature and electricity use in the utility’s service region during the summer months. In particular, the managers wish to be able to predict total electricity use for a day from the maximum temperature that day. The bivariate data below give the maximum temperature (in degrees Fahrenheit) and the electricity use (in thousands of kilowatt hours) of 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 , in which denotes the maximum temperature and denotes the electricity use. This line is shown in the Figure 1 scatter plot. Temperature, x (in degrees Fahrenheit) Electricity use, y (in thousands of kilowatt hours) 88.5 316.5 93.4 355.6 73.4 294.9 73.4 231.9 80.4 303.5 86.4 348.0 70.5 275.0 94.9 358.8 83.4 320.5 83.8 301.2 96.8 307.2 83.5 263.2 70.3 297.6 91.3 302.7 96.1 338.2 98.5 384.6 Send data to Excel x 70 75 80 85 90 95 100 y 225 250 275 300 325 350 375 400 Figure 1 Based on this information, answer the following: 1. Fill in the blank: For these data, temperature values that are less 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 use. Choose onegreater thanless than 2. According to the regression equation, for an increase of one degree Fahrenheit in temperature, there is a corresponding increase of how many thousands of kilowatt hours in electricity use? 3. From the regression equation, what is the predicted electricity use (in thousands of kilowatt hours) when the temperature is 83.4 degrees Fahrenheit? (Round your answer to at least one decimal place.) 4. What was the observed electricity use (in thousands of kilowatt hours) when the temperature was 83.4 degrees Fahrenheit? Clear Undo Help Please see attached