(b) Use a simple regression model with outside temperature as the causal variable to develop an...

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(b) Use a simple regression model with outside temperature as the causal variable to develop an equation to account for the relationship between outside temperature and hourly sales in the data. If required, round your answers to three decimal places. For subtractive or negative numbers use a minus sign even if there is a + sign before the blank. (Example: -300) Do not round intermediate calculation. Hourly Sales = 22.264 + 0.3977 Temperature Based on this model, compute an estimate of hourly sales for today from 2:00 P.M. to 3:00 P.M. if the temperature at 2:00 P.M. is 93F. If required, round your answers to two decimal places. $ 59.25 (c) Use a multiple linear regression model with the causal variable outside temperature and dummy variables as follows to develop an equation to account for both seasonal effects and the relationship between outside temperature and hourly sales in the data in the data: Hour1 = 1 if the sales were recorded between 1:00 P.M. and 2:00 P.M., 0 otherwise; Hour2 = 1 if the sales were recorded between 2:00 P.M. and 3:00 P.M., 0 otherwise; Hour8 = 1 if the sales were recorded between 8:00 P.M. and 9:00 P.M., 0 otherwise. Note that when the values of the 8 dummy variables are equal to 0, the observation corresponds to the 9:00 P.M. to 10:00 P.M. hour. If required, round your answers to three decimal places. For subtractive or negative numbers use a minus sign even if there is a + sign before the blank. (Example: -300) Do not round intermediate calculation. Hourly Sales = 21.8336 + 0.4498 Hour4 + 5.068 Hour5 + 5.488 Hour1 + -5.672 Hour7 + 1.928 Temperature + -5.2328 Hour6 + 7.285 Hour2 + -6.2917 Hour3 + 7.402 Hour8 Based on this model, compute an estimate of hourly sales for today from 2:00 P.M. to 3:00 P.M. if the temperature at 2:00 P.M. is 93F. If required, round your answers to two decimal places. 57.99 (b) Use a simple regression model with outside temperature as the causal variable to develop an equation to account for the relationship between outside temperature and hourly sales in the data. If required, round your answers to three decimal places. For subtractive or negative numbers use a minus sign even if there is a + sign before the blank. (Example: -300) Do not round intermediate calculation. Hourly Sales = 22.264 + 0.3977 Temperature Based on this model, compute an estimate of hourly sales for today from 2:00 P.M. to 3:00 P.M. if the temperature at 2:00 P.M. is 93F. If required, round your answers to two decimal places. $ 59.25 (c) Use a multiple linear regression model with the causal variable outside temperature and dummy variables as follows to develop an equation to account for both seasonal effects and the relationship between outside temperature and hourly sales in the data in the data: Hour1 = 1 if the sales were recorded between 1:00 P.M. and 2:00 P.M., 0 otherwise; Hour2 = 1 if the sales were recorded between 2:00 P.M. and 3:00 P.M., 0 otherwise; Hour8 = 1 if the sales were recorded between 8:00 P.M. and 9:00 P.M., 0 otherwise. Note that when the values of the 8 dummy variables are equal to 0, the observation corresponds to the 9:00 P.M. to 10:00 P.M. hour. If required, round your answers to three decimal places. For subtractive or negative numbers use a minus sign even if there is a + sign before the blank. (Example: -300) Do not round intermediate calculation. Hourly Sales = 21.8336 + 0.4498 Hour4 + 5.068 Hour5 + 5.488 Hour1 + -5.672 Hour7 + 1.928 Temperature + -5.2328 Hour6 + 7.285 Hour2 + -6.2917 Hour3 + 7.402 Hour8 Based on this model, compute an estimate of hourly sales for today from 2:00 P.M. to 3:00 P.M. if the temperature at 2:00 P.M. is 93F. If required, round your answers to two decimal places. 57.99