Question

Consider the following monthly revenue data for an up-and-coming cyber security company. sales data month revenue (thousands of dollars) month revenue (thousands of dollars) 1 326 9 789 2 539 10 817 3 527 11 829 4 579 12 845 5 635 13 857 6 683 14 851 7 699 15 855 8 707 the summary output from a regression analysis of the data is also provided. regression statistics multiple r 0.938204967 r square 0.880228559 adjusted r square 0.871015371 standard error 56.4264676 observations 15 anova df ss ms f regression 1 304,194.432143 304,194.432143 95.54006528 residual 13 41,391.301190 3183.946245 total 14 345,585.733333 coefficients standard error t stat p-value intercept 438.84761905 30.65978188 14.31346188 2.45722e-09 month 32.96071429 3.37212642 9.774459846 2.34292e-07 step 1 of 3 : write the estimated regression equation using the least squares estimates for b0 and b1 . round to four decimal places, if necessary.

Answer 1

The estimated regression equation for the monthly revenue data is ŷ = 438.8476 + 32.9607x.

Step-by-step explanation:

The estimated regression equation can be written as follows:

ŷ = 438.8476 + 32.9607x

This equation represents the relationship between the monthly revenue (ŷ) and the month (x) for the cyber security company. The intercept is 438.8476, which means that in the first month (x=1), the estimated revenue is $438,847.6. The slope is 32.9607, indicating that for every month increase, the estimated revenue increases by $32,960.7.