Consider the following monthly revenue data for an up-and-coming technology company.
Sales Data Consider the following monthly revenue data for an up-and-coming technology company. Sales Data Month | Revenue (Thousands of Dollars) | Month | Revenue (Thousands of Dollars) | 1 | The summary output from a regression analysis of the data is also provided. Regression Statistics Multiple R | 0.927968374 | | Adjusted R Square | 0.853410043 | | Standard Error | 62.99950902 | Stat | P-value | Intercept | 479.44736842 | 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. Step 2 of 3: Using the model from the previous step, predict the company's revenue for the 18th month. Round to four decimal places, if necessary. Step 3 of 3: What percent of the variation in revenue is explained by the linear time trend model? Round to two decimal places, if necessary. | | | |
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The summary output from a regression analysis of the data is also provided.
Regression Statistics Multiple R | 0.927968374 |
|
Adjusted R Square | 0.853410043 |
|
Standard Error | 62.99950902 |
Stat | P-value |
Intercept | 479.44736842 |
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.
Step 2 of 3: Using the model from the previous step, predict the company's revenue for the 18th month. Round to four decimal places, if necessary.
Step 3 of 3: What percent of the variation in revenue is explained by the linear time trend model? Round to two decimal places, if necessary.