Consider the following monthly revenue data for an up-and-comingtechnology company.

Month	Revenue (Thousands of Dollars)	Month	Revenue (Thousands of Dollars)
11	325325	99	797797
22	547547	1010	819819
33	539539	1111	831831
44	580580	1212	848848
55	640640	1313	860860
66	699699	1414	863863
77	697697	1515	859859
88	708708

The summary output from a regression analysis of the data is also provided.

Multiple R	0.9354095720.935409572
R Square	0.8749910670.874991067
Adjusted R Square	0.8653749950.865374995
Standard Error	57.7879539357.78795393
Observations	1515

	dfdf	SSSS	MSMS	FF
Regression	11	303,864.914286303,864.914286	303,864.914286303,864.914286	90.9925679290.99256792
Residual	1313	43,412.81904843,412.819048	3339.4476193339.447619
Total	1414	347,277.733333347,277.733333

	Coefficients	Standard Error	tt Stat	P-value
Intercept	443.92380952443.92380952	31.3995565931.39955659	14.1379005914.13790059	2.85887E-092.85887E-09
Month	32.9428571432.94285714	3.453490793.45349079	9.5390024599.539002459	3.10231E-073.10231E-07

Step3of3:

What percent of the variation in revenue is explained by the linear time trend model? Round to two decimal places, if necessary.

Answer: %