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please help ASAP To model sales, Stop-it, an internet security company, records their sales for the first 10 time periods they were in business. To fit the exponential model, Stop-it uses a log base 10 transform of Sales - Log(Sales) - as the Y variable and Time as the X variable in a simple regression model. The data and the Excel output appears below in the picture. Use the exponential model to predict Stop-it Sales for Time period 11.

please help ASAP To model sales, Stop-it, an internet security company, records their sales for the first 10 time periods they were in business. To fit the exponential model, Stop-it uses a log base 10 transform of Sales - Log(Sales) - as the Y variable and Time as the X variable in a simple regression model. The data and the Excel output appears below in the picture. Use the exponential model to predict Stop-it Sales for Time period 11.

MATLAB: An Introduction with Applications
MATLAB: An Introduction with Applications
6th Edition
ISBN:9781119256830
Author:Amos Gilat
Publisher:Amos Gilat
Chapter1: Starting With Matlab
Section: Chapter Questions
Problem 1P
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please help ASAP

To model sales, Stop-it, an internet security company, records their sales for
the first 10 time periods they were in business. To fit the exponential model, Stop-it uses
a log base 10 transform of Sales - Log(Sales) - as the Y variable and Time as the X
variable in a simple regression model. The data and the Excel output appears below in the picture.

Use the exponential model to predict Stop-it Sales for Time period 11.

 

Time Sales Log(Sales) 1 8.2 0.914 2 9.4 0.973 11.8 14.2 22.2 26 45.9 110.5 159.7 10 218.8 3 4 5 6 7 8 99 1.072 1.152 1.346 1.415 1.662 2.043 2.203 2.340 Regression Statistics Multiple R R Square Adjusted R Square Standard Error Observations ANOVA Regression Residual Total Intercept Time 0.9760 0.9526 0.9467 0.1211 10 df 1 8 9 SS 2.36 0.12 2.48 Coefficients Standard Error 0.582 0.169 0.083 0.013 MS 2.36 0.01 t Stat 7.03 12.68 F 160.80 P-value 0.0001 1.41E-06 Significance F 1.41E-06 T
Transcribed Image Text:Time Sales Log(Sales) 1 8.2 0.914 2 9.4 0.973 11.8 14.2 22.2 26 45.9 110.5 159.7 10 218.8 3 4 5 6 7 8 99 1.072 1.152 1.346 1.415 1.662 2.043 2.203 2.340 Regression Statistics Multiple R R Square Adjusted R Square Standard Error Observations ANOVA Regression Residual Total Intercept Time 0.9760 0.9526 0.9467 0.1211 10 df 1 8 9 SS 2.36 0.12 2.48 Coefficients Standard Error 0.582 0.169 0.083 0.013 MS 2.36 0.01 t Stat 7.03 12.68 F 160.80 P-value 0.0001 1.41E-06 Significance F 1.41E-06 T
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