Consider the following monthly revenue data for an up-and-coming technology company.Month123456789Sales DataRevenue (Thousands of Dollars)325547539580640699697708797The summary output from a regression analysis of the data is also provided.Month Revenue (Thousands of Dollars)108191183112848138601486315879169031791218908Regression StatisticsMultiple RR SquareAdjusted R SquareStandard ErrorObservations0.934825890.8738994440.8660181659.7264623218ANOVAdfSSMSFRegression 1 395,546.939628 395,546.939628 110.8828667Residual 16 57,076.004816Total 17 452,622.9444443567.250301P-valueCoefficients Standard Errort StatIntercept 470.50326797 29.37121675 16.01919566 2.83992E-11Month 28.57275542 2.713437989 10.530093385 1.33255E-08Step 2 of 3: Using the model from the previous step, predict the company's revenue for the 19th month. Round to four decimal places, if necessary.

Degree of freedom for regression is 1.Degree of freedom for residual is 16.

Answered: Month Revenue (Thousands 1 2 3 4 5 6 7…

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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Consider the following monthly revenue data for an up-and-coming cyber security company. Month 1 2 3 4 5 6 7 8 9 Revenue (Thousands of Dollars) 315 535 533 574 628 659 697 709 789 The summary output from a regression analysis of the data is also provided. Sales Data Intercept Month Regression Statistics Multiple R R Square Adjusted R Square Standard Error Month Revenue (Thousands of Dollars) 10 819 11 827 12 843 13 855 14 849 15 858 16 870 17 901 18 913 Observations 0.933348483 0.871139392 0.863085604 60.36074188 18 ANOVA df MS SS Regression 1 394,091.071207 394,091.071207 Residual 16 58,294.706570 3643.419161 Total 17 452,385.777778 F 108.16517503 t Stat P-value Coefficients Standard Error 460.94771242 29.68313147 15.52894488 4.53893E-11 28.52012384 2.742253998 10.400248796 1.58675E-08 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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Data for 50 U.S. “states" was used to examine the relationship between violent crime rate (violent crimes per 100,000 persons per year) and the independent variables of urbanization (percentage of the population living in urban areas) and poverty rate. 50 observations were examined. The Excel sheet output for the analysis of this data is shown in the Figure (with some information intentionally left blank). SUMMARY OUTPUT Regression Statistics Multiple R R Square Adjusted R Square 0.69 Standard Error 8.54 Observations 50 ANOVA df SS MS Significance F Regression 2060 54.9 0.000 Residual 47 18.77 Total 2942 Coefficients Standard Error t Stat P-value B0 B1 B2 31.9 148.2 -2.17 0.035 -4.6 1.654 2.83 0.007 39.3 13.52 2.91 0.006
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