Consider the following monthly revenue data for an up-and-coming cyber security company.Month123456789AnswerThe summary output from a regression analysis of the data is also provided.Revenue (Thousands of Dollars)321542540581641700698710799Sales DataMonth Revenue (Thousands of Dollars)10821833850862858861873879Revenue=Regression StatisticsMultiple RR SquareAdjusted R SquareStandard ErrorObservations111213141516170.9210770410.8483829150.8382751163.8031272917ANOVASSFdfMSRegression 1 341,679.531863 341,679.531863 83.9334416Residual 15 61,062.585784 4070.839052Total 16 402,742.117647Coefficients Standard ErrorIntercept 467.13970588 32.36730454 14.43245622Month 28.93872549 3.158725486 9.161519612t StatStep 1 of 3: Write the estimated regression equation using the least squares estimates for bo and b₁. Round to four decimal places, if necessary.P-value3.34210E-101.56139E-07MonthTablesKeyboa

An up-and-coming cyber security company's monthly revenue data:The regression analysis's summary…

Answered: Consider the following monthly revenue…

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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Hiroshi Sato, an owner of a sushi restaurant in San Francisco, has been following an aggressive marketing campaign to thwart the effect of rising unemployment rates on business. He used monthly data on sales ($1,000s), advertising costs ($), and the unemployment rate (%) fromJanuary 2008 to May 2009 to estimate the following sample regression equation: Sales(t) = 17.51 +0.05 Advertising Costs(t-1) – 0.70 Unemployment Rate t-1 Requirement: a. Hiroshi had budgeted $620 toward advertising costs in May 2009. Make a forecast in June2009, if the unemployment rate in May 2009 was 9.1%b. What will be the forecast if he raises his advertisement budget to $700?c. Reevaluate the above forecast if the unemployment rate in May 2009 was 9.5%. Please, if possible, can you do the answer in Excel/Spreadsheet
2. Following data is amount of Assessed Value for an office building for a given floor area, number of office space, number of entrances, and the age of the offic building. Use excel to …. Obtain a Scatter plot of Floor Area as the independent and Assessed value as the dependent variable. Note the linear regression Model Predict the assessed value for 6000 sq ft. FloorArea (Sq.Ft.) Offices Entrances Age AssessedValue ($'000) 4790 4 2 8 1796 4720 3 2 12 1544 5940 4 2 2 2094 5720 4 2 34 1968 3660 3 2 38 1567 5000 4 2 31 1878 2990 2 1 19 949 2610 2 1 48 910 5650 4 2 42 1774 3570 2 1 4 1187 2930 3 2 15 1113 1280 2 1 31 671 4880 3 2 42 1678 1620 1 2 35 710 1820 2 1 17 678 4530 2 2 5 1585 2570 2 1 13 842 4690 2 2 45 1539 1280 1 1 45 433…
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Weight (lbs.) 173 184 194 214 168 220 188 188 207 167 217 Systolic Blood Pressure 132 143 153 162 154 168 137 149 159 128 166 1. What is the response variable? 2. What is the explanatory variable? 3. What is the equation of the linear regression line? 4. If person's weight is 150, what can we expect the systolic blood pressure to be?
$339,380 $41,768 A national homebuilder builds single-family homes and condominium-style townhouses. The accompanying dataset provides information on the selling price, lot cost, and type of home for closings during one month. Complete parts a through c. E Click the icon to view the house sales data a. Develop a multiple regression model for sales price as a function of lot cost and type of home without any interaction term. Create a dummy variable named "Townhouse", where it is equal to 1 when Type = "Townhouse" and 0 otherwise. Determine the coefficients of the regression equation. Sales Price = 108726 + (3.68 ) • Lot Cost + ( - 75063 )• Townhouse (Round the constant and coefficient of Townhouse to the nearest integer as needed. Round all other values to two decimal places as needed.) b. Determine if an interaction exists between lot cost and type of home and find the best model. Use a = 0.1 as the level of significance. First determine whether an interaction exists. Select the…
Suppose a firm has had the following historic sales figures. Year: 2016 2017 2018 2019 2020 Sales $2,570,000 $3,750,000 $4,380,000 $5,010,000 $5,520,000 What would be the forecast for next year’s sales using regression to estimate a trend?
The invasive diatom species Didymosphenia geminata has the potential to inflict substantial ecological and economic damage in rivers. An article described an investigation of colonization behavior. One aspect of particular interest was whether y = colony density was related to x = rock surface area. The article contained a scatterplot and summary of a regression analysis. Here is representative data. x 50 71 55 50 33 58 79 26 y 157 1934 53 27 7 10 40 12 x 69 44 37 70 20 45 49 y 274 43 176 18 48 190 30 (a) Fit the simple linear regression model to this data. (Round your numerical values to three decimal places.) y = Predict colony density when surface area = 70 and calculate the corresponding residual. (Round your answers to the nearest whole number.) colony density corresponding residual Predict colony density when surface area = 71 and calculate the corresponding residual. (Round your answers to the nearest whole number.) colony…
Listed below are paired data consisting of movie budget amounts and the amounts that the movies grossed. Find the regression equation, letting the budget be the predictor (x) variable. Find the best predicted amount that a movie will gross if its budget is $140 million. Use a significance level of a = 0.05. Budget ($)in Millions Gross ($) in Millions 39 22 118 67 77 49 124 66 12 64 128 19 6. 147 9. 118 6. 99 75 127 107 95 99 64 109 209 44 8 293 48 Click the icon to view the critical values of the Pearson correlation coefficient r. The regression equation is y =+x. (Round to one decimal place as needed.)
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.
What is the explanatory variable, what is the response variable. What is the regression equation for this data. Plot the regression line in your calculator and draw it on your graph. Interpret in context the slope. Interpret in context the y-intercept. What is the residual at 45 years of age. Plot the residual plot on the axis to the right

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