Consider the following monthly revenue data for an up-and-coming cyber security company.Month123456789Sales DataMonthRevenue (Thousands of Dollars)315105351153312574136281465915697167091778918The summary output from a regression analysis of the data is also provided.Regression StatisticsMultiple RR SquareAdjusted R SquareStandard ErrorObservationsRevenue (Thousands of Dollars)8198278438558498588709019130.9333484830.8711393920.86308560460.3607418818ANOVAdfSSMSRegression 1 394,091.071207 394,091.071207Residual 16 58,294.706570 3643.419161Total 17 452,385.777778F108.16517503t StatP-valueCoefficients Standard ErrorIntercept 460.94771242 29.68313147 15.52894488 4.53893E-11Month 28.52012384 2.742253998 10.400248796 1.58675E-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: Intercept Month Coefficients Standard…

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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The results below represent the logit regression on the probability of taking out a loan. Assume that the equation is statistically significant. Coefficients: Estimate Std. Error z value Pr (>|z|) wwwwwwwwww (Intercept) -11.9761754 1.5197691 -7.880 3.27e-15 *** 1.689531 0.106523 15.861 < 2e-16 *** 0.000664 0.000523 1.271 0.203658 0.686931 0.068796 0.053941 0.002416 -0.070995 0.056987 -1.246 0.212841 0.083101 0.056694 1.466 0.14271 0.131343 0.036441 Age CCAvg Education Experience Family Income Mortgage 9.9852e-16 *** 22.482e-16 *** Select one: O a. Increases probability of taking out a loan O b. Decreases probability of taking out a loan O c. Cannot be determined 3.604 0.000313 *** What is the effect of increasing Age (in years) on the probability of taking out a loan? Direction not magnitude.
Please answer it as soon as possible! Thank you!
Comment on the coefficient of CPI in the model calculated WHERE B0=0.47111 and B1= -0.05304
Suppose seafood price and quantity data for the years 2000 and 2009 follow. Use 2000 as the base period. 2000 2000 Price ($/lb) 2009 Seafood Qty. (Ib) Price ($/lb) Halibut 75,090 2.01 2.33 Lobster 83,180 3.62 3.09 Tuna 50,779 1.87 1.97 (a) Compute a price relative for each type of seafood. (Round your answers to one decimal place.) Seafood Price Relative Halibut Lobster Tuna (b) Compute a weighted aggregate price index for the seafood catch. (Round your answer to one decimal place.) I 2009 - Comment on the change in seafood prices over the nine-year period. (Enter your percentage as a positive value. Round your answer to one decimal place.) Seafood prices have --Select--- v by % over the 9-year period according to the index.
Using the regression equation developed in step 2, forecast the monthly food and beverage sales for January through December for the fourth year of operation. Discuss your findings
Microprocessing, a computer firm specializing in software engineering, has compiled thefollowing revenue records for the years 1989 to 1995Year198919901991Revenue (× $100,000)1.11.51.919922.1199319942.42.9The second-degree equation that best describes the secular trend for these data is19953.5{=2.119 + 0.375x + 0.020x?, where 1992 = 0, and x units = 1 year(a) Calculate the percent of trend for these data.(b) Calculate the relative cyclical residual for these data.(c) Plot the percent of trend from part (a).(d) In which year does the largest fluctuation from trend occur, and is it the same for blmethods?
Find forecasted value for 2017using α = 0.5 and α = 0.7 using exponential smoothing method.Which α is better Years Sales Forecasted sale 2012 25 ……. 2013 17 2014 21 2015 30 2016 27 2017 ?
What scale should we use for the regressors? Expalain the reason?
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Plot the sales revenue figures and appropriate trend line on a graph and identify from this the choice of model for working out the seasonal component. Using the appropriate seasonal model, complete the calculations that are required to produce the required forecasts for quarters 1, 2, 3 and 4 of 2021. On the basis of your analysis explain the recommendation that you would make to ALAW Ltd regarding its future plans.

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Intercept Month Coefficients Standard Error t Stat 460.94771242 29.68313147 15.52894488 28.52012384 2.742253998 10.400248796 P-value 4.53893E-11 1.58675E-08 Step 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 nece