Problem #2b:It is believed that a trend, as well as seasonality, may be present in the data. Toaccount for the trend, another multiple regression equation was computed usingthe quarter dummy variables and time period (t). Letting t = 1 refer to theobservation in Quarter 1 of Year 1; t = 2 to refer to the observation in Quarter 2 ofYear 1; ... and t = 12 to refer to the observation in Quarter 4 of Year 3.SUMMARY OUTPUTRegression StatisticsMultiple R0.995376R Square0.990773Adjusted R Square0.985501Standard Error89.82788Observations12ANOVAdfSSMSSignificance FFRegression46065391.671516347.92187.920.00000034Residual56483.338069.05Total116121875.00ITIACoefficientsStandard Errort StatP-valueIntercept2306.6782.0028.130.000000018Qtrl-642.2977.118.330.00007Qtr2-1465.4275.04-19.530.00000023Qtr3349.7973.774.740.00210424623.137.942.910.022577878Based on this new multiple regression model, compute the four quarterly forecastsfor next year using the above Excel output.

Answered: Image /qna-images/answer/52a869b0-9ca2-4453-bd86-7d555656d28f.jpg

Answered: Problem #2b: It is believed that a…

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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Question 3
(d) If the life expectancy is increased by 3.5 years in a certain country, how much will the happiness index change? Round to two decimal places. (e) Use the regression line to predict the happiness index of a country with a life expectancy of 84 years. Round to two decimal places.
answer 6
To assess the relationship between monthly sales of a product and monthly advertising expenditure(both in thousands of dollars); a linear regression model was fitted using data for 20 months and results were as follows: s.e(8) Variable B Intercept 6.3812 advert 1.1762 0.0786 (a) From the results, we can say that an increase of 1000 dollars in monthly expenditure is associated with an increase of 1.1762 dollars in average sales. an increase of 1.1762 dollars in monthly expenditure is associated with an increase of one dollar in average selling price. an increase of 1000 dollars in monthly expenditure is associated with an increase of 1176.2 dollars in average sales. an increase of one dollar in monthly expenditure is associated with an increase of 1.1762 dollars in average sales. <
Accountants at the GIll Co and Charted Brothers Accounting believed that several traveling executives were submitting unusually high travel vouchers upon returning from business trips. First, they took a sample of 200 vouchers submitted from the past year. Then they developed the following multiple-regression equation relating expected travel cost (Y) to a number of days on the road (X1) and distance traveled (X2) in miles: Y = 90.00 + 48.50X1 + 0.40X2 Here is additional information concerning the regression model: Sb1 = 0.038 Sb2 =0.019 R2 = 0.68 Se = 1.63 F-Statistic = 32.123 Durbin-Watson (d) statistic = 0.5436 a. Which of the independent variables appear to be statistically significant (at the 0.05 significant level) in explaining the expected travel cost for accountants
The following table gives the personal savings (in billions of dollars) in the United States for selected years. Year 2005 2010 2011 2012 2013 2014 2015 2016 Personal Savings (billions) $153.8 189.3 187.5 142.0 155.7 152.1 175.6 199.6 5. Find the average rate of change between the years 2010 and 2012. 6. Interpret questions 4 and 5 on the graph. 7. Find the point of inflection of the regression equation and interpret what it means.
1. School administrators believe their freshman applications are influenced by two variables: tuition and the size of the applicant pool of eligible high school seniors in the state. The following data for an 8-year period show the tuition rates (per semester) and the sizes of the applicant pool for each year: Tuition in pesos Applicants Pool Applicants 27000 76,200 11,060 37500 78,050 10,900 41250 67,420 8,670 42000 70,390 9.050 46500 62,550 7,400 48750 59,230 7,100 52500 57,900 6,300 57900 60,080 6,100
What would be the simple linear regression (trend projection) forecast for week 4 and for week 53 for Small Town Restaurant (see file) if the regression equation for our data is: y = 3061.7 + 0.2364x ? Provide two decimal places and use normal rounding.
Question 6, 3.3.30
Consider the following passage: I ran a regression, with many variables to predict the result of another variable which was the murder rate. One can see that lots of things, can cause the murder rate to increase or decrease. I tried to account for all the important factors, and those factors are the SAT scores, unemployment rate, and international migration per 1,000. The SAT score is, average combined total score participants did on the SAT exam. The unemployment rate is, "a measure of the prevalence of unemployment and it is calculated as a percentage by dividing the number of unemployed individuals by all individuals currently in the labor force." (Wikipedia) International migration per 1,000 is, the number of people who come into a state from other countries per 1,000 people who live in the state. After I run the regression I will look at the t scores and p values and I should hopefully conclude that international migration does not cause crime. Which writing mistakes, if any, did…
Suppose that a simple linear regression model is appropriate for describing the relationship between y = house price (in dollars) and x = house size (in square feet) for houses in a large city. The population regression line is y = 22,500 + 43x and ?e = 4,000. b) Approximately what proportion of 2,000 sq ft homes would be priced over $110,000? (You may need to use a table. Round your answer to four decimal places.) Approximately what proportion of 2,000 sq ft homes would be priced under $100,000? (You may need to use a table. Round your answer to four decimal places.)
Question 38. You have performed a linear regression analysis to explore sunflowers' growth (in meters per month) depending on the watering (in litres per day). You have estimated the regression coefficient to be ß = 1.6. What can you conclude? a) There is a signifleant correlation between watering and growth. b) An average sunflower growths 1.6 meters per month. c) If you give it an additional litre of water per day, there will be an additional average growth of 1.6 meters per month. d) According to the model assumptions, an additional litre of water per day will result in additional 19.2 meters of growth after one year. e) You should consider further influencing quantities.

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