The table lists fossil fuel production as a percentage of total energy production for selected years. A linear regression model for this data is(A) Draw a scatter plot of the data and a graph of the model on the same axes.y = - 0.33x+95.0OA.OB.where x represents years after 1960 and y represents the corresponding percentage of oil imports.100100Fossil Fuel ProductionProduction (%)96Year196007->19701980916060Years after 106088Years after 1980199084OC.OD.200083100,1000-046060Years after 1960Years after 1960(B) Interpret the slope of the model.The rate of change of the percentage of oil imports with respect to time is -0.33%per year.(C) Use the model to predict fossil fuel production in 2010.In 2010 fossil fuel production as a percentage of total production will be about 78.5 %.(Round to one decimal place as needed.)(D) Use the model to estimate the year in which fossil fuel production will fall below 70% of total energy production.In the year fossil fuel production is expected to fall below 70% of total energy production.(Round up to the nearest year.)

ne table lists fossil fuel production as a percentage of total energy production for selected years. A linéar regression model for this data is y=-0.33x + 95.0 here x represents years after 1960 and y represents the corresponding percentage of oil imports. (A) Draw a scatter plot of the data and a graph of the model on the same axes. O A. B. 100T 100 Fossil Fuel Production Production (%) 96 91 88 84 83 Year 1960 1970 1980 1990 2000 Years after t000 Years after 1900 OC. OD. 100 100 E 60 Years after 1000 Years after 1960

ne table lists fossil fuel production as a percentage of total energy production for selected years. A linéar regression model for this data is y=-0.33x + 95.0 here x represents years after 1960 and y represents the corresponding percentage of oil imports. (A) Draw a scatter plot of the data and a graph of the model on the same axes. O A. B. 100T 100 Fossil Fuel Production Production (%) 96 91 88 84 83 Year 1960 1970 1980 1990 2000 Years after t000 Years after 1900 OC. OD. 100 100 E 60 Years after 1000 Years after 1960

Managerial Economics: Applications, Strategies and Tactics (MindTap Course List)

14th Edition

ISBN:9781305506381

Author:James R. McGuigan, R. Charles Moyer, Frederick H.deB. Harris

Publisher:James R. McGuigan, R. Charles Moyer, Frederick H.deB. Harris

Chapter4: Estimating Demand

Section: Chapter Questions

Problem 8E

See similar textbooks

Similar questions

The following data relate the sales figures of the bar in Mark Kaltenbach's small bed-and-breakfast inn in portland, to the number of guest registered that week: week guests bar sales 1 16 $330 2 12 $270 3 18 $380 4 14 $315 a) The simple linear regression equation that relates bar sales to number of guests(not to time) is (round your responses to one decimal place): Bar sales = [___]+[___]X guests
Please make a Data set and regression equation based on Student Debt and inflation with the following variables: Current student debt,interest rates, inflation rates ( will give thumbs up ) thank you so much.
A manufacturer is developing a facility plan to provide production capacity for its factory. The amount of capacity required in the future depends on the number of products demanded by its customers. The data below reflect past sales of its products: Year Annual Sales (number of products) Year Annual Sales (number of products) 1 490 5 461 2 487 6 475 3 492 7 472 4 478 8 458 Use simple linear regression to forecast annual demand for the products for each of the next three (3) years, by using the tabular method to: derive the values for the intercept and slope derive the linear equation plot the linear regression line develop a forecast for the firm’s annual sales for each of the next three years
A company wants to use regression analysis to forecast the demand for the next quarter.In such a regression model, demand would be the independent variable. True or false?a. Trueb. False
The regression equation to predict sales based on temperature is: Predicted sales = -2419.01+ 98.02 (temperature). A correct interpretation of the slope would be that 1. as temperature goes up by 1 degree, sales are predicted to go down by 2419.01. 2. as temperature goes down by 1 degree, sales are predicted to go up by 2419.01. 3. as temperature goes up by 1 degree, sales are predicted to go down by 98.02. 4. as temperature goes up by 1 degree, sales are predicted to go up by 98.02. 5. None of the answer choices provides a correct interpretation of the slope.
You estimated the following regression. What value would you predict for Y, if X = 46? (Round your final answer to zero decimal places.) Source | SS df MS Number of obs = 452 -------------+---------------------------------- F(1, 450) > 99999.00 Model | 451909533 1 451909533 Prob > F = 0.0000 Residual | 1435457.34 450 3189.9052 R-squared = 0.9968 -------------+---------------------------------- Adj R-squared = 0.9968 Total | 453344990 451 1005199.53 Root MSE = 56.479 ------------------------------------------------------------------------------ Y | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- X | 12.27466 .0326116 376.39 0.000 12.21057 12.33875 _cons | 69.67934 3.949949 17.64 0.000…
Consider the following estimated regression model relating annual salary to years of education and work experience. Estimated Salary=10,160.10+3147.75(Education)+1230.34(Experience) Suppose an employee with 8 years of education has been with the company for 20 years (note that education years are the number of years after 8th grade). According to this model, what is his estimated annual salary?
Numerical Answer Only Type Question Enter the numerical value only for the correct answer in the blank box. If a decimal point appears, round it to two decimal places. Assume that the number of visits by a particular customer to a mall located in downtown Toronto is related to the distance from the customer's home. The following regression analysis shows the relationship between the number of times a customer visits(Y)per month and the distance(X, measured in km) from the customer's home to the mall. \[ Y=15-0.5 X \] A customer who lives30 kmaway from the mall will visi______ who lives10 km away. less times than a customer
Past class data has shown that the regression line relating the final exam score and the midterm exam score for students who take statistics from the College of Information Technology and Engineering from Dr. Kalaw is: final exam = 50 + 0.5 × midterm One interpretation of the slope is a. students only receive half as much credit (.5) for a correct answer on the final exam compared to a correct answer on the midterm exam. b. a student who scored 0 on the midterm would be predicted to score 50 on the final exam. c. a student who scored 10 points higher than another student on the midterm would be predicted to score 5 points higher than the other student on the final exam. d. a student who scored 0 on the final exam would be predicted to score 50 on the midterm exam.
What is the functional form of this equation? What are the advantages and limitations of this functional form? Interpret precisely the coefficients of Px and Py in the regression.
In the December, 1969, American Economic Review (pp. 886-896), Nathanial Leff reports thefollowing least squares regression results for a cross section study of the effect of age composition onsavings in 74 countries in 1964:log S/Y = 7.3439 + 0.1596 log Y/N + 0.0254 log G - 1.3520 log D1 - 0.3990 log D2 (R2= 0.57)log S/N = 8.7851 + 1.1486 log Y/N + 0.0265 log G - 1.3438 log D1 - 0.3966 log D2 (R2= 0.96)where S/Y = domestic savings ratio, S/N = per capita savings, Y/N = per capita income, D1 = percentage ofthe population under 15, D2 = percentage of the population over 64, and G = growth rate of per capitaincome. Are these results correct? Explain..
Run the Multiple Linear Regression using Fatalities as the dependentvariable and Licensed drivers, registered vehicles, and GDP per capita as independentvariables. Submit Excel FileCalculate the following:a- Interpret the R-squareb-Find the equation of the fitted linec- Which variable has the strongest relationship with number of fatalities?d- Identify which variables are significant/non-significant using alpha = 0.05? Fatalities Licensed Drivers Registered Vehicles GDP per Capita (measured) 953 3999057 5300199 40598 80 536033 803684 71996 1010 5284970 5806313 43464 516 2145334 2817145 38919 3563 27039400 31022328 68970 632 4244713 5356018 59885 294 2605612 2879802 68555 111 786504 1008468 64895 31 527731 351933 176498 3133 15368695 17496002 43423 1504 7168733 8512550 50288 117 948417 1267385 58185 231 1252535 1879670 40189 1031 8714788 10588910 60419 858 4589405 6190736 49209 318 2260271 3691892 54520 404 2149430 2684010 53094 724…