A company wants to develop a simple linear regression model for one of its products. Use the following 12 periods of historical data to develop the regression equation and use it to forecast the next three periods. Click the icon to view the historical data for the previous 12 periods. The simple linear regression line is F₁ =+x. (Enter your responses rounded to two decimal places and include a minus sign if necessary.) Find the forecasts for periods 13-15 based on the simple linear regression and fill in the table below (enter your responses rounded to two decimal places). - X Period Forecast (F₁) (x) 13 14 15 O Period (x) 1 2 3 4 5 6 7 8 9 10 11 12 at (y) 211 184 199 187 156 150 162 165 146 130 119 129 D Next

A company wants to develop a simple linear regression model for one of its products. Use the following 12 periods of historical data to develop the regression equation and use it to forecast the next three periods. Click the icon to view the historical data for the previous 12 periods. The simple linear regression line is F₁ =+x. (Enter your responses rounded to two decimal places and include a minus sign if necessary.) Find the forecasts for periods 13-15 based on the simple linear regression and fill in the table below (enter your responses rounded to two decimal places). - X Period Forecast (F₁) (x) 13 14 15 O Period (x) 1 2 3 4 5 6 7 8 9 10 11 12 at (y) 211 184 199 187 156 150 162 165 146 130 119 129 D Next

ENGR.ECONOMIC ANALYSIS

14th Edition

ISBN:9780190931919

Author:NEWNAN

Publisher:NEWNAN

Chapter1: Making Economics Decisions

Section: Chapter Questions

Problem 1QTC

See similar textbooks

Similar questions

A home appraisal company would like to develop a regression model that would predict the selling price of a house based on the age of the house in years (X1), the living area of the house in square feet (X2), and the number of bedrooms (X3). The following regression model was chosen using a data set of house statistics: y=88,399554791.3333x231,471.1372x3 The first house from the data set had the following values: Selling price $324,000 Age - 22 years Square Feet 2.000 Bedrooms 3 The residual for this house is 23,558 -41,480 10,216 -16,095 27
What is the maximum weekly profit? What is the wholesale price per sleeping bag that should be charged to realize the maximum weekly profit?
RQ7. A teacher is trying to predict student test grades (Q). She believes test grades are a function of incoming GPA, hours studying, and hours spent on social media (a distraction). She runs a regression and it produces these coefficients: Variable Coefficient Intercept GPA Hours Studying Social Media 70.0 3.5 2.4 -4.0 For a given student Julian, his GPA is 2.0, he studies 4 hours for the exam, and he spends 6 hours on Facebook. Predict his exam score (round to the nearest whole number).
Find the degrees of freedom in a regression model that has 10 observations and 7 independent variables
calculate slope coefficient for a regression of Y on X calculate the constant of a regression of Y on X calculate the residual for the first observation in the table
Using a sample of recent university graduates, you estimate a simple linear regression using initial annual salary as the dependent variable and the graduate's weighted average mark (WAM) as the explanatory variable. If the regression model has an estimated intercept of 3200 and an estimated slope coefficient of 550, what is the predicted starting salary of a student with a WAM of 69? Answer:
1. Suppose output (Q) is related to labor (L) and capital (K) in the following nonlinear way: Q = albKc When taking log to this equation, it is transformed into a linear LnQ = Ina + b In(L) + c Ln (K) One hundred twenty-three observations are used to obtain the following regression results: Dependant Variable: Observations: Variable Intercept L K Q 123 5.5215 Parameter Standard Estimate error 0.650 R-square 0.350 0.7547 0.9750 0.2950 0.1450 F-ratio 184.56 t-ratio 5.66 2.20 2.41 p-value on F 0.00001 p-value 0.0001 0.0295 0.0173 a. Write the regression equation based on the output either in the transformed linear form or the original non-linear form.
13. Collinearity in a multiple regression analysis Suppose you want to examine the effects of a training program on future earnings using the following model: earn98= 4.64 +2.376train +0.371earn96 +0.366educ- 1.86 age +2.534 married (1.14) (0.43) (0.016) (0.062) (0.013) (0.4) where earn 98- 1998 earnings, in thousands of dollars train -1 if the individual participated in the training program, and =0 otherwise earn 96- 1996 earnings, in thousands of dollars educ years of education age = age, in years married-1 if the individual is married, and -0 otherwise Suppose that there is a high degree of correlation (but not perfect) between earnings in 1996, education, age, and marital status. True or False: We should be concerned about this high degree of correlation because it affects our ability to reliably estimate the impact of the training program on 1998 earnings, T. True False