Y-1.04 +0.24X₁-0.27X2whereY = quarterly sales (in thousands of cases) of the cold remedyX₁ = Cascade's quarterly advertising (x $1,000) for the cold remedyX₂= competitors' advertising for similar products (x $10,000)Here is additional information concerning the regression model:8b1 = 0.052, 862 = 0.080, R² = 0.640, 8e-1.63, F-statistic=31.402, and Durbin-Watson (d) statistic=0.499.Which of the independent variables (if any) appears to be statistically significant (at the 0.05 level) in explaining sales of theremedy? (Hint: to.05/2,33-32.042.) Check all that apply.□ X₁OX2What proportion of the total variation in sales is explained by the regression equation?0.6400.080O 0.132O 0.052The given F-value shows that youreject the null hypothesis that neither of the independent variables explains asignificant (at the 0.05 level) proportion of the variation in income. (Hint: Fo.05,2,33-2-F3.316.) whereY = quarterly sales (in thousands of cases) of the cold remedyX₁ Cascade's quarterly advertising (x $1,000) for the cold remedyX2 competitors' advertising for similar products (x $10,000)=Here is additional information concerning the regression model:$61 0.052, 862=0.080, R2 = 0.640, 8= 1.63, F-statistic 31.402, and Durbin-Watson (d) statistic = 0.499.-Which of the independent variables (if any) appears to be statistically significant (at the 0.05 level)'in explaining sales of the coldremedy? (Hint: to.05/2,33-3 2.042.) Check all that apply.O X₁X₂What proportion of the total variation in sales is explained by the regression equation?O 0.640O 0.080O 0.1320.052reject the null hypothesis that neither of the independent variables explains aThe given F-value shows that you.sinnificant fat the 0.05 level) proportion of the variation in income. (Hint: Fo.05,2,33-2- 3.316.)

Given The regression equation: Here The standard error of the regression coefficient The slope…

Y = -1.04+0.24X₁-0.27X₂ where Y = quarterly sales (in thousands of cases) of the cold remedy X₁ = Cascade's quarterly advertising (x $1,000) for the cold remedy X₂ = competitors' advertising for similar products (x $10,000) Here is additional information concerning the regression model: 8b1 = 0.052, 862 = 0.080, R² = 0.640, s 1.63, F-statistic = 31.402, and Durbin-Watson (d) statistic = 0.499. Which of the independent variables (if any) appears to be statistically significant (at the 0.05 level) in explaining sales of the remedy? (Hint: to.05/2,33-2.042.) Check all that apply. O X₁ OX₂ What proportion of the total variation in sales is explained by the regression equation? O 0.640 O 0.080 O 0.132 O 0.052 The given F-value shows that you reject the null hypothesis that neither of the independent variables explains a significant (at the 0.05 level) proportion of the variation in income. (Hint: F0.05,2,33-2-F3.316.)

Y = -1.04+0.24X₁-0.27X₂ where Y = quarterly sales (in thousands of cases) of the cold remedy X₁ = Cascade's quarterly advertising (x $1,000) for the cold remedy X₂ = competitors' advertising for similar products (x $10,000) Here is additional information concerning the regression model: 8b1 = 0.052, 862 = 0.080, R² = 0.640, s 1.63, F-statistic = 31.402, and Durbin-Watson (d) statistic = 0.499. Which of the independent variables (if any) appears to be statistically significant (at the 0.05 level) in explaining sales of the remedy? (Hint: to.05/2,33-2.042.) Check all that apply. O X₁ OX₂ What proportion of the total variation in sales is explained by the regression equation? O 0.640 O 0.080 O 0.132 O 0.052 The given F-value shows that you reject the null hypothesis that neither of the independent variables explains a significant (at the 0.05 level) proportion of the variation in income. (Hint: F0.05,2,33-2-F3.316.)

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

The measure of standard error can also be applied to the parameter estimates resulting from linear regressions. For example, consider the following linear regression equation that describes the relationship between education and wage: WAGE: = Bo + B₁ EDUC; + &i where WAGE; is the hourly wage of person i (i.e., any specific person) and EDUC; is the number of years of education for that same person. The residual ₂ encompasses other factors that influence wage, and is assumed to be uncorrelated with education and have a mean of zero. Suppose that after collecting a cross-sectional data set, you run an OLS regression to obtain the following parameter estimates: WAGE;= -10.7+ 3.1 EDUC; If the standard error of the estimate of B₁ is 1.04, then the true value of B₁ lies between grows, you would expect this range to in size. and . As the number of observations in a data set
The data for this question is given in the file 1.Q1.xlsx(see image) and it refers to data for some cities X1 = total overall reported crime rate per 1 million residents X3 = annual police funding in $/resident X7 = % of people 25 years+ with at least 4 years of college (a) Estimate a regression with X1 as the dependent variable and X3 and X7 as the independent variables. (b) Will additional education help to reduce total overall crime (lead to a statistically significant reduction in crime)? Please explain. (c) Will an increase in funding for the police departments help reduce total overall crime (lead to a statistically significant reduction in total overall crime)? Please explain. (d) If you were asked to recommend a policy to reduce crime, then, based only on the above regression results, would you choose to invest in education (local schools) or in additional funding for the police? Please explain.
The data below represent commute times (in minutes) and scores on a well-being survey. Complete parts (a) through (d) below. Commute Time (minutes), x Well-Being Index Score, y 5 72 105 20 25 35 60 69.2 68.0 67.5 67.1 65.9 66.0 63.8 (a) Find the least-squares regression line treating the commute time, x, as the explanatory variable and the index score, y, as the response variable. ŷ=x+ (Round to three decimal places as needed.) (b) Interpret the slope and y-intercept, if appropriate. First interpret the slope. Select the correct choice below and, if necessary, fill in the answer box to complete your choice. OA. For every unit increase in commute time, the index score falls by (Round to three decimal places as needed.) OB. For every unit increase in index score, the commute time falls by (Round to three decimal places as needed.) 1 D. For an index score of zero, the commute time is predicted to be (Round to three decimal places as needed.) on average. on average. OC. For a commute time…
An example of a cubic regression model is Yi= 30 + B1X + 32x2 + 33x³ + ui Yi = 30 + B1X + 32x² + ui. Yi = 30 + ß1ln(X) + ui Yi= 30 + 31X + B2Y2 + ui.
General Cereals is using a regression model to estimate the demand for Tweetie Sweeties, a whistle-shaped, sugar-coated breakfast cereal for children. The following (multiplicative exponential) demand function is being used: QD= 6,280 P 1.35) 42.05 N 2.70 where QD quantity demanded, in 10-oz boxes P = price per box, in dollars A = advertising expenditures on daytime television, in dollars N = proportion of the population under 12 years old, in percent What is the point price elasticity of demand for Tweetie Sweeties? O 2.70 O 2.05 -0.66 -1.35 What is the advertising elasticity of demand? -1.35 O 2.70 O 0.76 O 2.05 According to the estimated model, a percent increase in the proportion of the population under 12 years old by percent. the quantity demanded
Given the estimated multiple regression equation ŷ = 6 + 5x1 + 4x2 + 7x3 + 8x4 what is the predicted value of Y in each case? a. x1 = 10, x2 = 23, x3 = 9, and x4 = 12 b. x1 = 23, x2 = 18, x3 = 10, and x4 = 11 c. x1 = 10, x2 = 23, x3 = 9, and x4 = 12 d. x1 = -10, x2 = 13, x3 = -8, and x4 = -16
General Cereals is using a regression model to estimate the demand for Tweetie Sweeties, a whistle-shaped, sugar-coated breakfast cereal for children. The following (multiplicative exponential) demand function is being used: QD = 6,280 P(-2.15) A1.75N2.70 where QD = quantity demanded, in 10-oz boxes P = price per box, in dollars A = advertising expenditures on daytime television, in dollars N = proportion of the population under 12 years old, in percent What is the point price elasticity of demand for Tweetie Sweeties? 1.75 -1.23 2.70 -2.15 What is the advertising elasticity of demand? 0.65 1.75 -2.15 2.70 According to the estimated model, a percent increase in the proportion of the population under 12 years old by percent. the quantity demanded
A finance manager employed by an automobile dealership believes that the number of cars sold in his local market can be predicted by the interest rate charged for a loan. Interest Rate (%) Number of Cars Sold (100s) 3 5 10 7 8 2 The finance manager performed a regression analysis of the number of cars sold and interest rates using the sample of data above. Shown below is a portion of the regression output. Regression Statistics Multiple R0.998868 R2 0.997738 Coefficient |14.88462 Interest Rate -1.61538 Intercept 1. Are there factors other than interest rate charged for a loan that the finance manager should consider in predicting future car sales? 2. Is interest rate charged for a loan the most important factor to be considered in predicting future car sales? Explain your reasoning.The dealership's vice- president of marketing has requested a sales forecast at the prevailing interest rate of 7%. 3. As finance manager, what reasons would you convey to the vice-president in recommending…
A researcher wants to test the relationship between the number of years of formal education received (X) and the average weekly earnings (Y) (measured in hundred dollars). A report released by a government agency suggests that the average weekly earnings of individuals with no formal education is equal to $545. The researcher wants to test whether the average weekly earnings with no formal education is $545 or greater than that. He collects data from a sample of 120 individuals and estimates the following regression function: Y-6.91+2.30X, (1.25) (4.25) where , is the predicted value of the weekly earnings for the individual and the standard errors for the coefficients appear in parenthesis. The f-statistic for the test the researcher wants to conduct will be (Round your answer to two decimal places)
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
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