4. Exercise 4.4Cascade Pharmaceuticals Company developed the following regression model, using time-series data from the past 33 quarters, for one of itsnonprescription cold remedies:Y=-1.04 +0.24X1-0.27X2whereY - quarterly sales (in thousands of cases) of the cold remedyX- Cascade's quarterly advertising (x $1,000) for the cold remedyX= competitors' advertising for similar products (x $10,000)Here is additional information concerning the regression model:= 0.052, e-0.070, R- 0.6-40, s= 1.63, F-statistie - 31.402, and Durbin-Watson (d) statistic = 0.499.Which of the independent variables (f any) appears to be statistically significant (at the 0.0S level) in explaining sales of the cold remedy? (Hint:taas/ 1- 2.042.) Check all that apply.O X1O X: Which of the independent variables (if any) appears to be statistically significant (at the 0.05 level) in explaining sales of the cold remedy? (Hint:tea/ L- - 2.042.) Check all that apply.O XIWhat proportion of the total variation in sales is explained by the regression equation?O 0.122O 0.640O 0.070O 0.052The given F-value shows that youreject the nul hypothesis that neither of the independent varlables explains a significant (at the 0.05level) proportion of the variation in income. (Hint: Fom.11-3.316.)

The estimated regression model is Y=-1.04+0.24X1-0.27X2. So, b1=0.24 and b2=-0.27. Given that…

Answered: 4. Exercise 4.4 Cascade Pharmaceuticals…

MATLAB: An Introduction with Applications

6th Edition

ISBN:9781119256830

Author:Amos Gilat

Publisher:Amos Gilat

Chapter1: Starting With Matlab

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Multiple regression is sometimes used in litigation. In the case of Cargill, Inc. v. Hardin (1971), the prosecution charged that the cash price of wheat was manipulated in violation of the Commodity Exchange Act. In a statistical study conducted for this case, a multiple regression model was constructed to predict the cash price of wheat using three supply-and-demand explanatory variables: economic growth, population growth, and meat consumption. Data for 24 years were used to construct the regression equation, and a prediction for the suspect period was computed from this equation Based on a significance level of 5%, which of the following independent variables significantly predict the cash price of wheat? a. Economic Growth b. Population Growth c. Meat Consumption d. All the independent variables significantly predict the cash price of wheat.
Consider the following estimated regression model relating annual salary to years of education and work experience. Estimated Salary = 10,815.11 +2563.46 (Education) +897.49(Experience) Suppose two employees at the company have been working there for five years. One has a bachelor's degree (8 years of education) and one has a master's degree ( 10 years of education). How much more money would we expect the employee with a master's degree to make? Answer Tables Keypac Keyboard Shortcu
The first independent variable (Xt2) will be a cubed value of the rate of return on individual companies. Is the inclusion of this variable consistent with the assumptions of the simple linear regression model? Explain your answer b. What is the additional assumption for this time-series model that does not apply to a cross-sectional analysis? c. If you suspected that your dependent variable Y was not stationary, explain the three unit root processes that you could test for. d. What does the letter "t" represent in the model?
Cascade Pharmaceuticals Company developed the following regression model, using time-series data from the past 33 quarters, for one of its nonprescription cold remedies: 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 $10,000) Here is additional information concerning the regression model: Sb1 = 0.052, Sb2 = 0.070, R² = 0.640, se = 1.63, F-statistic = 31.402, and Durbin-Watson (d) statistic = 0.499. Which of the independent variables (if any) appears to be statistically significant (at the 0.05 level) in explaining sales of the cold remedy? (Hint: to.05/2,33-3= 2.042.) Check all that apply. O X₁ O X₂ What proportion of the total variation in sales is explained by the regression equation? O 0.122 O 0.070 O 0.640 O 0.052 The given F-value shows that you reject the null hypothesis that neither of the independent variables…
The following table shows both current and projected data on the number, in millions, of Facebook users in a certain country. t = years since 2012 F = number of Facebookusers (millions) 0 9.7 1 10.5 2 10.8 3 12.2 4 13.1 5 13.2 6 13.8 (a) Plot the data. (b) Find the equation of the regression line. (Round regression line parameters to two decimal places.) F(t) = Add the graph of the regression line to the plot from part (a). (c) Explain in practical terms the meaning of the slope of the regression line. (Round your answers to two decimal places). The slope of the regression line is million users per year. The slope means that, from 2012 on, the number of Facebook users in the country is expected to increase by million each year.
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3. A study of beer consumption using the annual data from 1980 -2001 produced the following regression: Y = 0.41 +0.052.X₁; −0.047X2; +0.032X3; −0.018X4i (0.027) (0.011) (0.009) (0.008) (0.009) R² = 0.94 F = 66.58 DW statistic=1.32, figures in the brackets are standard errors Where: Y₁ = Annual aggregate beer consumption in year t (billion pints) X₁₁ = Real disposable income income in year t ($ billions at 2001 prices) X2i = Price of beer in year t (index number with 2001 = 100) X31 Price of wine and sprits in year t (index number with 2001 = 100) = X4₁ = Price of cigarettes in year t (index number 2001 = 100) (a) What is the effect of a change in the price of beer on beer consumption? Does it have the correct sign? Explain. (b) Comment on the signs of the other three coefficients. Are they as expected? (c) Tests the significance of the coefficients on each of the variables Xit i = 1, 2, 3, 4. What assumptions have you made in carrying out these tests? (d) What conclusion do you draw…
A company studying the productivity of its employees on a new information system was interested in determingg if the age (X) of data entry opeertors influenced the number of completed entries made per hour (Y). The regression equation is y = 14.374 - 0.145x Suppose the acyual completed entries per hour for an operator who is 35 years old was 8. The residual is:
Tucson Machinery, Incorporated, manufactures numerically controlled machines, which sell for an average price of $17.0 million each. Sales for these NCMS for the past two years were as follows: Use Exhibit 3.10. QUARTER LAST YEAR 3 4 QUANTITY (UNITS) 17. 23 31 21 Y= a. Find the equation of a simple linear regression line using Excel. Note: Round your answers to 3 decimal places. Last Year This Year 20.536 + QUARTER THIS YEAR 1 2 3 4 b. Compute trend and seasonal factor from a linear regression line obtained with Excel. Note: Do not round intermediate calculations. Round your answers to 3 decimal places. Period 1 2 3 4 1 2 QUANTITY (UNITS) 16 29 33 20 3 4 0.7141 Trend Forecast 0.800 1.047 1.367 0.898 0.664 1.168 1.292 0.762 Seasonal Factors 0.732 1.108 1.330 0.830 0.732 1.108 1.330 0.830

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