Incredible Marble Coat Sales156-148134$134$156$100O$148OPlease refer to the graph above.What is the predicted value of the dependent variable?100Margarine SalesOO Brussels Sprouts Sales$2,172$2,477$2,555$2,822$1,715$2,401$3,546$3,773$2,210$2,630Enormous Copper Bag Sales$336$438$463$432$341$297$580$457$524$426You currently work at a company producing Enormous Copper Bags. Yourmanager sent you the following table and requested you perform a regression toshare your interpreted results on the impact from Brussels Sprouts sales. How doyou reply?for every one unit increase in Enormous Copper Bag sales, the estimated average valueof Enormous Copper Bag sales will decrease by -$0.11for every one unit increase in Brussels Sprouts sales, the estimated average value ofBrussels Sprouts sales will increase by $217.83for every one unit increase in Brussels Sprouts sales, the estimated average value ofBrussels Sprouts sales will increase by $45,212.53for every one unit increase in Brussels Sprouts sales, the estimated average value ofEnormous Copper Bag sales will increase by $0.08

To perform a regression analysis, you can calculate the coefficients of the regression equation…

Answered: You currently work at a company…

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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Suppose you wanted to estimate the effect of being educated on being in the labor force. You estimate a model with two variables, LF is a binary variable = 1 if the person is participating in the labor force and the variable educ measures the number of years of education a person has received. You get the following estimated regression: LF = 0.1 + 0.15educ Which of the following is the correct interpretation about the effect of an additional year of education? O A 1% increase in education increases the number of those participating in the labor force by 15 percentage points. A additional year in education increases the number of those participating in the labor force by 0.15 percentage points A additional year in education increases the number of those participating in the labor force by 0.15% An additional year of education increases the probability of participating in the labor force by 15 percentage points. An additional year of education increases the probability of participating…
You may need to use the appropriate technology to answer this question. A regression analysis involving 45 observations relating a dependent variable and two independent variables resulted in the following information. ŷ = 0.406 + 1.3385x₁ + 2x₂ The SSE for the above model is 43. When two other independent variables were added to the model, the following information was provided. ŷ = 1.9 - 3x₁ + 12x2 + 4x3 + 8x4 This model's SSE is 36. At a 0.05 level of significance, test to determine if the two added independent variables contribute significantly to the model. State the relevant null and alternative hypotheses. O Ho: One or more of the parameters is not equal to zero. H₂: B₁ = P₂ = P3 =B4 = 0 B1 O Ho: One or more of the parameters is not equal to zero. H₂: B3 =B₁ = 0 O Ho: B3 = P4 = 0 H₂: None of the parameters are equal to zero. O Ho: B3 B4= = = H₂: One or more of the parameters is not equal to zero. O Ho: P₁ = P₂ = P3= P4= H: One or more of the parameters is not equal to zero. Find…
Use this multiple regression table to answer the series of questions below. Although it is here for your convenience, you can also find it on page 26 of the assigned reading "How do I Interpret Multiple Regression?" Feel free to use the text of that reading to help you answer the questions here. What is the dependent variable?
Please see problem with the solution: after reviewing the information below, assess the appropriateness and accuracy of using a linear regression model. Discuss the meaning of the standard error of the estimate and how it affects the predicted values of Y for that analysis. Many people are also using online shopping to avoid going to stores in person. I decided to use my own data. I am going to add how many Amazon transactions I have made each month from March- August. X= the month Y= amount of purchases Simple linear regression results: Dependent Variable: saleIndependent Variable: monthsale = 0.53333333 + 1.6 monthSample size: 6R (correlation coefficient) = 0.99410024R-sq = 0.98823529Estimate of error standard deviation: 0.36514837 Parameter estimates: Parameter Estimate Std. Err. Alternative DF T-Stat P-value Intercept 0.53333333 0.50269117 ≠ 0 4 1.0609562 0.3485 Slope 1.6 0.087287156 ≠ 0 4 18.330303 <0.0001 Analysis of…
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See the attached image for the introduction. Is there evidence that Bathrooms adds important information to a model that already includes Square Feet and Price/Square Feet? Use the output to justify your answer.
You may need to use the appropriate technology to answer this question. A regression analysis involving 45 observations relating a dependent variable and two independent variables resulted in the following information. ý = 0.406 + 1.3385X + 2X2 The SSE for the above model is 43. When two other independent variables were added to the model, the following information was provided. ŷ = 1.9 - 3x₁ + 12x₂ + 4x3 + 8x4 This model's SSE is 36. At a 0.05 level of significance, test to determine if the two added independent variables contribute significantly to the model. State the relevant null and alternative hypotheses. O Ho: One or more of the parameters is not equal to zero. H₂: B₁ = B₂= B3 = P4 = 0 OH: One or more of the parameters is not equal to zero. H₂: B3 =B₁ = 0 O Ho: B3 =B4 = 0 H₂: None of the parameters are equal to zero. H₁: B3 =B4 = 0 H₂: One or more of the parameters is not equal to zero. O Ho: B₁ = B₂= B3 =B4 = 0 H₂: One or more of the parameters is not equal to zero. ✔ Find the…
Can brand, battery life, and camera resolution affect a smartphone's price? Use α = .05 to perform a regression analysis for the Smartphones01BC dataset and answer the following questions. Solve this problem using MegaStat, and write your complete answers in the Response Box so I know that you can interpret the output. Attach your MegaStat file so I can see that you used the correct analysis method to solve this problem. Download: Smartphones01BC a. Perform the regression and write the complete regression equation. b. Based on your regression equation, which brand increases the price of a smartphone the most? Use the statistics from your output to explain your answer. c. Which predictors are significant, and which are not? Use the statistics from your output to explain your answer. d. Use your regression equation to predict the price of an LG smartphone with a 40-hour battery and 12-megapixel camera.
image attached. a. State your regression model.b. Interpret the result in the table using alpha level of significance at 5%.c. What percentage of variation of the dependent variable is explained by the model?d. Is the overall model significant? Why do you say so?
This dataset continues our saga of modeling the price of this popular Honda automobile. The dataset has now been cleaned to remove the columns with the dealership where the car was offered for sale and specific trim. (a) write out your model in econometric notation. Be very precise! (b) using the 93 observations in the dataset, estimate a model where price is a function of age, mileage and trim of the car. Be sure to avoid the dummy variable trap!! Fully report the results of your model. In this case, interpretation of the coefficients on the dummy variables is particularly important. (c) test the hypothesis that the specific trim does not affect the price of a Civic. Be sure to do all parts of the hypothesis test. (please fully describe steps if you are using Excel) Price Years Old KM EX EXT SE Sport Touring 6555 9 290363 0 0 0 0 0 9999 9 142258 0 0 0 0 0 10281 6 132644 0 0 0 0 0 12480 5 167125 0 0 0 0 0 12991 7 57398 0 0 0 0 0 12991 6 93046 0 0 0 0 0 12991…
improvements to the experimental design?

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You currently work at a company producing Enormous Copper Bags. Your manager sent you the following table and requested you perform a regression to share your interpreted results on the impact from Brussels Sprouts sales. How do you reply?