Delicacies Above the Rest, a high-end United States based restaurant in Montego bay, used regression analysis to develop a cost function for producing minicheesecakes. The calculations have produced the following information:Ex = 270, Ey =360, nEx? = 10,818, Ey2 18,107 and nExy = 14,102Using the information given, what is the variable cost per unit?O a. US$0.96Ob.US$3.70Oc.US$0.52O d. US$1.21

The following information has been given: ∑ x =270 , ∑y =360n∑ x2 =10818 , ∑y2 =18107n∑ xy =14102…

Answered: Delicacies Above the Rest, a high-end…

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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The following regression model was estimated. Q is the number of meals served, P is the average price per meal (customer ticket amount, in dollars), Rxis the average price charged by competitors (in dollars), Ad is the local advertising budget for each outlet (in dollars), and I is the average income per household in each outlet's immediate service area. Least squares estimation of the regression equation on the basis of the 25 data observations resulted in the estimated regression coefficients and other statistics given in Table below. Variable Coefficient Standard Error of Coefficient Intercept 128832.240 69974.818 Price (P) Competitor Price (Px) | Advertising (Ad) Income () -19875.954 4100.856 15467.936 459.280 0.261 0.094 8.780 1.017 Coefficient of determination R =83.3% (a) Interpret the coefficients of independent variables. (b) Test the significance of independent variables at 5% level of Significance. (c) Interpret R? with the help of adjusted R2. (d) Test for the overall…
Part #2
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…
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. ý = 1.9 – 3X + 12X2 + 4Xg + 8x, 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 =B4 = 0 O Ho: One or more of the parameters is not equal to zero. H₂: B3 =B4 = 0 O Ho: B3 = P4 = 0 H₂: None of the parameters are equal to zero. ⒸH₁: B3 =B₁ = 0 H: One or more of the parameters is not equal to zero. O Ho: B₁ = P₂ = B3 =B4 = 0 H: One or more of the parameters is not equal to zero. ✔ Find…
The following multiple regression printout can be used to predict the price (Price) of a used car given how many miles it has on it (Mileage), the size of the engine (Liter), and whether it has leather interior (Leather), where Leather = 1 for cars that have leather interior and 0 otherwise. %3D Regression Analysis: Price Versus Mileage, Liter, Leather Coefficients Term Coef SE Coef T-Value P-Value Constant 6,959 511 13.62 0.000 Mileage -0.0848 0.0178 -4.76 0.000 Liter 3,795 126 30.12 0.000 Leather 1,028 311 3.31 0.001 Regression Equation Price = 6,959 - 0.0848 Mileage + 3, 795 Liter + 1,028 Leather (a) Given this Minitab printout, is the response variable in this multiple regression equation a categorical or a numerical variable? O numerical O categorical (b) Determine whether the following statement is true or false. Given this Minitab printout, the regression coefficient for miles on the car (Mileage) is statistically significant at a = 0.05. O True O False (c) Determine whether the…
Consider the following passage: I ran a regression, with many variables to predict the result of another variable which was the murder rate. One can see that lots of things, can cause the murder rate to increase or decrease. I tried to account for all the important factors, and those factors are the SAT scores, unemployment rate, and international migration per 1,000. The SAT score is, average combined total score participants did on the SAT exam. The unemployment rate is, "a measure of the prevalence of unemployment and it is calculated as a percentage by dividing the number of unemployed individuals by all individuals currently in the labor force." (Wikipedia) International migration per 1,000 is, the number of people who come into a state from other countries per 1,000 people who live in the state. After I run the regression I will look at the t scores and p values and I should hopefully conclude that international migration does not cause crime. Which writing mistakes, if any, did…
Bluereef real estate agent wants to form a relationship between the prices of houses, how many bedrooms, House size in sq ft and Lot Size in sq ft. The data pertaining to 100 houses were processed using MINITAB and the following is an extract of the output obtained: The regression equation is Price = B + ¢Bedroom + yHouse Size + ALot Size Predictor Сoef SE Coef т P Constant 37718 14177 2.66 ** Bedrooms 2306 6994 0.33 0.742 House Size 74.3 52.98 0.164 Lot Size -4.36 17.02 -0.26 0.798 S= 25023 R-Sq=56.0% R-Sq(adj)=54.6% Source DF MS F P Regression 3 76501718347 25500572782 Residual Error 96 60109046053 626135896 Total 99 • Is y significantly different from -0.5? Perform the F test at the 1% level, making sure to state the null and alternative hypotheses. Give an interpretation to the term “R-sq" and comment on its value.
in simple linear regression, given the following table : n = 25 E(y,-7) = 1407 %3D SSR = 472 Calculate the standard error of the estimate (s): Select one: Oa. 7.82 Ob. 6.38 Oc. 58.62 d. 34.63 Next
The City of Bellmore’s police chief believes that maintenance costs on high-mileage police vehicles are much higher than those costs for low-mileage vehicles. If high-mileage vehicles are costing too much, it may be more economical to purchase more vehicles. An analyst in the department regresses yearly maintenance costs (Y) for a sample of 200 police vehicles on each vehicle’s total mileage for the year (X). The regression equation finds: Y = $50 + .030X with a r2 of .90 What is the IV? What is the DV? If the mileage increases by one mile, what is the predicted increase in maintenance costs? If a vehicle’s mileage for the year is 50,000, what is its predicted maintenance costs? What does an r2 of .90 tell us? Is this a strong or weak correlation? How can you tell?
In multiple regression, when all independent variables are considered but the order in which they are added to the model is determined by the amount it contributes to the dependent variable, it is called: a.Two step b.Stepwise c.Simultaneous d.Backward step
An econometric model is a multiple linear regression model if Question 10Select one: a. it explains the average value of y as a linear function of several explanatory variables b. it explains the sample mean of y as a function, linear in the parameters , of several x c. it explains y as a linear function of several x , the explanatory variables d. it explains the average of y as a function of several x e. none of the answers is correct
Which of the multivariate regression parameters listed below would be best interpreted as: the proportion of variation in the dependent variable explain by all the independent variables in the model. a b1 X1 R2

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