What does the value for the coefficient mean in the regression analysis of both the company ?P-Values - how its related to customer satisfaction in the regression analysis of both the company ? Stepwise Regression for Customer SatisfactionSummaryANOVA TableExplainedUnexplainedRegression TableConstantOverall experiences SATISFACTION with QO010Sufficiency of Product informationEase of comparing productsAvailability of productsEase of indicating special requestsVariety of products that interests meStep InformationOverall experiences SATISFACTION with QO010Sufficiency of Product informationEase of comparing productsAvailability of productsEase of indicating special requestsVariety of products that interests meMultipleR0.9042Degrees ofFreedom6193CoefficientMultipleRR-Square0.88650.89250.89660.89910.90160.90420.8176Sum ofSquaresStandardError250.7856697 41.7976116255.93433028 0.289815183AdjustedR-square0.8120R-SquareMean ofSquares0.78590.79660.80390.80850.81280.8176t-Value-0.217270506 0.350478942 -0.619924565 0.53600.688174109 0.043293281 15.89563319 < 0.00010.07175455 0.03667907 1.956280555 0.05190.081436842 0.036042429 2.259471539 0.02500.098373018 0.038465928 2.557406575 0.0113-0.003101656 0.00132424 -2.342216078 0.02020.087626065 0.038904859 2.252316741 0.0254AdjustedR-squareStd. Err. ofEstimate0.78480.79460.80090.80450.80800.81200.538344855F144.2216078p-ValueStd. Err. ofEstimate0.57587540.5626917450.5540233890.5488808720.5439666390.538344855RowsIgnored0p-Value< 0.0001OutliersConfidence Interval 95%Enter orExit0LowerUpper-0.908531225 0.4739902130.6027854 0.773562818-0.00058874 0.1440978410.010349218 0.1525244670.022505449 0.174240587-0.005713496 -0.0004898160.01089278 0.164359351EnterEnterEnterEnterEnterEnter Stepwise Regression for Customer SatisfactionSummaryANOVA TableExplainedUnexplainedRegression TableConstantOverall experiences SATISFACTION with AMAZONAttractiveness of promotions and discounts offeredAvailability of productsEase of managing your shopping cartCheck-out and payment processReturn and exchange policiesVariety of products that interests meStep InformationOverall experiences SATISFACTION with AMAZONAttractiveness of promotions and discounts offeredAvailability of productsEase of managing your shopping cartCheck-out and payment processReturn and exchange policiesVariety of products that interests meMultipleR0.9218Degrees ofFreedom7192CoefficientMultipleRR-Square0.89000.90190.90950.91410.91710.91980.92180.8498Sum ofSquaresStandardErrorAdjustedR-square0.8443R-SquareMean ofSquares275.2596606 39.32280866 155.156732548.66033939 0.2534392680.79200.81340.82720.83560.84100.84600.8498t-Value-1.175650684 0.356010918 -3.302288286 0.00110.650562743 0.037808352 17.20685256 < 0.00010.104418655 0.030391701 3.435762106 0.00070.108206379 0.034817751 3.107793454 0.00220.100683803 0.034802557 2.893000169 0.00430.088201665 0.036571028 2.411790702 0.0168-0.002354944 0.000986141 -2.388039316 0.01790.089266709 0.040879106 2.183675699 0.0302Std. Err. ofEstimate0.50342752AdjustedR-squareF0.79100.81150.82460.83230.83690.84130.8443p-ValueStd. Err. ofEstimate0.5833138370.5539275690.5343468960.5225351740.5152592860.508318620.50342752RowsIgnored0p-Value< 0.0001Enter orExitConfidence Interval 95%LowerUpper-1.87784537 -0.4734559990.575989681 0.7251358040.044474171 0.164363140.039531969 0.1768807890.032039362 0.1693282450.016069098 0.160334233-0.004300005 -0.0004098820.008636905 0.169896514EnterEnterEnterOutliersEnterEnterEnterEnter0

Here there are two multiple regression models. The coefficients and their respective p-value,…

Answered: What does the value for the coefficient…

What does the value for the coefficient mean in the regression analysis of both the company ? P-Values - how its related to customer satisfaction in the regression analysis of both the company ?

Managerial Economics: Applications, Strategies and Tactics (MindTap Course List)

14th Edition

ISBN:9781305506381

Author:James R. McGuigan, R. Charles Moyer, Frederick H.deB. Harris

Publisher:James R. McGuigan, R. Charles Moyer, Frederick H.deB. Harris

Chapter4: Estimating Demand

Section: Chapter Questions

Problem 8E

See similar textbooks

Similar questions

If you are interested in investigating themarginal effect of the percentage change of lot size on the changeof house price, then in your linear regression analysis you should apply thelogarithm transformation on: Select one: a. The house price variable. b. Both the house price variable and the lot size variable. c. The lot size variable. d. The bedroom variable. e. Both the bedroom variable and the house price variable.
Suppose that a coffee producing firm estimated the following regression of thedemand for its brand of coffee:Qc = 1.5 − 3.0Pc + 0.8Y + 2.0Pb − 0.6PS +1.2 Awhere Qc = sales of coffee brand C, in dollarsper pound Pc = price of coffee brand C,in dollars per poundY = personal disposable income, in millions of dollars per yearPb = price of the competitive brand of coffee, in dollarsper pound Ps = price of sugar, in dollars per poundA = advertising expenditures for coffee brand C, in hundreds of thousands ofdollars per year.Suppose also that this year, Pc = $2, Y = $2.5, Pb = $1.80,Ps = $1 and A =$1.a. Interpret the results of the estimated demand.b. Compute point price elasticity of demand for the firm’s brand of coffeewith respect to its price.c. Compute the cross-price elasticity of demand for coffee with respect to theprice of competitive coffee brand b.d. At the current price level, would it be viable for the firm to increase the pricelevel of its brand of coffee? Support your answer.…
Run the Multiple Linear Regression using Fatalities as the dependentvariable and Licensed drivers, registered vehicles, and GDP per capita as independentvariables. Submit Excel FileCalculate the following:a- Interpret the R-squareb-Find the equation of the fitted linec- Which variable has the strongest relationship with number of fatalities?d- Identify which variables are significant/non-significant using alpha = 0.05? Fatalities Licensed Drivers Registered Vehicles GDP per Capita (measured) 953 3999057 5300199 40598 80 536033 803684 71996 1010 5284970 5806313 43464 516 2145334 2817145 38919 3563 27039400 31022328 68970 632 4244713 5356018 59885 294 2605612 2879802 68555 111 786504 1008468 64895 31 527731 351933 176498 3133 15368695 17496002 43423 1504 7168733 8512550 50288 117 948417 1267385 58185 231 1252535 1879670 40189 1031 8714788 10588910 60419 858 4589405 6190736 49209 318 2260271 3691892 54520 404 2149430 2684010 53094 724…
Please no written by hand and no emage Your company, which specializes in running shoes for men who are growing increasingly follicly-challenged (BalderDash®), has the following demand function: Q = a + bP + cM + dR where Q is the quantity demanded of BalderDash’s most popular shoes, P is the price of that product, M is consumer income, and R is the price of a related product. The regression results are: Adjusted R Square 0.7796 Independent Variables Coefficients Standard Error t Stat P-value Intercept 21,055.04 1428.27 14.74 8.1E-16 P -83.912 19.079 -4.398 0.000 M 0.0266 0.013 2.064 0.047 R -16.6 10.664 -1.556 0.129 Discuss whether you think these regression results will generate good sales estimates for BalderDash. Now assume that the income is $69,100, the price of the related good is $39, and BalderDash chooses to set the price of its product at $54. b. What is the estimated number of units sold given the data above? (round to nearest unit; no decimals) c.…
Given the following data X (consumers of teff) or popn 3 6 8 1 13 13 14 Y ( teff consumption) 8 6 10 12 12 14 20 year 2013 2014 2015 2016 2017 2018 2019 Estimate the regression equation, Y= a+bX, Where Y denotes demand for teff while X is consumers of teff (population) By assuming demand for teff is only affected by its consumers, find the amount demand for teff in the year 2022 if the populations (consumers of teff) are about 18 people? (Hint: use the least square method, parameter a and b can be estimated by solving the two linear equations) SY= na+ bSX SXY=aSX +b Where n is number of years. For example, Estimate the sales for 2012, 2015 and fit a linear regression equation and draw a trend line.ar X Sales (Y) XY X2 year X Sales (Y) XY X2 2002 1 22734 22734 1 2003 2 24731 49462 4 2004 3 31489 94467 9 2005 4 44685 178740 16 2006 5 55319…
look at the influence of mileage on the sale price of each brand ofautomobile. Postulate the following statistical models,?_ℎ????? = ?0 + ?1?????? + ?? and ?_?ℎ??? = ?0 + ?1?????? + ?? with ??~?(0, ?2).For each model (‘model_honda’ and ‘model_chev’) perform the following,Comment on the miles variable after examining it with hist() and summary() commands.What does the assumption of non-random independent variable imply about how thedata must be collected if the same miles variable is used for both brand of automobile?
A firm is faced with the following demand function (estimated in a regression equation based onpast data).QX = 200 – 4PX , where current price (PX ) = $30The firm is thinking of reducing price to $25 because someone suggested that wouldbring in more revenue. a. What is the own-price elasticity of demand? (show absolute value)b. Is the demand function elastic, inelastic or unitary elastic at a price of $30?c. Will the price reduction bring in more revenue?
Consider the following estimated regression model relating annual salary to years of education and work experience. Estimated Salary=11,722.40+3182.56(Education)+1202.44(Experience)Estimated Salary=11,722.40+3182.56(Education)+1202.44(Experience) Suppose an employee with 66 years of education has been with the company for 33 years (note that education years are the number of years after 8th8th grade). According to this model, what is his estimated annual salary?
4- The manager of Collins Import Autos believes the number of cars sold in a day(Q) depends on two factors: (1) the number of hours the dealership is open (H) and (2) the number of salespersons working that day (S ). After collecting data for two months (53 days), the manager estimates the following log-linear model: Q = aHbSc ----- a. Explain how to transform this log-linear model into a linear form that can be estimated using multiple regression analysis. b. How do you interpret coefficients b and c? If the dealership increases the number of salespersons by 20 percent, what will be the percentage increase in daily sales? c. Test the overall model for statistical significance at the 5 percent significance level.
Hello, please help me to solve the question (c) and (d) below.Consider this regression model (1) : Yt = β0 + β1 Ut + β2 Vt + β3 Wt + β4 Xt + εt ; where t= 1, ..., 75.We use OLS to estimate the parameters, producing the following model:Ŷt = 1.115 + 0.790 Ut − 0.327 Vt + 0.763 Wt + 0.456 Xt (0.405) (0.178) (0.088) (0.274) (0.017) Given that:R2 = 0.941; Durbin Watson stat DW = 1.907; RSS = 0.0757.(To answer the question, use the 5% level of significance, state clearly H0 and H1 that are tested, the test statistics that are used, and interpret the decisions.) (a) Describe the concepts of unbiasedness and efficiency. State the conditions required of regression (1) in order that the OLS estimators of the model parameters possess these properties. (b) Perform the following tests on the parameters of regression (1): (i) test whether the parameters β1, β2, β3 and β4 are individually statistically significant; (ii) test the overall significance of the regression model;…
1. Let kids denote the number of children ever born to a woman, and let educ denote years of education for the woman. A simple model relating fertility to years of education is kids = 0 + 1educ + u, where u is the unobserved error. (a) What kinds of factors are contained in u? Are these likely to be correlated with level of education? (b) Will a simple regression analysis uncover the ceteris paribus eect of education on fertility? Explain.
Imagine you are trying to explain the effect of square footage on home sale prices in the United States. You collect a random sample of 100,000 homes that recently sold. a) Homes can be one of three types: single-family houses, townhomes, or condos. How would you control for a home’s type in a regression model? b) Write down a regression model that includes controls for home type, square footage, and number of bedrooms. c) How would you interpret the es3mated coefficients for each of the variables from part b? Be specific.