Data Collection and Descriptive Statistics of the Walt Disney Company _phase II _group project_MBAF

The table to the right contains price-demand and total cost data for the production of projectors, where p is the wholesale price (in dollars) of a projector for an annual demand of x projectors and C is the total cost (in dollars) of producing x projectors. Answer the following questions (A) - (D). (A) Find a quadratic regression equation for the price-demand data, using x as the independent variable. X 270 360 520 780 The fixed costs are $. (Round to the nearest dollar as needed.) ITTI y = (Type an expression using x as the variable. Use integers or decimals for any numbers in the expression. Round to two decimal places as needed.) Use the linear regression equation found in the previous step to estimate the fixed costs and variable costs per projector. The variable costs are $ per projector. (Round to the nearest dollar as needed.) (C) Find the break even points. The break even points are (Type ordered pairs. Use a comma to separate answers as needed. Round to the nearest integer as…

2. Which of the following types of regressions will always have a binary outcome variable? (A) Probit (B) Difference-in-differences (C) Regression discontinuity (D) (A) and (B) will both have binary outcome variables

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 ?

d. If the director used these 4 weeks of data to create a linear regression, what does that linear regression formula suggest for this week's forecast of employee appointments? What does the regression analysis suggest in general about employee appointments for Director Very Busy?

d/My courses / Faculty Of Economics & Administratiive Sciences / ECON309 / Finals / ECON 309 Fin 13. In the simple linear regression model, the regression slope a. O a. indicates by how many percent Y increases, given a one percent increase in X. ut of O b. represents the elasticity of Y on X. uestion Oc. when multiplied with the explanatory variable will give you the predicted Y. O d. indicates by how many units Y increases, given a one unit increase in X. nage

1. Suppose that you have following data: Variable Description CEO salary measured in thousands of $ Firm's sale measure in millions ofS Return on equity in percent Salary sales roe *Return on equity is a measure of financial performance calculated by dividing net income by shareholders' equity. Your estimated regression is given by log (salary) = 4.322 + 0.276 log(sale) + 0.0215roe - 0.0008roe?, R = 282, n = 209. (324) (0.033) (0.0129) (0.00026) a) Is the effect of all independent variables statistically equal to 0? b) Interpret the coefficient on log(sale). c) Interpret the effect of roe on log(salary). • Without more information, your interpretation of the effect of roe on log(salary) should include answers to these sub-question. Should the roe be included in this model? il. Comment on relationship between roe and log(salary): is it U-shaped or inverse U-shaped? What is the turning point? How would you interpret this point? Plot log(salary) vs roe. v. ii. iv. Compute predicted value…

Question 9 Regression analysis was applied between demand for a product (Y) and the price of the product (X), and the following estimated regression equation was obtained. Y = 120 - 15 X Based on the above estimated regression equation, if price is increased by 2 units, then demand is expected to: O Increase by 120 units Decease by 30 units O Increase by 2Q units Next « Previous Submi No new data to save. Last checked at 9:51pm

As an auto insurance risk analyst, it is your job to research risk profiles for various types of drivers. One common area of concern for auto insurance companies is the risk involved when offering policies to younger, less experienced drivers. The U.S. Department of Transportation recently conducted a study in which it analyzed the relationship between 1) the number of fatal accidents per 1000 licenses, and 2) the percentage of licensed drivers under the age of 21 in a sample of 42 cities. Your first step in the analysis is to construct a scatterplot of the data. FIGURE. SCATTERPLOT FOR U.S. DEPARTMENT OF TRANSPORATION PROBLEM U.S. Department of Transportation The Relationship Between Fatal Accident Frequency and Driver Age 4.5 3.5 3 2.5 1.5 1 0.5 6. 10 12 14 16 18 Percentage of drivers under age 21 Upon visual inspection, you determine that the variables do have a linear relationship. After a linear pattern has been established visually, you now proceed with performing linear…

Given are five observations for two variables, x and y. Excel File: data14-17.xlsx 9 13 20 Yi 7 18 26 23 The estimated regression equation for these data is ŷ = 7.6 + 0.9x . Compute SSE, SST, and SSR (to 1 decimal). SSE SST SSR What percentage of the total sum of squares can be accounted for by the estimated regression equation (to 1 decimal)? % What is the value of the sample correlation coefficient (to 3 decimals)? 2] 100

5. Among important factors that affecting the price of land lot are size, number of mature trees and distance to the lake. Using data for 60 recently sold land lots are shown below: B 1 SUMMARY OUTPUT 2 3. Regression Statistics 4 Multiple R 5 R Square 6 Adjusted R Square 7 Standard Error 0.4924 0.2425 0.2019 40.24 8 Observations 60 9. 10 ANOVA Significance F 5.97 11 df S MS 9676.6 0.0013 12 Regression 13 Residual 3 29,030 90,694 56 1619.5 14 Total 59 119,724 15 16 Coefficients Standard Error t Stat P-value 0.0331 0.2156 17 Intercept 51.39 23.52 2.19 18 Lot size 0.700 0.559 1.25 19 Trees 0.679 0.229 2.96 0.0045 20 Distance -0.378 0.195 -1.94 0.0577 a) Write the regression equation b) What is the standard error of estimate? Interpret its value. c) What is the coefficient of determination? Interpret its value. d) What is the adjusted coefficient of determination? Interpret its value. e) Test the validity of the model. f) Interpret each of the coefficients. g) Test at 5% level of…

As an auto insurance risk analyst, it is your job to research risk profiles for various types of drivers. One common area of concern for auto insurance companies is the risk involved when offering policies to younger, less experienced drivers. The U.S. Department of Transportation recently conducted a study in which it analyzed the relationship between 1) the number of fatal accidents per 1000 licenses, and 2) the percentage of licensed drivers under the age of 21 in a sample of 42 cities. Your first step in the analysis is to construct a scatterplot of the data. FIGURE. SCATTERPLOT FOR U.S. DEPARTMENT OF TRANSPORATION PROBLEM U.S. Department of Transportation The Relationship Between Fatal Accident Frequency and Driver Age 4.5 3.5 3 2.5 2. 1.5 0.5 8. 10 12 14 16 18 20 Percentage of drivers under age 21 Upon visual inspection, you determine that the variables do have a linear relationship. After a linear pattern has been established visually, you now proceed with performing linear…

1. You are interested the causal effect of X on Y, B1. Suppose that X, and X2 are uncorrelated. You estimate B1 by regressing Y onto X1 (so that X2 is not included in the regression). Does this estimator suffer from omitted variable bias due to the exclusion of X2? (a) Yes (b) No (c) Maybe 2. Omitted variable bias violates which of the following assumptions: (a) The conditional distribution of u, given X1i X2i, ...Xki has a mean of zero (b) (Xi, X2i...Y;), i = 1, ., n are independently and identically distributed (c) Heteroskedasticity (d) Perfect multicollinearity

A manufacturer is developing a facility plan to provide production capacity for its factory. The amount of capacity required in the future depends on the number of products demanded by its customers. The data below reflect past sales of its products: Year Annual Sales (number of products) Year Annual Sales (number of products) 1 490 5 461 2 487 6 475 3 492 7 472 4 478 8 458 Use simple linear regression to forecast annual demand for the products for each of the next three (3) years, by using the tabular method  to: derive the values for the intercept and slope derive the linear equation plot the linear regression line develop a forecast for the firm’s  annual sales for each of the next three years

Table 1 Descriptive statistics and a summary of zero-order correlations and regression values for the Dark Triad (predictor variables) and chronotype (outcome variable). r ẞ (t) M (SD) Dark Triad -.14* [-.23**] 0.00 (0.80) Narcissism -.04 [-.05] .04 (0.60) 0.34 (0.18) Entitlement/ -.20** [-.36**] -.24 (-3.33**) 0.19 (0.26) exploitativeness Grandiose exhibitionism -.05 [.07] -.05 (-0.69) 0.30 (0.26) Leadership/authority .05 [.07] .18 (2.29*) 0.40 (0.26) Psychopathy -.13* [-.18**] -.09 (-1.05) 142.56 (27.99) Primary -.07 [-.11] .16 (-1.07) 75.16 (16.57) Secondary -.18** [-.26**] -.20 (-2.63**) 67.40 (14.54) Machiavellianism -.14* [-.23**] -.09 (-1.07) 75.16 (16.57) *p < .01, **p < .001, '-' denote variables not included in the regression. Correlations in brackets are the disattenuated values using the reliability for the scales. 1. turn your attention to Table 1 in Jonason et al. (2013). The first column represents the correlation between the chronotype index and dark traits while the…

8. Which of the following best describes the linear probability model? The model is the application of the linear multiple regression model to a binary dependent variable The model is an example of probit estimation The model is another form of logit estimation The model is the application of the multiple regression model with a binary variable as at least one of the regressors OO

SoCal Edison reported the following data for operating revenue and net income for 2001 through 2005. Year Operating Revenue (Millions), X Net Income (Millions), Y 2001 2270 96.9 2002 1482 89.1 2003 2138 103.9 2004 2260 81.6 2005 2600 78.1   Determine the least-squares regression line and interpret its slope. Estimate the net income if the operating revenue figure is $2500 million.

Water is being poured into a large, cone-shaped cistern. The volume of water, measured in cm³, is reported at different time intervals, measured in seconds. A regression analysis was completed and is displayed in the computer output. Regression Analysis: cuberoot (Volume) versus Time Predictor Coef SE Coef Constant -0.006 0.00017 -35.294 0.000 Time 0.640 0.000018 35512.6 0.000 s=0.030 R-Sq=1.000 R-sq (adj)=1.000 What is the equation of the least-squares regression line? Volume = 0.640 - 0.006(Time) Volume = 0.640 - 0.006(Time) Volume = -0.006 + 0.640(Time) Volume = - 0.006 + 0.640(Time?)

(Econmetrics) Q.1 How can you test for general misspecification of model if it would have only (any of) two independent variables?

10- What would be the consequences for the OLS estimator if heteroskedasticity is present in a regression model but ignored?* a. It will be biased b. It will be inconsistent c. It will be inefficient d. All of (a), (b), and (c) will be true

11. Which of the following statements is not true about multicollinearity? (a) Perfect multicollinearity will prevent you from being able to estimate a linear regression model. (b) Imperfect mulitcollinearity affects the individual t-statistics of the regressors. (c) Multicollinearity is defined as a linear relationship between different independent variables. (d) Imperfect multicollinearity affects model validity of the model. (e) The least squares estimators are unbiased in the presence of imperfect multicollinearity.

Q4. The Omantel firm has estimate the Sales of fibre internet connections in Oman with the related to advertising expenditure made by the company over the past 26 months. Following is the firm estimated results of the regression equation. DEPENDENT VARIABLE: Y R-SQUARE F-RATIO P-VALUE ON F OBSERVATIONS: 26 0.85121212 8.747 0.0187 PARAMETER STANDARD VARIABLE ESTIMATE ERROR T-RATIO P-VALUE INTERCEPT 7.6 6.33232 1.200 0.2643969 3.53 0.52228 ? 0.0001428 a. What is the dependent and independent variables in the above regression equation of Omantel firm? b. Calculate the estimated t-ratio. Test the slope estimates for statistical significance at the 10 percent significance level. d. Interpret the coefficient of determination.

Topic: Regression    Problem 3:  A researcher is interested in determining whether there is a relationship between grades and hours studied for statistics.   Hours studied(X) Grade on final(Y) 1 20 2 30 4 40 7 60 6 70 7 78 8 83 9 98   1- You are given data for Xi (independent variable) and Yi (dependent variable).      2- Calculate the correlation coefficient, r:             r =           -1 ≤ r ≤ 1     3- Calculate the coefficient of determination: r2 = =                0 ≤ r2 ≤ 1 This is the proportion of the variation in the dependent variable (Yi) explained by the independent variable (Xi)                4- Calculate the regression coefficient b1 (the slope):              b1 = = Note that you have already calculated the numerator and the denominator for parts of r.  Other than a single division operation, no new calculations are required.   5- Calculate the regression coefficient b0 (the Y-intercept, or constant):…

1.1 Which of the following is NOT a good reason for including a disturbance term in a regression equation?/   A. To allow for random influences on the dependent variable/ B. To allow for errors in the measurement of the dependent variable/ C. It captures omitted determinants of the dependent variable D. To allow for the non-zero mean of the dependent variable/   1.2 Consider the equation Y = B1 + B2X2 + u. A null hypothesis of H0: B2 = 0 means that/   A. X2 has no effect on the expected value of Y / B. B2 has no effect on the expected value of Y/ C. X2 has no effect on the expected value of B2 / D. Y has no effect on the expected value of X2/   1.3 The OLS residuals in the multiple regression model/   A. can be calculated by subtracting the fitted values from the actual values / B. are zero because the predicted values are another name for forecasted values / C. are typically the same as the population regression function errors / D. cannot be calculated because there…

Question: a) Calculate Regression Analysis of the data. b) Calculate Profit of a shop for 45 units sales

The following data relate the sales figures of the bar in Mark Kaltenbach's small bed-and-breakfast inn in portland, to the number of guest registered that week:                            week            guests         bar  sales                                 1                 16              $330                                 2                  12              $270                                 3                  18              $380                                4                   14              $315 a) The simple linear regression equation that relates bar sales to number of guests(not to time) is (round your responses to one decimal place):                           Bar sales = [___]+[___]X guests

5 We are given a sample of n observations which satisfies the following regression model: yi = β0 + β1xi1 + β2xi2 + ui , for all i = 1, . . . , n. This model fulfills the Least-Squares assumptions plus homoskedasticity. (a) Explain how you would obtain the OLS estimator of the coefficients {β0, β1, β2} in this model. (You do not need to show a full proof. Writing down the relevant conditions and explain)

10. Residual analysis Consider a regression of y on several independent variables, and the resulting predicted values of the dependent variable. The residual for the ith observation Consider a data set for a large sample of professional basketball players. Each observation contains the salary, as well as various performance statistics such as points, rebounds, and assists for each player. Suppose a regression of salary on all performance statistics is run, and the residuals are obtained. The player with the lowest (most negative) resid represents which of the following? (Assume the regression reasonably predicts salaries in most cases.) The most fairly paid player relative to her on-court performance The most overpaid player relative to her on-court performance The highest-paid player, regardless of her on-court performance The most underpaid player relative to her on-court performance

(2)What would the consequence be for a regression model if theerrors were not homoscedastic?

1. A Travel-Cost Method Now it is time for your first consulting job as an environmental economist. The Forest Service would like to know whether they should preserve some national forest land, previously slated to be logged, for hiking. You are helping do a travel-cost analysis to estimate the annual benefits of hiking in this area. Survey data has been gathered from 500 hikers who visited the forest last year. This forest did not charge an entrance fee. Using a statistical technique called regression analysis, you have controlled for differences in income, employment status, age, and other important factors that might affect the number of hiking trips taken. Taking these factors into account, you have developed the following relationship: Zone 1 2 3 Population in zone 1,000 3.000 2,000 Cost Per Hiking Trip Per Person 20 40 80 Hiking Trips Taken Per Zonal Person Per Year 8 6 2 where cost includes gas, tolls, fares, hotels, entry fees, and the value of time spent traveling. a. Graph a…