a. Interpret the coefficient of service. b. In the regression model, the consumer goods as an industry is omitted from the model. Explain the implications of adding the binary variable goods to the regression model.
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- The regression table from STATA and the table that I created by looking at the values from STATA are attached. Question: Run a separate regression for each measure of social preferences (risk-taking, patience, trust) and compare whether subjective measures of individual’s math skills, their gender and age are important determinants. Summarize the results of the regressions in a table (each column representing one regression). Interpret the estimated coefficients and provide an intuition of what you have found out.The controller of Chittenango Chain Company believes that the identification of the variable and fixed components of the firm’s costs will enable the firm to make better planning and control decisions. Among the costs the controller is concerned about is the behavior of indirect-materials cost. She believes there is a correlation between machine hours and the amount of indirect materials used.A member of the controller’s staff has suggested that least-squares regression be used to determine the cost behavior of indirect materials. The regression equation shown below was developed from 40 pairs of observations.S = $200 + $9H where S = Total monthly cost of indirect materials H = Machine hours per month The estimated cost of indirect materials if 900 machine hours are to be used during a month is $8,300 (Assume that 900 falls within the relevant range for this cost equation.) The high and low activity levels during the past four years, as measured by machine hours, occurred…Suppose you work for North Dakota DNR Grand Forks office. DNR would like to know whether they should set aside some conservation land, previously slated to be logged, for a potential state park. You are helping do a travel-cost analysis to estimate the benefits of the set-aside. You collected data from 500 visitors who came to a state park in a neighboring state. You ran regression analysis and controlled for these visitors' age, income, education, employment status, and other important factors that might affect the number of visits. With all the information, you have developed the following relationship: (a) (b) Cost to Visit $20 $40 $80 # of Visits Per Person Per Year 8 6 2 Graph the demand curve for the number of visits as a function of the "price" -- the travel cost. Based on demographic information about the people living in the vicinity of the proposed park, you have estimated that 10,000 people will take an average of 4 visits per year. For the average person, calculate: (1) The…
- 13. Collinearity in a multiple regression analysis Suppose you want to examine the effects of a training program on future earnings using the following model: earn98= 4.64 +2.376train +0.371earn96 +0.366educ- 1.86 age +2.534 married (1.14) (0.43) (0.016) (0.062) (0.013) (0.4) where earn 98- 1998 earnings, in thousands of dollars train -1 if the individual participated in the training program, and =0 otherwise earn 96- 1996 earnings, in thousands of dollars educ years of education age = age, in years married-1 if the individual is married, and -0 otherwise Suppose that there is a high degree of correlation (but not perfect) between earnings in 1996, education, age, and marital status. True or False: We should be concerned about this high degree of correlation because it affects our ability to reliably estimate the impact of the training program on 1998 earnings, T. True False46) The following model is a simplified version of the multiple regression model used by Biddle and Hamermesh (1990) to study the trade-off between time spent sleeping and working and to look at other factors affecting sleep: sleep = Bo + B₁totwrk + ß₂educ + ß3age +u, where sleep and totwrk (total work) are measured in minutes per week and educ and age are measured in years. (i) If adults trade off sleep for work, what is the sign of f₁? (ii) What signs do you think ₂ and 3 will have? (iii) Using the data in SLEEP75, the estimated equation is sleep = 6,241.15 + 0.211totwrk + 9.22educ + 1.67age n = 211, R² = 0.981 If someone works five more hours per week, by how many minutes is sleep predicted to fall? Is this a large tradeoff? (iv) Discuss the sign and magnitude of the estimated coefficient on educ. (v) Would you say totwrk, educ, and age explain much of the variation in sleep? What other factors might affect the time spent sleeping? Are these likely to be correlated with totwrk?Your company just became international by offering its products in both the United States and Canada. Experts in your analytics department believe that tastes for your product differ in those two countries, and have carefully collected data on prices and quantity demanded in both countries. They then present you with the results of two regressions, one for each country, as follows: Log Price regressed on Log Quantity (United States): Standard Coefficients Error t Stat P-value Lower 95% Upper 95 % 1.67605E- Intercept 52.75573994 10.81051303 4.88040363 31.48708283 74.0239705 06 | Log Quantity -5.382266173 1.170584108 -4.597932039 6.15253E-06 -7.685279168 -3.079253177 Log Price regressed on Log Quantity (Canada): Standard Error Coefficients 22.8707593 10.64507785 -2.095788278 | 1.152727409 -1.818112644 0.069981782 t Stat 2.148482109 Upper 95% 43.8139384 P-value Lower 95% Intercept Log Quantity 0.032425603 1.9275802 -4.363669916 0.17209336 Assume you have adequate statistical significance…
- 1. Data were collected on sales of mountain bikes in 30 sporting goods stores. The regression model was y = total sales (thousands of dollars), *₁ = display floor space (square meters), 2 = competitors' advertising expenditures (thousands of dollars), and x3 = advertised price (dollars per unit). A summary of the regression output is below. Variable (nickname) Intercept FloorSpace Competing Ads Price Coefficient 1225.44 11.52 -6.935 -0.1496 (a) Write the fitted regression equation. Round your coefficient Competing Ads to 3 decimal places, coefficient Price to 4 decimal places, and other values to 2 decimal places. (b-1) Put an X in the correct answer circle. The coefficient of FloorSpace says that each additional square foot of floor space... O adds about 11.52 to sales (in thousands of dollars). takes away 11.52 from sales (in thousands of dollars). O adds about 6.935 to sales (in thousands of dollars). takes away 0.1496 from sales (in thousands of dollars). (b-2) Put an X in the…An economist believes that price, x, (in dollars) is the biggest factor affecting quantity sold, y. To support his argument, he collected data on price and quantity sold from a sample of 29 stores, selling the same product, and generated the regression output in Excel. The regression equation is reported as y = and the correlation coefficient r = - 0.333. 9.45x + 20.86 What proportion of the variation in quantity sold y can be explained by the variation in price? R² = % Report answer as a percentage accurate to one decimal place.Consider a linear demand model to explain the quantity demanded for a product: where Q = quantity sold, Price = price of the product, Income = purchaser’s income, Advert = advertising. The following data was collected in year 2018. The company spends millions of money in advertisements. The company wants to know how advertisement as well how other factors affect the quantity of units sold. The results are as follows: Model Summary R 0.986 R2 0.973 Standard error of estimate 6.9872 Variables Coefficient Std error Sig Constant 205.862 19.354 0.000 Price -12.242 1.407 0.000 Income 1.414 0.422 0.015 Advert -3,344 1.798 0.112 Interpret and write a report based on the results obtained above.
- Oil Recovery A U.S. state's Bureau of Economic Geology published a study on the economic impact of using carbon dioxide enhanced oil recovery (EOR) technology to extract additional oil from fields that have reached the end of their conventional economic life. The following table gives the approximate number of jobs for the citizens that would be created at various levels of recovery. Percent Recovery (%) 20 40 80 100 Jobs Created (Millions) 6 6 12 18 Find the regression line. j(r) = Use the regression line to estimate the number of jobs (in millions) that would be created at a recovery level of 50%. million jobsAn economist uses regression analysis to determine the relationship between used car price (y) and the age of a car (x). The analysis resulted in the following equation: Y = 30,000 - 500*X The above equation implies that an increase of O 1 year in the age of the car is associated with an increase of $500 in the price of the car O 1 year in the age of the car is associated with a decrease in $500 in the price of the car O $500 in the price of the car is associated with an increase of 5 years in the age of the car 5 years in the age of the car is associated with a decrease of $100 in the price of the carLiteracy rate reflects the educational facilities and quality of education available in a country, and mass communication plays a large part in the educational process. To relate the literacy rate of a country to various mass communication outlets, a demographer has proposed to relate literacy rate to the following variables: News = number of daily newspaper copies (per 1000 population) Radio = number of radios (per 1000 population) TV = number of TV sets (per 1000 population). The regression model to estimate the literacy rate (?̂ ) is below: ?̂= 0.5149 + 0.0005*News - 0.0003* TV + 0.002* Radio Answer the following questions: 4.1) Interpret the coefficient value of the TV variable in the model. 4.2) Predict the literacy rate for a country that has 200 daily newspaper copies (per 1000 in the population), 800 radios (per 1000 in the population), and 250 TV sets (per 1000 in the population). Show a detailed solution to support your answer.