Question 4In a study of the relationship between real aggregate consumption and pricesin a specific country based on a time series of 104 observations, the followingOLS regression function was obtained (standard errors in parentheses),R = 0.12,Ct = 13.74 + 0.24 P,(0.35)SER= 2.85,(1)(0.05)where C; is logarithm of consumption and P, is logarithm of price(a) Is the coefficient on P statistically significant?

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Answered: Question 4 In a study of the…

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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Table 8: Regression of Weight Loss on Hours of Exercise and Caloric Intalk Variable se b 95% CI Hours of Exercise 0.27 0.11 0.05, 0.49 Caloric Intake -0.002 .0008 -0.0035, -0.0004 Intercept 8.50 0.73 7.05, 9.95 Note: R? = .35, adj. R? = .35, F = 22.18, df = 2,117; n = 120 %3D swer the following questions about this regression analysis. Which predictors of weight loss are statistically significant, if either? What is the predicted weight loss for someone who consumes 2,000 calori hour per day on average? What is the literal interpretation for the slope of hours of aerobic exercise
Question 2 A regression was run to determine if there is a relationship between hours of study per week (x) and the final exam scores (y). The results of the regression were: y=ax+b a=6.992 b=38.9 > r²=0.779689 r=0.883 Use this to predict the final exam score of a student who studies 4 hours per week, and please round your answer to a whole number.
Consider the following estimated regression model relating annual salary to years of education and work experience. Estimated Salary = 11,433.70 + 3312.68(Education) + 819.54 (Experience ) Suppose an employee with 7 years of education has been with the company for 5 years (note that education years are the number of years after 8th grade). According to this model, what is his estimated annual salary? 囲 Tables E Keypad Answer Keyboard Shortcuts
Question 49 Regression analysis was applied and a significant relationship between product demand (Y) and the product price (X) was found. The following estimated regression equation was obtained. (Yhat ) = 120 - 10 X Based on the above estimated regression equation, if price is increased by 10 units, then demand is expected to o decrease by 10 units O decrease by 100 units O increase by 100 units O incease by 10 units O none of these * Previous Quiz saved at 10:26am Sub o ooC
Data was recorded for the temperature, in degrees Celsius, of a cup of coffee over a 30-minute period. Given the regression equation, In(Temp) = 4.20 0.023(Time), what is the predicted temperature after 3 minutes? 33.45 °C 62.24 °C 65.17 °C 66.69 °C
Consider the following estimated regression model relating annual salary to years of education and work experience. Estimated Salary = 10,815.11 +2563.46 (Education) +897.49(Experience) Suppose two employees at the company have been working there for five years. One has a bachelor's degree (8 years of education) and one has a master's degree ( 10 years of education). How much more money would we expect the employee with a master's degree to make? Answer Tables Keypac Keyboard Shortcu
Consider the following estimated regression model relating annual salary to years of education and work experience. Estimated Salary = 10,896.07 + 2755.34 (Education) + 773.89 (Experience) Suppose two employees at the company have been working there for five years. One has a bachelor's degree (8.years of education) and one has a master's degree ( 10 years of education). How much more money would we expect the employee with a master's degree to make?
What happened to the standard error of educ after adding KWW to the model? Discuss. Do you agree or disagree with the following statement? “If the log of the dependent variable appears in the regression, changing the unit of measurement of any independent variable affects both the slope and intercept coefficients”. Discuss by providing the resource.
QUESTION 7 In order to estimate the effect on wage of years of schooling, researcher B analysed the dataset that researcher A collected in the previous question. Researcher B considers the following regression equation, log(income);=Bo+Bieduc;+ + u; %3D where educ, is years of school of individual j and she realised that unobserved factors such as motivation, and work ethic, would determine the income level and be correlated with the variable of interest i ... includes additional terms associated with other characteristics in the data set. When researcher B was carefully examining omitted factors in the error term Up regarding the unobserved factors is correct, then: educ; If researcher B's conjecture O a. The OLS estimator a would not be computed. Ob. The OLS estimator B1 would overestimate the effect on income, of educ; c. The OLS estimator B1 would would suffer imperfect multicollinearity. Od. The OLS estimator B1 would have a large standard error. Oe. The OLS estimator B1 would…
A study done in 1994, of Maryland's portion of Chesapeake Bay found the following information about stocks of market-sized oysters. The stock population was 218 million, the stock will not exceed 5089 million oysters, and the intrinsic exponential growth rate is 0.274 per year.
Question 38. You have performed a linear regression analysis to explore sunflowers' growth (in meters per month) depending on the watering (in litres per day). You have estimated the regression coefficient to be ß = 1.6. What can you conclude? a) There is a signifleant correlation between watering and growth. b) An average sunflower growths 1.6 meters per month. c) If you give it an additional litre of water per day, there will be an additional average growth of 1.6 meters per month. d) According to the model assumptions, an additional litre of water per day will result in additional 19.2 meters of growth after one year. e) You should consider further influencing quantities.
Question 4. Suppose that a random sample of 200 twenty-year-old men is selected from a population and that these men's height and weight are recorded. A regression of weight on height yields Weight = -99.41 +3.94 x Height, R² = 0.81, SER = 10.2 where Weight is measured in pounds, Height is measured in inches and SER stands for the standard error of the regression. (1) What is the regression's weight prediction for someone who is 70 in. tall? 65 in tall? 74 in. tall? (2) A man has a late growth spurt and grows 1.5 in. over the course of a year. What is the regression's prediction for the increase in this man's weight? (3) Suppose that instead of measuring weight and height in pounds and inches these vari- ables are measured in centimeters and kilograms. What are the regression estimates from this new centimeter-kilogram regression? (Give all results, estimated coefficients, R², and SER)

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