For the following multiple linear regression equation Y=Bo+ B2X^2+B3coSx find the estimated parameters (Bo, B2, B3) using the ordinary least squares method for the following data 13 2. 505060 25 45 60 X,
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- . From the Exerrise 6.2(1). find the regression line using the least squares 11 lerhod . Interpret the result. Then. estimate the ntunber of hours he or she exercises per \\"week \vhen his or her age is 50 years old. Exercise 6.2(1). Age. x 18 26 32 38 52 59 Hours. y 10 5 -1 3 1. 5 1The following data is given: x -7 -4 -1 0 2 5 7 y 20 14 5 3 -2 -10 -15 Use linear least-squares regression to determine the coefficients m and b in the function y=mx+b that best fit the data.Suppose the least squares regression line for predicting weight (in pounds) from height (in inches) is given by Weight= -110+3.5*(height) Which of the following statements is correct? l. A person who is 61 inches tall will weigh 103.5 pounds ll. For each additional inch of height, weight will decrease on average by 3.5 pounds. lll. There is a negative linear relationship between height and weight. a) l and lll only b) l and ll only c) ll only d) l only e) ll and lll only
- Consider the following regression model Yt = β0 + β1 Ut + β2 Vt + β3 Wt + β4Xt + ∈t , where U, V, W, X and Y are economic variables observed from t = 1, . . . , 75, β0 , . . . , β4 are the model parameters and ∈t is the random disturbance term satisfying the classical assumptions. Ordinary Least Squares (OLS) is used to estimate the parameters, producing the following estimated model: Yt = 1.115 + 0.790*Ut − 0.327*Vt + 0.763*Wt + 0.456*Xt (0.405) (0.178) (0.088) (0.274) (0.017) where standard errors are given in parentheses, the R-squared = 0.941, the Durbin-Watson statistic is DW = 1.907 and the residual sum of squares is RSS = 0.0757. In answering this question, use the 5% level of significance for any hypothesis tests that you are asked to perform, state clearly the null and al- ternative hypotheses that you are testing, the test statistics that you are using and interpret the decisions that you make.…The following information pertains to a simple least squares regression for DEF Corporation: Mean value of the dependent variable 30Mean value of the independent variable 8Coefficient of the independent variable 3Number of observations 12 What is the "a" value for the leasts-quares regression model? a. 60b. 30c. 6d. 0The following data show the number of class sessions missed during a semester of SOC221 and the final grade for a sample of 8 students selected at random. Number of Sessions Missed Final Grade (x) (y) 0 96 2 88 12 68 6 91 8…
- Which of the following are feasible equations of a least squares regression line for the annual population change of a small country from the year 2000 to the year 2015? Select all that apply. Select all that apply: yˆ=38,000+2500x yˆ=38,000−3500x yˆ=−38,000+2500x yˆ=38,000−1500xFind the least-squares equation for the following pairs of data: x = earthquake magnitude 2.9 4.2 3.3 4.5 2.6 3.2 3.4 y = depth of earthquake (in km) 5 10 11.2 10 7.9 3.9 5.5 A. y = 2.16 + 0.221x B. y = 0.221 + 2.16x C. y = 2.16 + 0.312x D. y = 0.221 + 2.82xBased on the data presented in the table below, please calculate the values of b0 and b1 using OLS (ordinary least squares) and for the equation: q1 = β0 + β1pi + ui. qi pi 1 2 2 3 3 5
- Which of the following are feasible equations of a least squares regression line for the annual population change of a small country since the year 2000? Select all that apply. Select all that apply: A. yˆ=38,000+2500x B. yˆ=38,000−2500x C. yˆ=−38,000+2500x D. yˆ=−38,000−2500xAn experiment was performed on a certain metal to determine if the strength is a function of heating time. Results based on 10 metal sheets are given below. ∑ X = 30 ∑ X 2 = 104 ∑ Y = 40 ∑ Y 2 = 178 ∑ XY = 134 Using the simple linear regression model, find the estimated y-intercept and slope and write the equation of the least squares regression line.A “Cobb–Douglas” production function relates production (Q) to factorsof production, capital (K), labor (L), and raw materials (M), and an errorterm u using the equation Q = λKβ1Lβ2Mβ3eu, where λ, β1, β2, and β3 areproduction parameters. Suppose that you have data on production and thefactors of production from a random sample of firms with the same Cobb–Douglas production function. How would you use regression analysis toestimate the production parameters?
