If the t ratio for the slope of a simple linear regression equation is equal to 1.614 and the critical values of the t distribution at the 1% and 5% levels of significance, respectively, are 3.499 and 2.365, then the slope is   not significantly different from zero.   significantly different from zero at both the 1% and the 5% levels.   significantly different from zero at the 1% level but not at the 5% level.   significantly different from zero at the 5% level but not at the 1% level.

When Y is regressed on X, B, > 0, sx+ 0, sy # 0, and the fraction of the variation in Y explained by the least-squares regression of Y on X does not equal 1. In this example, the sum of the squares of the deviations of the actual Y-values from their mean would definitely be greater than the sum of the squares of the deviations of the predicted Y-values from their mean. 1 A) B) true false 4. When Y is regressed on X, the sample slope is negative, sx + 0, sy + 0, and Y; = Y; for every observation. In this example, the sum of the squares of the deviations of the predicted Y-values from their mean would definitely be less than the sum of the squares of the deviations of the actual Y-values from their mean. A) В) true false 3.

Prove that the slope of the sample regression function (beta2) is the BLUE if the assumptions of Gauss Marko theorem are satisfied make sure to show all the steps and the underlying assumptions

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Consider the following Cobb-Douglas production function for the bus transportation system in a city:  Q = Lβ1Fβ2Bβ3 Where  L = labour input in worker hours              F = fuel input in gallons              B = capital input in number of buses              Q = output measured in millions of bus miles   Suppose that the parameters (α, β1, β2 and β3) of this model were estimated using annual data for the past 25 years. The following results were obtained: β1 = 0.45, β2 = 0.20 and β3 = 0.30 a.         Determine the (i) labour, (ii) fuel, and (iii) capital-input production elasticities

Using a sample of recent university graduates, you estimate a simple linear regression using initial annual salary as the dependent variable and the graduate's weighted average mark (WAM) as the explanatory variable. If the regression model has an estimated intercept of 3200 and an estimated slope coefficient of 550, what is the predicted starting salary of a student with a WAM of 69? Answer:

Under the least square procedure, the larger the û (in absolute terms), the larger the a. Standard error b. Regression error c. Sum of squares residuals d. Difference between the true parameter and the estimated parameter O a. a O b. b О с. с ○ d. d

(a) Interpret the elasticity of cigarette consumption with respect to prices. (b) Does this regression model return the expected sign for this relationship? Explain. (c) Is the independent variable's coefficient statistically significant at a = 0.05? Explain. (d) As you have noticed, both the dependent and independent variables are defined in logs. Does this fact violate the linearity portion of CLRM Assumption I? Explain your answer. (e) A Shapiro-Wilk test on this model's residuals returns a p-value of 0.5329. Given this fact, is CLRM Assumption VII satisfied? Explain.

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