regression result for your product is shown in the table below: Variable Parameter Estimates Standard error Constant -164.0 20.24 Price of good X (P,) Price of good Y (P,) -3.50 1.55 2.50 0.28 Per capita Income () 0.45 0.52 R-squared Adjusted R-squared 0.8672 0.8132 F-statistic 15.6893 a) Suppose the average price of 3 units of good X is GH¢12, price of 2 units of goodY is GH¢60, the per capita income of Ghana is GH¢420. Write down the estimated demand equation for your firm's product and interpret 1. the parameter estimates. Determine the quantity of good X sold. Estimate the own price elasticity of demand and state the type of demand curve 11. 111. your firm has? iv. What would be the effect of a price increase on the firm's total revenue? Assess the probable impact on your firm if the firm producing good Y decreases their price by 10%. Explain the adjusted R-squared Is the good Xa normal or inferior good? V. vi. vii.

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1. Consider a linear regression model y = XB + € with E(e) = 0. The bias of the ridge estimator of 3 obtained by minimizing Q(B) = (y — Xß)¹ (y — Xß) + r(BTB), for some r > 0, is ——(X²X + r1)-¹8 1 (X¹X +rI)-¹3 r -r(XTX+rI) ¹8 r(X¹X+r1) ¹3

18 Given that the sum of the squared deviations of EDUC is  9025.6   The standard error of the slope coefficient is ________.   a 0.1524           b 0.1249           c 0.1024           d 0.0839

18. A multiple regression model, K = a + bX + cY + dZ, is estimated regression software, which produces the following output: D. If X equals 50, Y equals 200, and Z equals 45, what value do you predict K will take?

The sum of squared residuals for the first group (SSR1) is 15.3 and for the second group (SSR2) is 25.7. Each group contains 12 observations after removing the central observations and this model has only one independent variable. Calculate the F-statistic (rounded to two dp) (a) 2.57 (b) 2.41 (c) 1.92 (d) 1.68 (e) 1.67

You estimated a regression with the following output.       Source |       SS           df       MS      Number of obs   =       472-------------+----------------------------------   F(1, 470)       >  99999.00       Model |  2.2728e+09         1  2.2728e+09   Prob > F        =    0.0000    Residual |  4246681.85       470   9035.4933   R-squared       =    0.9981-------------+----------------------------------   Adj R-squared   =    0.9981       Total |  2.2771e+09       471  4834590.83   Root MSE        =    95.055------------------------------------------------------------------------------           Y |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]-------------+----------------------------------------------------------------           X |   29.84419   .0595046   501.54   0.000     29.72726    29.96112       _cons |   88.27799   7.592427    11.63   0.000     73.35868    103.1973------------------------------------------------------------------------------…

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

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