ECON2900Y 5 Regression analysis was individually applied between the dependent v
ID: 3331765 • Letter: E
Question
ECON2900Y 5 Regression analysis was individually applied between the dependent variable Y and three independent variables Xi, X and Xs The following estimated regression equations with 202 observations were obtained Model 1 Model 2 100 25 X Model 3 Y=100-20 x. R60 R2 = 60 R2 = .60 S1-10 Sea 10 S-10 100-35Xs For a level of significance of 5%. a) Which variable can explain individually and statistically better the behaviour of Y? b) Could the F test be useful in this case? c) Can you predict the value of Y when x,-5X-5 and Xs-5 using any of the previous Models 1, 2 or 3? Regression analysis was also applied between the dependent variable Y and the three independent variables altogether X, X2 and Xs. The following estimated regression equation was obtained with 204 observations Model 4 Y=100-100 X: + 200 X2-100 X3 So, = 10 S02 = 10 Sea = 10 A/ 70 For a level of significance of 5%; d) Can you predict the value of Y when Xt 5, X2 5 and X3-5 e) Which model does fit better the data? f) Would you explain better the behaviour of Y using Xt, X2 and Xs altogether? Which model is the best? Why? 20 pointsExplanation / Answer
a) at 5% level of significane the variable x2 explain individually and statistically better behaviour of Y because unit change in x2 may cause to increase unit change in y.
b) Yes F test is useful in this case as we know observed or tabulated F value is greater than the F test statistic value then we say that there is significant of beta value.
c) from model 1, at x1 =5.
yht = 100 - 20 * 5 = 0.
From model 2 and x2 =5.
yht. = 100 + 25 * 5 =225.
d) From model 3 and x3 =5.
yht = 100 - 35*5 =100 - 175 = - 75.
For model 4 and x1=x2 =x3=5.
yht = 100 - 100 *5 +200*5-100*5.
= 100-500+1000-500.
=100.
e).
Model 4 does fit better the data because R Square is as. Orecompare to another models.
f) R Square is 0.70 which is well enough and explain model is good and adequate means behaves better with regressor x1, x2, x3.
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