Problem-
Yi
|
X1i
|
X2i
|
X3i
|
X4i
|
0.076
|
0.022
|
0.048
|
0.027
|
0.042
|
0.227
|
1.111
|
0.15
|
0.136
|
0.152
|
0.041
|
0.003
|
0.027
|
0.024
|
0.076
|
0.063
|
0.06
|
0.041
|
0.056
|
0.037
|
0.077
|
0.004
|
0.052
|
0.012
|
0.08
|
0.032
|
0.007
|
0.018
|
0.052
|
0.047
|
0.281
|
0.452
|
0.205
|
0.194
|
0.165
|
0.047
|
0.006
|
0.024
|
0.027
|
0.025
|
0.174
|
0.043
|
0.123
|
0.063
|
0.117
|
0.127
|
0.062
|
0.098
|
0.039
|
0.105
|
0.066
|
0.03
|
0.054
|
0.246
|
0.287
|
0.047
|
0.044
|
0.026
|
0.023
|
0.017
|
0.122
|
0.087
|
0.075
|
0.083
|
0.094
|
0.004
|
0.095
|
0.109
|
0.057
|
0.004
|
0.116
|
0.164
|
0.065
|
0.003
|
0.043
|
0.416
|
0.347
|
0.187
|
0.086
|
0.143
|
0.09
|
0.004
|
0.043
|
0.11
|
0.073
|
0.15
|
0.074
|
0.113
|
0.061
|
0.099
|
0.136
|
0.261
|
0.094
|
0.064
|
0.078
|
0.137
|
0.025
|
0.073
|
0.092
|
0.132
|
0.229
|
0.058
|
0.128
|
0.18
|
0.139
|
0.12
|
0.346
|
0.067
|
0.039
|
0.046
|
Consider the data, set in the above table to estimate the regression model
Yi = β0 + βlX1i + β2X2i + β3X3i + β4X4i + εi
a. Compute b0, b1, b2, b3 and b4.
b. Compute the predicted values for Yi{Yt).
c. Compute Se(b0), Se(b1), Se(b2), Se(b3) and Se(b4).
d. Plot the residuals (ei) against X1i and then against Yi
e. Test the hypothesis H0: β1 = 0 against H1: β1 ≠ 0 at 5% significance level.
f. Test the hypothesis H0: β4 = 1 against H1: β4 ≠ 1 at 1% significance.
g. Compute the coefficient of determination R2 and Adjusted - R2
h. Test the hypothesis that H0: β1 = β2 = β3 = β4 = 0 against the H1: H0 is not true at 1% significance level.
i. Test the hypothesis that H0: β1 = β2 = β3 = 0 against the H1: H0 is not true at 5% significance level.
j. Test the hypothesis that H0: β1 = β2 = 0 against the H1: H0 is not true at 1% significance level.
Additional information-
The question related to Basic Statistics and it discuss about establishing and calculating null hypotheses given in the question.