Regression Model
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Using these Factors we got the following regression model SUMMARY OUTPUT Regression Statistics Multiple R 0.999949977 R Square 0.999899957 Adjusted R Square 0.999499783 Standard Error 1.556319756 Observations 6 ANOVA df Regression Residual Total
Intercept
4 1 5
SS 24208.36087 2.422131183 24210.783
MS 6052.090217 2.422131183
F 2498.663268
P-value 0.076410504
Coefficients
Standard Error
-636.9924094
76.8244984
t Stat -8.29152708 8
10.35237955 0.023146274
2.755276053 0.000679989
3.757293046 34.03918342
0.165596952 0.018697179 0.073657693
0.000452368
PLR Minimum Vehicular Sales/ WPI GDP Factor cost Growth Rate Dollar-Rupee Exchange Rate
4.630387414
0.538145026
-0.889744822
0.775116617
8.604348627 -1.14788510 9
Diversification Factor
-0.252894963
0.107629005
1.720869725
0.456236839
Significanc eF 0.015002761
Lower 95% -1613.14021 5 -24.6567220 7 0.014506193 -2.20739346 1 -10.7385352 5 -0.25289496 3
Upper 95% 339.1553958 45.36148118 0.031786354 11.46816829 8.95904561 -0.25289496 3
Lower 95.0% -1613.14021 5 -24.6567220 7 0.014506193 -2.20739346 1 -10.7385352 5 -0.25289496 3
Upper 95.0% 339.1553958 45.36148118 0.031786354 11.46816829 8.95904561 -0.25289496 3
Intercept
4 1 5
SS 24208.36087 2.422131183 24210.783
MS 6052.090217 2.422131183
F 2498.663268
P-value 0.076410504
Coefficients
Standard Error
-636.9924094
76.8244984
t Stat -8.29152708 8
10.35237955 0.023146274
2.755276053 0.000679989
3.757293046 34.03918342
0.165596952 0.018697179 0.073657693
0.000452368
PLR Minimum Vehicular Sales/ WPI GDP Factor cost Growth Rate Dollar-Rupee Exchange Rate
4.630387414
0.538145026
-0.889744822
0.775116617
8.604348627 -1.14788510 9
Diversification Factor
-0.252894963
0.107629005
1.720869725
0.456236839
Significanc eF 0.015002761
Lower 95% -1613.14021 5 -24.6567220 7 0.014506193 -2.20739346 1 -10.7385352 5 -0.25289496 3
Upper 95% 339.1553958 45.36148118 0.031786354 11.46816829 8.95904561 -0.25289496 3
Lower 95.0% -1613.14021 5 -24.6567220 7 0.014506193 -2.20739346 1 -10.7385352 5 -0.25289496 3
Upper 95.0% 339.1553958 45.36148118 0.031786354 11.46816829 8.95904561 -0.25289496 3
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