How do I calculate UCL/LCL (95%) from the following available data; Coefficient, Standard Error, T-Stat, P-Value.
df for Residual is 194
Critical t value for 95% confidence interval and df = 194 is 1.972
Upper 95% level = Coefficient + 1.972 * Standard Error
Lower 95% level = Coefficient - 1.972 * Standard Error
For Intercept,
Upper 95% level = 45482.366 + 1.972 * 19403.8863 = 83746.83
Lower 95% level = 45482.366 - 1.972 * 19403.8863 = 7217.902
For v1,
Upper 95% level = -10383.543 + 1.972 * 3153.7202 = -4164.407
Lower 95% level = -10383.543 - 1.972 * 3153.7202 = -16602.68
For v2,
Upper 95% level = 11.088 + 1.972 * 10.4859 = 31.76619
Lower 95% level = 11.088 - 1.972 * 10.4859 = -9.590195
For v3,
Upper 95% level = 783.388 + 1.972 * 175.8223 = 1130.11
Lower 95% level = 783.388 - 1.972 * 175.8223 = 436.6664
For v4,
Upper 95% level = 0.014 + 1.972 * 0.0023 = 0.0185356
Lower 95% level = 0.014 - 1.972 * 0.0023 = 0.0094644
For v5,
Upper 95% level = -2.546 + 1.972 * 1.2209 = -0.1383852
Lower 95% level = -2.546 - 1.972 * 1.2209 = -4.953615
How do I calculate UCL/LCL (95%) from the following available data; Coefficient, Standard Error, T-Stat, P-Value....
Have I correctly calculated the standard error in this problem? Using the following: Se = SQRT(SSE / n-k-1) = 39368362197.53 / (200-5-1) = 1425.3397 Alternative: Se = SQRT/MSE = SQRT/ 202929702.05 = 14245.3397 SUMMARY OUTPUT Regression Statistics Multiple R R Square Adjusted R Square Standard Error Observations 0.9613 0.9241 1425.3397 ANOVA Significance df MS Regression Residual Total 479410417802.47 95882083560.49 472.4892 39368362197.53 518778780000.00 0.00 194 199 202929702.05 Upper 95% CoefficientsStandard Erro t Stat P-value Lower 95% 45482.366 -10383.543 11.088 738.388 0.014...
Have I calculated the F-calc correctly in this problem? Using: F-calc = MSR / MSE = 95882083560.49 / 202929702.05 = 472.4892 SUMMARY OUTPUT Regression Statistics Multiple R R Square Adjusted R Square Standard Error Observations 0.9613 0.9241 0.9222 1425.3397 200 ANOVA Significance df MS Regression Residual Total 479410417802.47 95882083560.49 472.4892 39368362197.53 518778780000.00 0.00 194 199 202929702.05 Upper 95% CoefficientsStandard Erro t Stat P-value Lower 95% 45482.366 -10383.543 11.088 738.388 0.014 2.546 19403.8863 3153.7202 10.4859 175.8223 0.0023 1.2209 Intercept 2.340.0201 7217.90...
Have I calculated the degrees of freedom correctly in this problem? Using the formulas below: Degrees of Freedom (df) = Sum of Square / Mean Square Regression = 479410417802.47 / 95882083560.49 = 5 Residual = 39368362197.53 / 202929702.05 = 194 Total = Regression df + Residual df = 5 + 194 = 199 SUMMARY OUTPUT Regression Statistics Multiple R R Square Adjusted R Square Standard Error Observations 0.9613 0.9241 0.9222 1425.3397 200 ANOVA Significance MS Regression Residual Total 479410417802.47 95882083560.49...
Have I correctly calculated R squared in this problem? SUMMARY OUTPUT Regression Statistics Multiple R R Square Adjusted R Square Standard Error Observations 0.9241 0.9222 1425.3397 200 ANOVA Significance df MS Regression Residual Total 479410417802.47 95882083560.49 472.4892 39368362197.53 518778780000.00 0.00 194 199 202929702.05 Upper 95% CoefficientsStandard Erro t Stat P-value Lower 95% 45482.366 -10383.543 11.088 738.388 0.014 2.546 19403.8863 3153.7202 10.4859 175.8223 0.0023 1.2209 2.340.0201 Intercept V1 v2 7217.90 83746.83 -3.290.0012 16602.68 -4164.41 31.77 391.67 1085.11 0.02 0.14 1.06 0.2916...
In the following table, which parameters are significant and why? SUMMARY OUTPUT Regression Statistics Multiple R R Square Adjusted R Square Standard Error Observations 0.9613 0.9241 0.9222 1425.3397 200 ANOVA Significance df MS Regression Residual Total 479410417802.47 95882083560.49 472.4892 39368362197.53 518778780000.00 0.00 194 199 202929702.05 Upper 95% value 2.34 0.0201 CoefficientsStandard Erro t Stat Lower 95% 45482.366 -10383.543 11.088 738.388 0.014 2.546 19403.8863 3153.7202 10.4859 175.8223 0.0023 1.2209 Intercept V1 v2 7217.90 83746.83 -3.290.001216602.68 -4164.41 31.77 391.67 1085.11 0.02 0.14...
In the following table, which parameter is the most significant; least significant; why? SUMMARY OUTPUT Regression Statistics Multiple R R Square Adjusted R Square Standard Error Observations 0.9613 0.9241 0.9222 1425.3397 200 ANOVA Significance df MS Regression Residual Total 479410417802.47 95882083560.49 472.4892 39368362197.53 518778780000.00 0.00 194 199 202929702.05 Upper 95% value 2.34 0.0201 CoefficientsStandard Erro t Stat Lower 95% 45482.366 -10383.543 11.088 738.388 0.014 2.546 19403.8863 3153.7202 10.4859 175.8223 0.0023 1.2209 Intercept V1 v2 7217.90 83746.83 -3.290.001216602.68 -4164.41 31.77 391.67...
In determining if this regression is significant, I observed the following, am I taking the correct approach? To check if your results are reliable (statistically significant), look at Significance F (0.00). If this value is less than 0.05, the regression is acceptable. If Significance F is greater than 0.05, it's advisable to stop using this set of independent variables. As part of the hypothesis test, we should evaluate R-squared as it measures the strength of the relationship between the model...