Multiple Regression Analysis and Forecasting

IndepMac MultMac Temp Forecast Output Input both Delete_Input DL do_forecast DU export export2 H_autocorrelation H_colinearity H_durbinwatson H_equation H_forecast H_independent H_Input H_Logo_Click H_Parameters H_scatter keyflag overview scrForecast scrInput scrOutput x y No. of Variables No. of Observations R Square Adjusted R Square Standard Error F - Statistic Multiple Regression Equation Independent Analysis Coefficients Gradient Intercept Intercept Independent R-Square Matrix to +/- on result of Regression Equation Equation Parameters Adjusted for Sample Size bias Step 2 - Forecasting Third Order Polynomial Second Order polynomial Exponential Linear 3rd Ord Polynomial X3 X2 X Int R2 Exp 3rd poly 2nd poly exp linear Independent Variable Trend R-Squared Matrix Leave Blank 3rd Ord Poly 2nd Ord Poly Choose Method Time Period Number of Periods to Forecast 2nd Ord Polynomial R Squared Forecast Output Residual (e-et-1)^2 e^2 DL DU d dl du Positive Autocorrelation detected No Autocorrelation detected Negative Autocorrelation detected Durbin-Watson Statistic Auto Correlation Du Dl DW-Stat D Critical F-Statistic at 95% Confidence Adjusted R-Squared against other Indep Tests for Multicolinearity between Independent Variables Positive Autocorrelation maybe present Negative Autocorrelation maybe present Dependent Indep1 Indep2 Indep3 Page 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 000.00 00.00 00.00 0.00 00.00 0.00 -0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 00.00 00.00 0.00 0.00 0.00