Analysis of data for an autoregressive forecasting model produced the following

Question

Analysis of data for an autoregressive forecasting model produced the following tables. CoefficientsStandard Error t Statisticp-value 3.745787 0.844260.34299 0.082849-1.66023 0.103822 0.035709 14.650446.69E-19 3.85094 0.70434 0.62669 Intercept t-2 df MS p-value Regression Residual Total 135753.5 67876.76 107.33361.91E-17 27192.79 632.3904 162946.3 43 45 The results indicate that the first predictor, yt-1, is significant at the 10% level the second predictor, yt-2, is significant at the 1% level all predictor variables are significant at the 5% level none of the predictor variables are significant at the 5% level the overall regression model is not significant at 5% level

Explanation / Answer

Analysis of data for an autoregressive forecasting model produced the following tables. CoefficientsStandard Error t Statisticp-value 3.745787 0.844260.34299 0.082849-1.66023 0.103822 0.035709 14.650446.69E-19 3.85094 0.70434 0.62669 Intercept t-2 df MS p-value Regression Residual Total 135753.5 67876.76 107.33361.91E-17 27192.79 632.3904 162946.3 43 45 The results indicate that the first predictor, yt-1, is significant at the 10% level the second predictor, yt-2, is significant at the 1% level all predictor variables are significant at the 5% level none of the predictor variables are significant at the 5% level the overall regression model is not significant at 5% level

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