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How to interprete the minitab output of a regression analysis

# To improve the model we would like to delete those

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To improve the model, we would like to delete those observation and recompute the line. Step 5: Checking the validity of the assumptions: We made the assumptions that the all the error terms are identically and independently normally distributed with mean 0 and common variance sigma –square.

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Residual Percent 5.0 2.5 0.0 -2.5 -5.0 99 90 50 10 1 Fitted Value Residual 120 90 60 30 0 5.0 2.5 0.0 -2.5 -5.0 Residual Frequency 6 4 2 0 -2 -4 -6 6.0 4.5 3.0 1.5 0.0 Observation Order 15 14 13 12 11 10 9 8 7 6 5 4 3 2 1 5.0 2.5 0.0 -2.5 -5.0 Normal Probability Plot of the Residuals Residuals Versus the Fitt ed Values Histogram of t he Residuals Residuals Versus the Order of the Data Residual Plots for Weight Interpretation: 1. the graph on top left checks the assumption of normality of error terms. In this case we see that most of the points are clustered around blue line indication that the error terms are approximately normal. Thus our assumption of normality is valid. 2. The graph on top right plots the error terms against the fitted values. There are approximately half of them are above and half are below the zero line indicating that our assumption of error terms having mean zero is valid. 3. On the same graph we see the clear cyclic pattern among the error terms indicating that they are violating the assumption of independence of error. Error terms are not independent. May be there is another factor present in this example which we need to find out. 4. The bottom left graph again re-emphasizes the normality assumption. Though our sample size is just 15. 5. The bottom right graph is also important in this case because data is a time series and order of the data is important. A clear cyclic pattern indicates that error terms are dependent on the time variable. Step VI:
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How to interprete the minitab output of a regression analysis

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