How to interprete the minitab output of a regression analysis

5 00 observation order 13 12 11 10 9 8 7 6 5 4 3 2 1

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1.5 0.0 Observation Order Residual 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 Probabilit y Plot  of t he Residuals Residuals Versus t he Fit t ed Values Hist ogram of t he Residuals Residuals Versus t he Order of t he Dat a 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: Although the beta is significant and R sq adj is very high indicating that model is a very good fit to the data, there is violation of assumption of independence indicate that there is some other factor which is playing role behind the screen and we may have to study it further.
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