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How to interprete the minitab output of a regression analysis

Step vi although the beta is significant and r sq adj

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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. Step VII: Let us estimate the value of y and interpret it Say for x = 14 we find and interval for the average value of y y-hat = 123 - 5.57 * 14 = 45.02 that is we expect that on the average the expected value of weight on the 14 th day approx 45 grams. 98% confidence interval: 45.02 ± t * .8441 = 45.02± 2.326*.8441= (43.0565, 46.9635)
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We are 98% confidant that that on the 14 th day the weight of the soap on the average lies between 43 grams and 47 grams approx. 98% prediction interval: 45.02± 2.326* 3.1163 = (41.9036, 48.1363) We are 98% confidant that on the 14 th day the predicted value of the weight of the soap lies between 42 grams and 48 grams approx. 25 20 15 10 5 0 0 0 0 0 0 0 0 0 S 2.94921 R-Sq 99.5% R-Sq(adj) 99.5% Regression 95% CI 95% PI Fitted Line Plot Weight =  123.1 - 5.575 Day (optional)For those who want to improve upon the model Quadratic fitting: compare the s-value and Rsq adj value with last model.
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25 20 15 10 5 0 140 120 100 80 60 40 20 0 S 1.95599 R-Sq 99.8% R-Sq(adj) 99.8% Regression 95% CI 95% PI Fitted Line Plot Weight =  127.3 - 6.744 Day + 0.05063 Day* * 2 Validation of assumptions in quadratic fitting: 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 2 0 -2 -4 Residual Frequency 3 2 1 0 -1 -2 -3 -4 4 3 2 1 0 Observation Order 15 14 13 12 11 10 9 8 7 6 5 4 3 2 1 2 0 -2 -4 Normal Probabilit y Plot  of t he Residuals Residuals Versus t he Fitt ed Values Histogram of t he Residuals Residuals Versus t he Order of t he Data Residual Plots for Weight
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How to interprete the minitab output of a regression analysis

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