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ee231_ass8 - 312 LINEAR REGRESSION continued best-fit line...

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312 LINEAR REGRESSION continued best-fit line folloss a Gaussian distribution, and that the standard deviation is the same at e er) \ alue of the dependent variable. These assumptions are rarely true after transforming data. As a consequence of the last conclusion, some analysts suggest that rather than using linear transformations. nonlinear regression should be employed to fit curvilinear data. in this approach. a best-fit curve is developed that directly minimizes the untransformed residuals, We will describe how this is done in Chap. 14. PROBLEMS 8.8 0.5 9.8 9.4 10.0 9.4 10.1 9.2 11.3 9.4 10.0 10.4 7.9 10.4 9.8 9.8 9.5 8.0 8.8 10.6 10.1 9.5 9.6 10.2 8.9 Determine (a) the mean, (b) median, (c) mode, (d) range. (e) standard deviation. (f) sariance. and (g) coefficient of ariation. 1 13.2 Construct a histogram from the data from Prob. 13.1 L se a range front 7.5 to 11.5 with inlers als of 0.5. 13.3 Given the data 28.65 2655 26,65 2765 27.35 28.35 26.85 28.65 29.65 27.85 27.05 28.25 28.85 26.5 27.65 28.45 28.65 28.45 31.65 26.35 27.75 29.25 2’.65 2.65 27.65 28.55 2’.65 2.25 Determine (a) the mean, bj median, (c) mode. d) lange. tel standaid des iation. (1) s ariance. and (gi coefficient of S ariation. dii Construct a histogram. I SC a I ange trun 2n to 32 v. ith increments f I iS. (I) Assunting that the distribution is normal, and that rour et in late of the standard des iation is al id. compute the range that is. the loss ci atid thc upper salues that en compasses 6S’ of the readings.

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