- 1.
- Let X be the length (cm) of a laboratory mouse and let Y be its weight (gm). Consider the data for X and Y given below. Obtain a scatterplot of the data and comment on the plot.
- 2.
- For the data set in Problem #1, eyeball a linear fit obtaining an estimate of the slope and the intercept.
- (a)
- Plot your fit.
- (b)
- Use your plotted fit, to predict the weight of a mouse that is 20 cm long.
- (c)
- Use your predicition equation to predict the weight of a mouse that is 25 cm long.
- (d)
- What does the estimate of slope mean in terms of the problem?
- (e)
- What does the estimate of intercept mean in terms of the problem?
- 3.
- Use the formulas given in class to determine the LS fit for the data given in Problem #1. (ANS: LS slope is: 2.405).
- 4.
- Plot your fit.
- 5.
- Compare the LS fit with your eyeball fit? Which is a better fit? Why?
- 6.
- Use the LS predicition equation to predict the weight of a mouse that is 25 cm long.
- 7.
- What does the estimate of slope mean in terms of the problem?
- 8.
- Use the regression module to scatterplot the data and obtain the LS and Wilcoxon fits. Write the Wilcoxon fit down.
- (a)
- Plot the Wilcoxon fit on your plot in #1.
- (b)
- Compare the Wilcoxn and the LS. Which is a better fit? Why?
- (c)
- Use the Wilcoxon predicition equation to predict the weight of a mouse that is 25 cm long.
- (d)
- What does the estimate of slope mean in terms of the problem?
- 9.
- The height weight data for the baseball players is given here. Obtain the scatterplot of this data and the LS and Wilcoxon fits.

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