Lab#3-in-class -RIGHT-solution.docx - Stat252 winter2019 Lab#3 in class Friday Feb 1st 2019 1 Lab#4(in class – Friday February 1st 2019 Example#1

Lab#3-in-class -RIGHT-solution.docx - Stat252 winter2019...

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Stat252 winter2019 Lab#3 in class- Friday, Feb 1 st , 2019 1 Lab #4 (in class) – Friday, February 1 st , 2019. Example #1: Predicting the heights of the criminal from the footprints. Can a footprint taken at the scene of a crime help to determine the height of the criminal? In other words, is there an association between height and foot length? A sample of 3 3 students measured their height (in inches) and their foot length (in centimeters). (Whenever you use JMP, cut and paste the appropriate output) Dataset “Lab#3-height-shoe-cm” 1) Using data “ Lab#3 – Height_Shoe” create a scatterplot of “How tall” vs. “Shoe-Cm” variables. Comment on whether these two variables appear to be associated. Positive strong linear relationship between height and shoe size 2) Report the correlation coefficient, r , between “How Tall” (later I will call this variable as “Height”) and Shoe Size – CM ( just “Shoe size”). r=0.859 3) Use JMP to determine the regression equation for predicting height from shoe size. Plot this line on a scatterplot (and submit). Record this equation. Interpret the slope of this equation. For every 1 cm increase in shoe size, height of the person will increase on average 2.05 inches 4) Report the value of r 2 and interpret it in the context of the problem. How to compute : r 2 r=.859 r 2 =(.859)^2=.737 How to compute : r 2 r 2 = SSR/SST= 436.26/591.87 =.737 How to interpret r 2 r 2 means that 73.7 % of the observed variability in people’s height ( Y) can be explained by an approximate linear relationship between height ( Y) and foot size (X) .
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