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How tall is the teacher?. Amanda Vonder Schmalz Lauren Hoover Marlee Levin. What did we measure?. Height. Neck Circumference. Hand Length. Foot Length. Marked 77 inches on the white board Placed ruler above heads to accurately measure height. Used a tape measure Hair pulled up
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How tall is the teacher? Amanda VonderSchmalz Lauren Hoover Marlee Levin
What did we measure? Height Neck Circumference Hand Length FootLength • Marked 77 inches on the white board • Placed ruler above heads to accurately measure height • Used a tape measure • Hair pulled up • Started where hairline ended • Used tape measure • Right hand • Palm to tip of middle finger • Measured with ruler taped to floor • Subjects asked to take off shoe • Right foot • Heel to tip of big toe
Neck Circumference vs. Height • Linear • Positive • Moderately Strong • R= .5916 • R2 = .35 • Ŷ= 1.2(Neck Circumference)+49.9
Residual Plot: Neck Circumference The plot shows a slight pattern as it gets larger in the center of the data, but once the outlier is removed, it is more of a scattered shape. The correlation got stronger with the removal of the outlier. 35% (with) or 48.0% (without) of the change in height is due to the change in neck circumference. Rw= .5916 Rw/o= .6928 Without Outlier
Neck Circumference & Gender • Female • Linear • Negative • Weak • R= .2366 • R2= .056 • Ŷ=.47(Neck Circum.)+70.4 • Male • Linear • Positive • Weak • R= .33 • R2= .11 • Ŷ=.571(Neck Circum.)+61.2
Hand Length vs. Height • Linear • Positive • Strong • R=.7483 • R2=.56 • Ŷ= 5.72(Hand Length)+ 26.6
Residual Plot: Hand Length There seems to be a slight negative curve to the right of the residual plot. It is not very scattered, therefore the line is not the best fit for our data. 54.0% of the change in height is due to the change in hand length. R = .7348
Hand Length & Gender • Female • Linear • Positive • Moderately Strong • R= .5196 • R2= .27 • Ŷ= 4.43(hand length)+34.2 • Male • Linear • Positive • Moderately Weak • R= .4358 • R2= .19 • Ŷ= 2.08(hand length)+54.8
Foot Length vs. Height • Linear • Positive • Strong • r= .6782 • r2 = .46 • Ŷ= 2.99(Foot Length)+ 41
Residual: Foot Length There seems to be a somewhat cone shaped pattern in our data. The data gets increasingly larger towards the center of our residual plot, therefore this line is not the best fit for our data. 46.0% of the change in height is due to the change in foot length. R= .678
Foot Length & Gender • Female • Linear • Positive • Weak • R= .31 • R2= .097 • Ŷ=1.17(foot length)+54.7 • Male • Linear • Positive • Weak • R= .273 • R2= .075 • Ŷ=.987(foot length)+61
Best Models • Neck Circumference • This residual plot had the least amount of patterning!
Our Residuals Residual = Actual - Predicted
Predicting Teacher Heights Mr. Lake * MOST CONFIDENT Mr. Walsh* MOST CONFIDENT • Ŷ= 1.2(17)+49.9 = 70.3 • We are confident in this because our residuals were all low and we also saw less of a pattern in the neck circumference residual plot. • Ŷ= 1.2(14)+49.9= 67.96 • We are confident in this because our residuals were all low and we also saw less of a pattern in the neck circumference residual plot.
Teacher Predictions Cont’d Tannous Robinson • Ŷ= 1.2(12.5)+49.9 = 64.9 • We are confident in this because our residuals were all low and we also saw less of a pattern in the neck circumference residual plot. • Ŷ= 1.2(14.5)+49.9 = 67.3 • We are confident in this because our residuals were all low and we also saw less of a pattern in the neck circumference residual plot.
Bias & Error • Some subjects left flat shoes, such as flip flops, on their feet throughout the measurements. • All measurements rounded to nearest ¼ inch • Ruler may not have been completely parallel when measuring height • Different heights at different stations* • Inaccurate markings on white board • Weight effected certain measurements, which threw off predictions
Conclusion • From our data of 55 subjects, including the 5 teachers, for females we found that the best measurement used to estimate height was neck circumference. For males, we found that foot length worked best. Overall, when predicting the teachers heights, we were more confident in the males heights because we know the teachers and their general height in comparison to our own. There were many sources of bias that could have affected our data.