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 Our group measured…  Arm length- From shoulder to farthest finger tip.  Wrist circumference- Wrapped tape measure around the subject’s wrist.  Lower.

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Presentation on theme: " Our group measured…  Arm length- From shoulder to farthest finger tip.  Wrist circumference- Wrapped tape measure around the subject’s wrist.  Lower."— Presentation transcript:

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2  Our group measured…  Arm length- From shoulder to farthest finger tip.  Wrist circumference- Wrapped tape measure around the subject’s wrist.  Lower Leg- (Had subject sit down) From top of knee to ankle.  Actual height and gender were also collected.

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4 Describe graph (form, direction, strength) Give correlation value (r) LSR line Interpret slope… Interpret r-squared… Describe residual plot (form, outliers) Is the linear model appropriate? Use correlation, residual plot, and original plot

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6 FEMALES: Describe female plot List Female LSR line List female correlation List female r-squared Describe female resid plot and comment on fit of linear model for females

7 MALES: Describe male plot List male LSR line List male correlation List male r-squared Describe male resid plot and comment on fit of linear model for males

8 Compare male and female data

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10 Describe graph (form, direction, strength) Give correlation value (r)  HEIGHT = a + b(wrist circ) Interpret slope… Interpret r-squared… Describe residual plot (form, direction, strength) Is the linear model appropriate? Use correlation, residual plot, and original plot

11 Same gender analysis as before See slides 5 – 8

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13 Describe graph (form, direction, strength) Give correlation value (r)  HEIGHT = a + b(arm length) Interpret slope… Interpret r-squared… Describe residual plot (form, direction, strength) Is the linear model appropriate? Use correlation, residual plot, and original plot

14 Same gender analysis as before See slides 5 – 8

15  Justify choice

16  Justify choices

17 (use gender specific models)  Partner #1  Lower leg measurement = 17.5 inches  Height = (17.5) = 68.1 inches  Actual Height = 64 inches  Residual = 64 – 68.1 = -4.1 inches  Overestimate  Partner #2  Lower leg measurement = 18 inches  Height = (18) = inches  Actual Height = 67 inches  Residual = 67 – = inches  Overestimate  Partner #3  Lower leg measurement = 17.5 inches  Height = (17.5) = 68.1 inches  Actual Height = 71 inches  Residual = 71 – 68.1 = 2.9  Underestimate

18 (use gender specific models)  Mrs. McNelis  Height = 2.68(17.5) = 68.1 inches  Mrs. Ladley  Height = 2.68(17.5) = 68.1 inches  Mr. Smith  Height = 5.2(17) = 66.8 inches  Mrs. Tannous  Height = 2.68(17.5) = 68.1 inches  Miss Gemgnani  Height = 2.68(17.5) = 68.1 inches  Mrs. Bolton  Height = 2.68(17) = 66.8 inches

19  State how confident you are in your predictions of the guest teachers, and JUSTIFY WHY!

20  Create linear model for best measurement and copy onto ppt

21 Do lin Reg t test using the output above Ho: β 1 = 0 Ha: β 1 >, <, ≠ 0 Conditions (with graphs! Do not say “see previous slides”) & statement t = b1 – 0 = # SEb P(t > ______|df = n—2) = We reject Ho…. We have sufficient evidence… Therefore…

22  Since we rejected Ho, we need to complete a conf. int. Statement b + t*(SE b ) = (____, ____) We are 90% confident that for every 1 X variable unit increase, the Y variable increases btw ____ and ____ units on average.

23 Summary Stats: (do not copy this table from Fathom. Instead, type these on your slide neatly)

24 Ho: μ males = μ females Ha: μ males ≠ μ females Conditions & statement t = mean1– mean2 = s s 2 2 n1 n2 2* P(t > _______| df = ___) = We reject….. We have sufficient evidence ….

25  If you reject Ho, complete an appropriate confidence interval

26 DO NOT put this type of table on your power point!! Write out the numbers neatly.

27 Ho: μ males = μ females Ha: μ males ≠ μ females Conditions & statement t = mean1– mean2 = s s 2 2 n1 n2 2* P(t > _______| df = ___) = We reject….. We have sufficient evidence ….

28  If you reject Ho, complete an appropriate confidence interval

29  List and explain possible sources of bias and error in your project.

30  Make conclusions about ALL of your data analysis Each measurement Each test of significance and confidence interval Overall conclusion about predicting heights Can be a bulleted list with explanation


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