1 / 30

Predicting heights project

Predicting heights project. Introduction. 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.

jerod
Download Presentation

Predicting heights project

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Predicting heights project

  2. Introduction • 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.

  3. Height vs. Lower Leg

  4. Height vs. Lower Leg • 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

  5. GENDER VS. LOWER LEG

  6. FEMALES and Lower leg • 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 & LOWER LEG • 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

  9. Height Vs. Wrist Circumference

  10. Height Vs. Wrist Circumference • 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. Height and Wrist Circumference Based on Gender • Same gender analysis as before • See slides 5 – 8

  12. Height vs. Arm Length

  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. Height and arm length based on gender • Same gender analysis as before • See slides 5 – 8

  15. Best OVERALL Model – Lower Leg • Justify choice

  16. Best Model for each gender • Justify choices

  17. Our Predicted Heights (use gender specific models) • Partner #1 • Lower leg measurement = 17.5 inches • Height = 21.2 + 2.68(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 = 21.2 + 2.68(18) = 69.44 inches • Actual Height = 67 inches • Residual = 67 – 69.44 = -2.44 inches • Overestimate • Partner #3 • Lower leg measurement = 17.5 inches • Height = 21.2 + 2.68(17.5) = 68.1 inches • Actual Height = 71 inches • Residual = 71 – 68.1 = 2.9 • Underestimate

  18. Predicted teacher heights (use gender specific models) • Mrs. McNelis • Height = 2.68(17.5) + 21.2 = 68.1 inches • Mrs. Ladley • Height = 2.68(17.5) + 21.2 = 68.1 inches • Mr. Smith • Height = 5.2(17) + 10.4= 66.8 inches • Mrs. Tannous • Height = 2.68(17.5) + 21.2 = 68.1 inches • Miss Gemgnani • Height = 2.68(17.5) + 21.2 = 68.1 inches • Mrs. Bolton • Height = 2.68(17) + 21.2 = 66.8 inches

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

  20. Linreg t test (on best model) • Create linear model for best measurement and copy onto ppt

  21. Do linReg t test using the output above Ho: β1 = 0 Ha: β1 >, <, ≠ 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. Lin Reg Confidence interval • Since we rejected Ho, we need to complete a conf. int. Statement b + t*(SEb) = (____, ____) We are 90% confident that for every 1 X variable unit increase, the Y variable increases btw ____ and ____ units on average.

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

  24. 2 sample t test on the mean height of male/female Ho: μ males = μ females Ha: μ males ≠ μ females Conditions & statement t = mean1– mean2 = s12 + s22 n1 n2 2* P(t > _______| df = ___) = • We reject….. • We have sufficient evidence ….

  25. Confidence interval • If you reject Ho, complete an appropriate confidence interval

  26. Analysis of arm length by gender DO NOT put this type of table on your power point!! Write out the numbers neatly.

  27. 2 sample t test on the mean ARM LENGTH of male/female Ho: μ males = μ females Ha: μ males ≠ μ females Conditions & statement t = mean1– mean2 = s12 + s22 n1 n2 2* P(t > _______| df = ___) = • We reject….. • We have sufficient evidence ….

  28. Confidence interval • If you reject Ho, complete an appropriate confidence interval

  29. Bias and errors • List and explain possible sources of bias and error in your project.

  30. Conclusion • 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

More Related