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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 Leg- (Had subject sit down) From top of knee to ankle. Actual height and gender were also collected.

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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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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

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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

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Compare male and female data

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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

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Same gender analysis as before See slides 5 – 8

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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

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Same gender analysis as before See slides 5 – 8

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Justify choice

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Justify choices

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(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

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(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

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

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Create linear model for best measurement and copy onto ppt

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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…

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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.

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

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Ho: μ males = μ females Ha: μ males ≠ μ females Conditions & statement t = mean1– mean2 = s 1 2 + s 2 2 n1 n2 2* P(t > _______| df = ___) = We reject….. We have sufficient evidence ….

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If you reject Ho, complete an appropriate confidence interval

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

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Ho: μ males = μ females Ha: μ males ≠ μ females Conditions & statement t = mean1– mean2 = s 1 2 + s 2 2 n1 n2 2* P(t > _______| df = ___) = We reject….. We have sufficient evidence ….

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If you reject Ho, complete an appropriate confidence interval

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

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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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© 2000 Prentice-Hall, Inc. Chap. 9 - 1 Forecasting Using the Simple Linear Regression Model and Correlation.

© 2000 Prentice-Hall, Inc. Chap. 9 - 1 Forecasting Using the Simple Linear Regression Model and Correlation.

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