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PPD 404

PPD 404. Robert A. Stallings RGL 200 Hours: Wednesdays 2:00 – 4:00 p.m. e-mail: rstallin@usc.edu Web page: www~rcf.usc.edu/rstallin.

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PPD 404

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  1. PPD 404 Robert A. Stallings RGL 200 Hours: Wednesdays 2:00 – 4:00 p.m. e-mail: rstallin@usc.edu Web page: www~rcf.usc.edu/rstallin

  2. Dates and exact weights of all required activities are as follows:Excel Exercise One Due September 20 2.5 percentFirst Examination September 25 20 percentSAS Exercise One Due October 16 10 percentSecond Examination November 8 25 percentExcel Exercise Two Due November 22 2.5 percentSAS Exercise Two Due December 6 10 percentFinal Examination December 13 30 percent

  3. Introduction to Statistical Analysis Involves mathematically manipulating quantitativedata Aim is to identify patterns that explain something about the “real world” Possible explanations are stated as hypotheses

  4. Hypotheses Essentially “hunches” about how things are related in the “real world” Statements linking two (or more) variables If X exists, then Y is likely to exist also Alternatively, Y = f(X)

  5. Variables Properties of objects that differ as you move from object to object • for example, people’s weight (in pounds) 110, 258, 160, 210, 175, 120, 300, 120, 193 • NOTE that to differ does NOT mean that two (or more) people cannot have the same weight • If everyone had the same weight, then weight would be a constant rather than a variable

  6. Variables (continued) Properties of objects that differ from one object to another can also be qualities rather than something that we measure (such as people’s weight) For example, people’s gender (female or male) can also be treated as a variable If everyone in a group of people were of the same gender, then gender would be a constant

  7. Variables can be one of two types • Discrete variables things that you can count and report frequencies e.g., the number of women in this room • Continuous variables things that you can measure and report values e.g., the ages of all students in the room

  8. Sirkin (pp. 34-52) identifies four types of variables: • Nominal-level variables these are discrete variables • Ordinal-level variables a “mixed” type • Interval-level variables equal-interval scales • Ratio-level variables equal intervals AND meaningful zero point

  9. Only TWO Statistical Tasks • Description Central Tendency Variability Association • Inference Estimation Hypothesis Testing

  10. A Diagnostic Exercise(for review purposes)

  11. 1. Solve for Y where a = 2, b = 6, c = 3, and d = 5

  12. Y = 0.2857Y = 0.286

  13. 2. Solve for Y where n = 74

  14. Solve for Y where X1 = 17, X2 = 23, and X3 = 31

  15. Y = (17 + 23 + 31)2Y = (71)2Y = 5,041.00

  16. 4. Solve for Y where X1 = 17, X2 = 23, and X3 = 31

  17. Y = 172 + 232 + 312Y = 289 + 529 + 961Y = 1,779.00

  18. 5. Round to the nearest three decimal places 0.6154 = 1.8485 = 2.6735 = 0.0046 =

  19. 0.6154 = 0.615 1.8485 = 2.6735 = 0.0046 =

  20. 0.6154 = 0.615 1.8485 = 1.848 2.6735 = 0.0046 =

  21. 0.6154 = 0.615 1.8485 = 1.848 2.6735 = 2.674 0.0046 =

  22. 0.6154 = 0.615 1.8485 = 1.848 2.6735 = 2.674 0.0046 = 0.005

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