PPD 404

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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

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

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

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

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)

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

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

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

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

Only TWO Statistical Tasks
• Description

Central Tendency

Variability

Association

• Inference

Estimation

Hypothesis Testing

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