Statistics: Understanding Quantitative Data and Techniques for Analysis, Lecture notes of Analytical Techniques

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Aug. 20, 2012

Interpret results -- not a math course

• applying statistical techniques to data
• Statistics

1. Quantitative data
2. Summaries/calculations done on raw data (This class)
3. Techniques for collecting, analyzing, and interpreting data (This class)
4. (^) The science of creating and applying these techniques

• Why?
• Know enough about stats to not be mislead by them.
• Basic Concepts
• Variables: an attribute/characteristic that changes over time or varies from case to case that can be measured quantitatively.

■ Categorical [aka qualitative] -- vary in kind or quality (substantive property) but whose values have no inherent numerical value.

• Ordinal = ordered (ex: attitude towards drinking age)
• Nominal = not ordered; have no rank (ex: gender) _You cannot be more of a_* _nominal trait_* ■ Quantitative -- inherent numerical values
• Discrete = a count of something ; cannot be broken down from full units (ex: number of siblings)
• Continuous = can be broken down to any level of precision... theoretically (ex: age)
• Distribution: how values of a variable are spread over a range

■ Central tendency -- where center of distribution is (ex: median/mode) ■ Variability/Dispersion -- range of distribution (ex: standard deviation)

• Population: entire group (every member) that is relevant to analysis ■ Parameters ■ Unknown ■ Do not vary (fixed)
• Sample: subset of a population selected for analysis ■ Known ■ Varies ■ Hope sample is similar to population... but not fixed
• Descriptive statistics: describing your sample as a sample
• (^) Inferential statistics: use sample statistic to make inferences (informed

statements) about population parameter