01 - INTRO DESCRIPTIVES (student)

Basic Concepts and Definitions

Statistics Overview

• Categories:

  • Descriptive Statistics: Collection, organization, summarization, and presentation of data.

  • Inferential Statistics: Drawing inferences about a larger group from a sample.

Fundamental Data Concepts

• Data Definition: Typically numerical values obtained through measurement or counting.

Variables

• Definition: Characteristics that vary and can represent different values.

  • Dependent Variable: Outcome of interest

  • Independent Variable: Influences changes in the dependent variable.

Types of Variables

• Quantitative: Measurable numbers (height, weight, age).

• Qualitative: Categorical variables (gender, color of hair).

• Discrete vs. Continuous:

  • Discrete: Whole numbers (hospital visits).

  • Continuous: Any value in a defined range (height, blood pressure).

Measurement Scales

Nominal Scale

• Description: Unordered categories; values are distinct and mutually exclusive.

• Examples: Gender, ethnicity (0=Male, 1=Female).

Ordinal Scale

• Description: Categories can be ranked but distances between them are not known.

• Examples: Pain scale, cancer stages.

Interval Scale

• Description: Ordered measurements where distances have meaning; "zero" is arbitrary.

• Examples: Temperature in Fahrenheit/Celsius, IQ scores.

Ratio Scale

• Description: Highest level of measurement with absolute zero.

• Examples: Weight, height.

Displaying Data

Frequency Tables and Graphs

• Frequency Table Example: Breakdown of women with breast cancer by tumor stage.

• Bar and Pie Charts: Visual representations of data for clarity, relationship, and comparisons.

• Stem-and-Leaf Plot: For showing data distributions while retaining individual observations.

• Boxplots: Effectively display continuous datasets with median, quartiles, and potential outliers.