Descriptive Statistics & Data Visualization: Study Notes

Module 2: Descriptive Statistics & Data Visualization

Overview

This module introduces foundational methods for summarizing and visualizing data in statistics. It covers types of variables, data summarization techniques, descriptive statistical terminology, and practical skills for exploring and interpreting real datasets using statistical software.

Learning Objectives

• Identify different types of variables and data.

• Understand appropriate methods for summarizing, describing, and displaying data.

• Calculate and interpret measures of central tendency and dispersion.

• Use SAS Studio to describe, graph, and explore data.

Section 1: Variables and Data

Variables and Data

In statistics, data refers to collected information before any computations are performed. A variable is a characteristic that differs from one person, place, or thing to another. Examples include age, sex, weight, and diastolic blood pressure.

Types of Variables

Variables are classified into two main types: Quantitative and Qualitative. Each type has further subcategories, as shown below:

Type	Subtypes	Description	Examples
Quantitative	Discrete	Countable numeric values	Number of daily admissions, number of abnormal cells
Quantitative	Continuous	Any value within a range	Height, weight, blood pressure
Qualitative	Categorical	Distinct categories, no logical order	Eye color, blood type
Qualitative	Binary (Dichotomous)	Two categories only	Sex (Male/Female), diabetes type
Qualitative	Ordinal	Natural order among categories	Cancer stage, pain score scale

Quantitative (Numeric) Variables

• Discrete: Quantitative variable that is countable. Example: Number of daily admissions to a hospital, number of abnormal cells.

• Continuous: Quantitative variable that can take on any value within a range. Example: Heights of adult males, weights of preschool children, blood pressure.

Qualitative (Character) Variables

• Categorical: No logical order, values fall into distinct categories. Example: Eye color, blood type.

• Binary (Dichotomous): Special case of categorical variable with only two categories. Example: Sex (Male/Female), diabetes type.

• Ordinal: Values have a natural order. Example: Cancer stage, pain score scale.

Examples: Identifying Variable Types

• Example 1: Number of school-aged children present in a household. Type: Quantitative, discrete.

• Example 2: Distance patient lives from the hospital. Type: Quantitative, continuous.

• Example 3: Telephone area code. Type: Qualitative, categorical.

Additional info: The notes continue with scales of measurement, summarizing data, and measures of central tendency and dispersion, which are standard topics in introductory statistics. These topics are essential for understanding how to analyze and interpret data in public health and other fields.