I. Types of Data

Before we can perform data analysis, we need to identify the different types of scales that measure various kinds of data. Exactly how we measure data depends on the type of data, since each type requires specific scales and is measured differently. In this lesson, we discover how to measure the various types of statistical data.

1. Statistical Scales

 1) Nominal

 2) Ordinal

 3) Interval

 4) Ratio

2. Variable Categories

 1) Discrete

 2) Continuous

 3) Qualitative

 4) Quantitative

 5) Independent

 6) Dependent

II. Descriptive Statistics

Descriptive statistics are numbers we use to describe data that we see everywhere like economic reports, average rainfall, or the average amount we spend on groceries. We even have smartwatches that provide us with descriptive statistics like our average amount of sleep, exercise, and calories. Descriptive statistics have interesting properties like central tendency and dispersion that we explore in this section.

 1. Measures of Central Tendency

1) Mean

2) Median 

3) Mode

2. Measures of Dispersion

1) Standard deviation

2) Variance

3) Range 

III. Displaying Statistical Data

Graphing and displaying data is the first and often most important step in data analysis. This step provides information that we use as the basis for major decisions. This section examines graphical methods for displaying data. We'll investigate different types of graphs and learn when to use each type. We will review methods to graph and display quantitative and qualitative data.

1. Analyzing graphs and charts for statistical data

2. Frequency Tables

3. Quantitative Variables

1) Histograms

2) Line Graphs

4) Scatter Diagrams

4. Qualitative Variables 

1) Pie Charts

2) Bar Charts

5. Interpreting statistical data from graphs and charts

 IV. Statistical Distributions

How data is distributed and the shape of these distributions offer valuable information for data analysis. We will discover some important distributions that are commonly seen in data analysis. We also explore factors that influence distribution shape, and why the shape is important for interpreting data. We will explore the importance of creating frequency tables and frequency distributions.

1. Types of statistical distributions 

2. Frequency tables

3. Normal distribution

4. Skewed distributions