1. Chapter 1: Exploring Data
Introduction
Data Analysis: Making Sense of Data
The Practice of Statistics, 4th edition - For AP*
STARNES, YATES, MOORE

2. Chapter 1 Exploring Data
Introduction: Data Analysis: Making Sense of Data
1.1 Analyzing Categorical Data
1.2 Displaying Quantitative Data with Graphs
1.3 Describing Quantitative Data with Numbers

3. Introduction Data Analysis: Making Sense of Data
Learning Objectives
After this section, you should be able to…
DEFINE “Individuals” and “Variables”
DISTINGUISH between “Categorical” and “Quantitative” variables
DEFINE “Distribution”
DESCRIBE the idea behind “Inference”

4. Data Analysis Statistics is the science of data.
Data Analysis is the process of organizing, displaying, summarizing, and asking questions about data.
Data Analysis
Definitions:
Individuals – objects (people, animals, things) described by a set of data
Variable - any characteristic of an individual
Categorical Variable
– places an individual into one of several groups or categories.
Quantitative Variable
– takes numerical values for which it makes sense to find an average.

5. Dotplot of MPG Distribution
A variable generally takes on many different values. In data analysis, we are interested in how often a variable takes on each value.
Data Analysis
Definition:
Distribution – tells us what values a variable takes and how often it takes those values
Example
Dotplot of MPG Distribution
Variable of Interest:
MPG

6. Number of Family Members
Here is information about 10 randomly selected U.S. residents from the census imported using Fathom Software.
Who are the individuals in this data set?
What variables are measured? Identify each as categorical or quantitative. In what units were the quantitative variables measured?
Describe the individual in the first row.
Alternate Example
Data Analysis
State
Number of Family Members
Age
Gender
Marital
Status
Total Income
Travel time to work
Kentucky 2 61
Female
Married
21000 20
Florida 6 27
21300
Wisconsin
Male
30000 5
California 4 33
26000 10
Michigan 3 49
15100 25
Virginia 26
25000 15
Pennsylvania 44
43000 22
Never married/ single
3000 1 30
40000
New York 34
Separated 40

7. Data Analysis How to Explore Data Examine each variable by itself.
Then study relationships among the variables.
Start with a graph or graphs
Add numerical summaries

8. Data Analysis From Data Analysis to Inference Population Sample
Collect data from a representative Sample...
Make an Inference about the Population.
Perform Data Analysis, keeping probability in mind…

9. Activity: Hiring Discrimination
Follow the directions on Page 5
Perform 5 repetitions of your simulation.
Turn in your results to your teacher.
Teacher: Right-click (control-click) on the graph to edit the counts.
Data Analysis

10. Introduction Data Analysis: Making Sense of Data
Summary
In this section, we learned that…
A dataset contains information on individuals.
For each individual, data give values for one or more variables.
Variables can be categorical or quantitative.
The distribution of a variable describes what values it takes and how often it takes them.
Inference is the process of making a conclusion about a population based on a sample set of data.

11. Looking Ahead… In the next Section…
We’ll learn how to analyze categorical data.
Bar Graphs
Pie Charts
Two-Way Tables
Conditional Distributions
We’ll also learn how to organize a statistical problem.