Statistics is the science of data. Statistics is the science of data. 

Collecting, classifying, organizing, analyzing, interpreting, etc.

An experimental unit is an object (person or thing) upon which we collect data.

example: M&M’s can be an experimental unit.

A variable is a characteristic that differs or varies from one observation to the next.

Quantitative data consists of numbers. 

example: The number of M&M’s in a small bag is a piece of quantitative data.

Qualitative data consists of categories (or classes) that are non-numeric. 

example: The color of an M&M is a piece of qualitative data.

There are two types of quantitative data.

Discrete data is data that can assume only a countable number of values.

Continuous data is data that can assume any value in one or more intervals on the number line.

example: Consider an experiment whose population is the set of all Kennesaw State University students. Different statistics are:

height;
weight;
GPA;
number of math classes on a student's transcript;
number of classes the student is enrolled in this semester.

The height of a student is continuous. Height can assume any value in the interval (0", 120").

GPA is continuous. GPA can assume any value in the interval [0.00, 4.00].

The number of classes is discrete. The number of classes can assume any value in the set {0,1,2,3,4,5,...}.

A population is a collection of data that describe some phenomenon.

A sample is a subset of a population.

example: I want to know the average GPA of all KSU students. The population is the collection of all GPA’s for students at KSU. One possible sample is the collection of all GPA’s for students in Dr. DeMaio’s Statistics class. Another possible sample is a set of GPA’s from 30 students in the library.

Using a sample to make an inference about a population is called inferential statistics.

example: I want to know the average age of all students who attend a university in the greater Atlanta area.

The population is the set of all ages of students who attend a university in the greater Atlanta area.

Let sample S₁ be the collection of ages of students in a 8:25 PM class at KSU. Is S₁ a good sample to use to make an inference about the population?

When proper sampling techniques are used, inferential statistics provides a measure of reliability for the inference.

A sample must be carefully collected in order to make an accurate inference about the population.

If a sample is not carefully gathered the results are skewed or biased.

A random sample of n experimental units is one selected from a population in such a way that every different sample of size n has an equal chance of selection.

example: I want to know the average GPA of all KSU students. The population is the collection of all GPA’s for students at KSU. How should we collect a random sample?

Homework

Chapter One:
Section 1-2: 5-8, 17-20
Section 1-3: 1-5, 7, 8, 10, 11, 14, 17, 18, 19, 21, 23, 24, 26
Cumulative Review Exercises: 2-7, 9-12