SPSS

Part I

SPSS is a statistical analysis program that allows:

Goals for this section of the course include:

Note: This is not a statistics course such as Math 207. We will only concentrate on basic statistical concepts.

Examining the Help utility within SPSS

SPSS has a very nice help utility as part of the application. Let's briefly examine this utility before diving into SPSS.

Creating a Simple Dataset

Part I: Let's go to the Tutorial section entitled "Using the Data Editor" and discuss the following:

Data View versus Variable View

10.1 Exercise

  1. Create the variables needed for the following dataset. They will all be numeric for starters.
Brand Name ServingPerPkg OzPerPkg Calories TotalFatInGrams SatFatInGrams
M&M/Mars Snickers Peanut Butter 1 2 310 20 7
Hershey Cookies 'n Mint 1 1.55 230 12 6
Hershey Cadbury Dairy Milk 3.5 5 220 12 8
M&M/Mars Snickers 3 3.7 170 8 3
Charms Sugar Daddy 1 1.7 200 2.5 2.5

Note: Variable names must begin with a letter and cannot contain spaces or any illegal characters. Let's use the following convention for variable namnes: 1) the name begins with a letter and 2) the variable can contain letters, numbers, an underscore, or a period.

  1. Switch to Data View and look at your variables.
  2. Change the type of Name to a String and the decimals column is to be 0, 0, 1, 2, 0, 1, 1.
  3. Create the Value Labels where 1 = "M&M/Mars", 2 = "Hershey", 3 = "Charms"
  4. Enter the data into the correct SPSS cells..

Next let's go to "Examining Summary Statistics for Individual Variables"

SPSS contains the following data types:

Q. What should the measures be for each of the variables in our dataset and why?

10.2 Exercise

  1. Create a frequency table of the brands of candy. That is, show the number of M&M/Mars, Hershey, and Charms candy.
  2. Create a Pie Chart of the same information using the frequency procedure.

Types of Data Analysis

When doing data analysis, we are interested in two types of summaries:

  1. Statistical Summaries (e.g. descriptive, hypothesis testing)
  2. Visual Summaries (e.g. tables, graphs)

Statistics is sometimes broken up into two different areas:

  1. Descriptive Statistics - a situation is described by the statistics by the collection, summarization, organization and presentation of data.
  2. Inferential Statistics - where inferences are made from samples of the population (e.g. smokers smoking a pack of cigarettes per day have a higher cholesterol). In this area we get into Hypothesis testing.

In the Descriptive Statistics world, we are concerned about each of the following. Just give a general description of the meaning of each of the following terms:

10.3 Exercise

  1. Find the Mean, Median, and Mode of the OzPerPkg, Calories, TotalFatInGrams, and SatFatInGrams.

10.4 Exercise

A paint manufacturer tested two experimental brands of paint over a period of months to determine how long they would last without fading. Here are the results:

Brand A Brand B
10 25
20 35
60 40
40 45
50 35
30 30

What do the descriptive statistics tell us about the paint with regard to fading?

Method #1: One way has two variable columns where the first is BrandA and the second is BrandB. Enter the above data and find the asked for information. Save this file as BrandMethod1.sav.

Method #2: The second way has two columns where the first column is a variable called Brand and the second column is called Fading. Create value labels where 1="BrandA" and 2="BrandB". Enter the information and find the asked for information. Save this file as BrandMethod2.sav.

When you have completed exercise 10.4 using Method#1 and Method#2, let me take a look at both answers.

10.5 Exercise

  1. To make sure we stay fresh with Excel for the final, here's a little problem. Generate 100 random integer numbers (i.e. the numbers do not contain any decimal places) between 1 and 20. Beside each number output "EVEN" or "ODD". Save this file as random.xls.
  2. Import this data into SPSS and create a Histogram and Pie Chart of the dataset.