1. Course Topics
Simple statistical methods for data analysis using Excel.
• descriptive statistics,
• an introduction to statistical inference, and
• linear regression models.
Excel workbooks for computing elementary statistics
using the Data Analysis toolkit.
Transferring digital information (graphs and tables) into
Word documents, developing presentations in Power
Publishing documents on the web
2. Statistics with Microsoft Excel by B.J. Dretzke
(Recommended for students that are not
familiar with Excel)
Introduction to the Practice of Statistics, by
David S. Moore and George P. McCabe
Elementary Statistics (2002), by M. F. Triola.
The Basic Practice of Statistics (2000), by D.S.
3. Useful links
Surfstat: an online text in introductory
Statistics at Square One:
The DePaul University library offers a number
of good books on Excel using books 24X7: IT
4. Getting ready for the class
• Open Excel
• Check that the Tools menu contains the Data Analysis
• If not, use Tools|Add Ins… and click on box labeled
5. The goal of data analysis is to gain information from the data.
Long listings of data are of little value.
Statistical methods come to help us.
Exploratory data analysis: set of methods to display and summarize the data.
Data on just one variable: the distribution of the observations is analyzed by
I. Displaying the data in a graph that shows overall patterns and unusual
observations (stem-and-leaf plot, bar chart, histogram, box plot, density
II. Computing descriptive statistics that summarize specific aspects of the
data (center and spread).
Exploratory Data Analysis
6. Data contain information about a group of individuals or subjects
A variable is a characteristic of an observed individual which takes
different values for different individuals:
Quantitative variable (continuous) takes numerical values.
Ex.: Height, Weight, Age, Income, Measurements
Qualitative/Categorical variable classifies an individual into
categories or groups.
Ex. : Sex, Religion, Occupation, Age (in classes e.g. 10-20, 20-30, 30-
The distribution of a variable tells us what values it takes and how often it
takes those values
Different statistical methods are used to analyze quantitative or categorical
7. Pie chart
Graphs for categorical
The values of a categorical variable are labels.
The distribution of a categorical variable lists the count or
percentage of individuals in each category.
Wireless surfers by Age
18-34 35-54 55>
A sample of 400 wireless internet users.
Counts: 212 168 20
8. Wireless surfers by gender
Wireless internet users
Male 288 (72%)
Female 112 (28%)
Total 400 (100%)
9. Survived Dead
Male Female Male Female
First class 62 141 118 4
Second class 25 93 154 13
Third class 88 90 422 106
Crew members 192 20 670 3
Example: On the morning of April 10, 1912 the Titanic
sailed from the port of Southampton (UK) directed to NY.
Altogether there were 2,201 passengers and crew
members on board. This is the table of the survivors of
the famous tragic accident.
10. Example: CEO salaries
Forbes magazine published data on the best small firms in 1993. These were firms with
annual sales of more than five and less than $350 million. Firms were ranked by five-
year average return on investment. The data extracted are the age and annual salary of
the chief executive officer for the first 59 ranked firms.
Salary of chief executive officer (including
bonuses), in $thousands
145 621 262 208 362 424 339 736 291
58 498 643 390 332 750 368 659 234
396 300 343 536 543 217 298 1103 406
254 862 204 206 250 21 298 350 800
726 370 536 291 808 543 149 350 242
198 213 296 317 482 155 802 200 282
573 388 250 396 572
11. 1. Construct a distribution table:
i. Define class intervals or bins (Choose intervals of equal width!)
ii. Count the percentage of observations in each interval
iii. End-point convention: left endpoint of the interval is included,
and the right endpoint is excluded, i.e. [a,b)
2. Draw the horizontal axis.
3. Construct the blocks:
Height of block = percentages!
The total area under an histogram must be 100%
Drawing a histogram