Course Topics Simple statistical methods for data analysis using Excel. descriptive statistics, an introduction to statistical inf

 
 

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 Point.
• Publishing documents on the web

 
 

• 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. Moore. 

 

Optional Texts

 
 

Useful links 

• Surfstat: an online text in introductory Statistics: http://www.anu.edu.au/nceph/surfstat/surfstat-home/surfstat.html
• Statistics at Square One: http://bmj.com/collections/statsbk/index.shtml
• The DePaul University library offers a number of good books on Excel using books 24X7: IT Pro 
  

 
 

Getting ready for the class 

• Open Excel
• Check that the Tools menu contains the Data Analysis option
• If not, use Tools|Add Ins… and click on box labeled Analysis ToolPak

 

 
 

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 

1. Displaying the data in a graph that shows overall patterns and unusual observations (stem-and-leaf plot, bar chart, histogram, box plot, density curve).

1. Computing descriptive statistics that summarize specific aspects of the data (center and spread). 
   

 
 

Exploratory Data Analysis

 
 

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, 30-40)

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

variables. 

Observed variables

 
 

Graphs for categorical variables 

The values of a categorical variable are labels.

The distribution of a categorical variable lists the count or percentage of individuals in each category.  
 

A sample of 400 wireless internet users.  

Counts:   212               168                  20

 
 

112 (28%) 

Female 

400 (100%) 

Total 

Wireless internet users 

288 (72%) 

Male  

Another Example

 
 

Dead 

Survived 

13 

154 

93 

25 

Second class 

106 

422 

90 

88 

Third class 

3 

670 

20 

192 

Crew members  

4 

118 

141 

62  

First class 

Female 

Male  

Female 

Male 

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. 

Assigning Categories

 
 

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 
 

The Histogram

 
 

1. Construct a distribution table:
1. Define class intervals or bins (Choose intervals of equal width!)
2. Count the percentage of observations in each interval
3. 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