1. BUSINESS STATISTICS BQT 173

2. CHAPTER 1 : DATA & STATISTICS

3. DATA & STATISTICS Statistics ??? Meaning : Numerical facts
Field or discipline of study
Collection of methods for planning experiments, obtaining data and organizing, analyzing, interpreting and drawing the conclusions or making a decision.

4. Application in Business
Production and Operation
Auditing
Financial
Application in Business
Economy

5. Examples present some statistics in business:
Bill Gates is the richest American with a net worth $43 billion (Forbes, September,30,2002).
A total of 35billion transactions were handled by the Visa system during 2001 (Forbes, September 16,2002).
On average, a household carried a credit card balance in $8562 in 2001 (Newsweek, April 1, 2002).
On average, a wedding in America costs $20,357 (Smart Money, June 2002).

6. BASIC TERMS IN STATISTICS
Population
Entire collection of individuals which are characteristic being studied.
Sample
- Subset of population.
Population
Sample

7. Census
Survey includes every member of population.
Sample survey
Collecting information from a portion of population (techniques)
Element
- Specific subject or object about which information collected.
Variable
Characteristics which make different values.

8. - Value of variable for an element. Data Set
Observation
- Value of variable for an element.
Data Set
- A collection of observation on one or more variables.
Table 1: Student’s Score for Business Statistic
Name
Score
Mohd Amirul bin Hamdi 90
Hashimah 78
Variable
Element
Observation/
Measurement

9. TYPES OF VARIABLES Variable Quantitative Qualitative
Continuous (e.g., length, age, height, weight, time)
Discrete
(e.g, number of houses, cars accidents
e.g., gender, marital status

10. QUANTITATIVE AND QUALITATIVE variable
1) Quantitative variable
A variable that can be measured numerically.
Data collected on a quantitative variable are called quantitative data.
There are two types of quantitative variables:-
i. Discrete Variable
A variable whose values are countable, can assume only
certain values with no intermediate values.
ii. Continuous Variable
A variable that can assume any numerical value over a
certain interval or intervals.
2) Qualitative variable
A variable that cannot assume a numerical value but can be classified into two or more nonnumeric categories.
Data collected on such a variable are called qualitative data.

11. MEASUREMENT SCALES There are four measurement scales :- i. Nominal
ii. Ordinal
iii. Interval
iv. Ratio
1) Nominal
only for qualitative classification.
the weakest data measurement where numbers are used to represent an item / characteristic.
each data should not be treated as numerical since relative size has no meaning.
no order or ranking can be imposed on the data.
(e.g : gender – male =1 , female = 2)

12. 2) Ordinal
it is possible to rank order all the categories according to some criterion.
classifies data into categories that can be ranked ( no precise difference )
(e.g : grades – A,B,C,D and collegiate class – freshman, sophomore, junior, senior)
3) Interval
have the property that the distances between categories are defined by fixed and equal units.
is ranks of data
quantity and compare the size of difference between two observations (precise difference do exist)
(example :For age, a change from age 21 to 22 is the same for changes age 31 to 42)

13. 4) Ratio
The highest level of measurement and allows for all basic arithmetic operations including division and multiplication.
Has the property that a zero point is naturally defined.
The mode, mean, median can be used to describe interval and ratio data.
Poses all the characteristics of interval measurement.
True zero exist.
(E.g : Production of 20 units per hour (ratio level) is twice the production of 10 units per hour)

14. Measurement Levels and the Appropriate Averages
ALL DATA
Qualitative data
Quantitative data
Interval and Ratio
Sales ($)
Accounts Receivable
Market share
Nominal
Car makes,
Days of Week,
Gender
Ordinal
TV channel
Ranks and title
Calendar dates
Mean
Mode
Median

15. 1.2 Data
Data is the collection of the observations or measurements on a variable.
A data with lot of observations usually looks non informative that is we cannot get much information with the raw data.
Raw data is also called as ungrouped data.
Data refers to quantitative or qualitative attributes of a variable or set of variables.
- Example :- the whole numbers that represents the scores of students.
Data is categorized by two :-
- quantitative data
- qualitative data
Data should be summarized in more informative way such as graphical, diagrams and charts.

16. CROSS SECTION VERSUS TIME-sERIES DATA
1) Cross Section Data
Data collected on different elements at the same point in time or for same period of time.
An example of cross-section data which is giving of six companies for the same period (2007) :-
Company
2007 Charitable Giving (millions of dollars)
Home Depot 42
Macy`s
35.2
Wal-Mart
337.9
Best Buy
31.8
Target
168.9
Lowe`s
27.5
Table 1.2 : Charitable Givings of Six Retailers in 2007

17. Total Indoor Movie Screens Table 1.3 : Number of Movie Screens
2) Time-Series Data
Data collected on the same element for the same variable at different points in time or for different periods of time.
Example, a Movieplex with 8 screens would count as 8 toward the total number of screens.
Year
Total Indoor Movie Screens
2003
35,361
2004
36,012
2005
37,092
2006
37,776
2007
38,159
2008
38,198
Table 1.3 : Number of Movie Screens

18. SOURCES OF DATA Primary Data Secondary Data
1.3 Data Sources
SOURCES OF DATA
Primary Data
(data collected
by the researcher)
Examples:-
Personal Interview
Telephone Interview
Questionnaire
Observations
Secondary Data
(already collected/
published by someone else)
Examples:
From books, magazines,
annual report, internet

19. Using tables, graphs & summary measures Using sample result in
STATISTICS
DESCRIPTIVE STATISTICS
INFERENTIAL STATISTICS
Using tables, graphs & summary
measures
Using sample result in
making decision or
predict about a population.
Also called inductive reasoning
or inductive statistics.

20. 1.4 Descriptive Statistics
Consists of methods for organizing, displaying and describing data by using tables, graphs and summary measures.
In general divided by two categories :-
- Data presentation (display)
- Statistics

21. 1.5 Inferential Statistics
Consists of methods that use sample results to help make decisions or predictions about a population.
Area statistics which are deal with decision making procedures.
Example :-
- In order to find the salary of a college graduate, we may select 2000 recent college graduates, find the starting salaries and make decision based on the information.

22. 1.6 Statistical Analysis using Excel Example 1.1 :-
Following table shows data for income tax returns for 1995 to that were filed electronically. Get the sum of income tax for all years and get average of the income tax for those 7 years.
i. Data is key in using Excel.
Figure 1

23. ii. To get sum of income tax for all years, type =SUM(. iii
ii. To get sum of income tax for all years, type =SUM(. iii. Select the range of cells (C4:C10) of numerical data, and close the bracket.
Figure 2

24. iv. Press Enter, the sum should appear.
Figure 3

25. v. To get, the average, the sum should divide by the number of years.
vi. Type =AVERAGE(.
vii. Select the range of cells for all years (C4:C10) and close bracket.
viii. Press Enter. The average of income tax for those years should appear.
Figure 4