BUSINESS STATISTICS BQT 173

CHAPTER 1 : DATA & STATISTICS

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.

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

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).

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

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.

- 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

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

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.

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)

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)

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)

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

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.

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

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

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

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.

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

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.

1.6 Statistical Analysis using Excel Example 1.1 :- Following table shows data for income tax returns for 1995 to 2001 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

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

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

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