1. Introduction to Data.pdf

Text Book
Applied Statistics in
Business and Economics
By Doane et al.
Introduction and Data Collection

Dr. Himani Gupta, IIFT
Learning Objectives
 How statistics is used in business
 The sources of data used in business
 The types of data used in business
 The basics of Microsoft Excel and SPSS

Statistics in Business
 Accounting — auditing and cost estimation
 Economics — regional, national, and international economic
indicators and performance
 Finance — investments, portfolio and risk management
 International Business — market and demographic analysis
 Management — human resources Analytics, compensation, and
quality management, optimum strategy
 Management Information Systems — performance of systems
which gather, summarize, and disseminate information to
various managerial levels
 Marketing — market analysis and consumer research

Why Study Statistics?
Decision Makers Use Statistics To:
 Present and describe business data and information
properly
 Draw conclusions about large populations, using
information collected from samples
 Make reliable forecasts about a business activity
 Improve business processes

Types of Statistics
 Statistics
 The branch of mathematics that transforms data
into useful information for decision makers.
Descriptive Statistics
Collecting, summarizing, and
describing data
Inferential Statistics
Drawing conclusions and/or
making decisions concerning a
population based only on sample
data

Descriptive Statistics
 Collect data
 ex. Survey
 Present data
 ex. Tables and graphs
 Characterize data
 ex. Sample mean = i
X
n


Inferential Statistics
 Estimation
 ex. Estimate the population
mean weight using the
sample mean weight
 Hypothesis testing
 ex. Test the claim that the
population mean weight is
120 pounds
Drawing conclusions and/or making decisions
concerning a population based on sample results.

Basic Vocabulary of Statistics
VARIABLE
A variable is a characteristic of an item or individual.
DATA
Data are the different values associated with a variable.
OPERATIONAL DEFINITIONS
Variable values are meaningless unless their variables have
operational definitions, universally accepted meanings that are
clear to all associated with an analysis.

Basic Vocabulary of Statistics
POPULATION
A population consists of all the items or individuals about which
you want to draw a conclusion.
SAMPLE
A sample is the portion of a population selected for analysis.
PARAMETER
A parameter is a numerical measure that describes a characteristic
of a population.
STATISTIC
A statistic is a numerical measure that describes a characteristic of
a sample.

Population vs. Sample
Population Sample
Measures used to describe the
population are called parameters
Measures computed from
sample data are called statistics

Why Collect Data?
 A marketing research analyst needs to assess the
effectiveness of a new television advertisement.
 A pharmaceutical manufacturer needs to determine
whether a new drug is more effective than those currently
in use.
 An operations manager wants to monitor a manufacturing
process to find out whether the quality of product being
manufactured is conforming to company standards.
 An auditor wants to review the financial transactions of a
company in order to determine whether the company is in
compliance with generally accepted accounting
principles.

Sources of Data
 Primary Sources: The data collector is the
one using the data for analysis
 Data from a survey, Questionnaire
 Data collected from an experiment
 Observed data

 Secondary Sources: The person performing data
analysis is not the data collector
 Analyzing census data
 Examining data from print journals or data
published on the internet.

Types of Variables
 Categorical (qualitative) variables have
values that can only be placed into
categories, such as “yes” and “no.”
 Numerical (quantitative) variables have
values that represent quantities.

Types of Variables
Data
Categorical Numerical
Discrete Continuous
Examples:
 Marital Status
 Political Party
 Eye Color
(Defined categories)
Examples:
 Number of Children
 Defects per hour
(Counted items)
Examples:
 Weight
 Voltage
(Measured characteristics)

Measurement Scales
 Measurement : Assignment of numbers or
symbols to Characteristics of
objects
Example: Consumers’ Perception
Attitude
Preferences
It is like a mapping;

Characteristics
Numbers
1,2,3…..

Scaling
 Scaling is extension of ‘ Measurement ’
 Scaling is creating a range or continuum on which
measured objectives are located.
 Example: Measurement of attitude :
favorable or unfavorable
 Scaling creates four levels:
 Very favorable, favorable, Unfavorable, Very unfavorable.
 Measurement assigns the number: 5,4,3,2,1

Measurement Scales
 Nominal
 Ordinal
 Interval
 Ratio

Measurement Scales: examples
 Nominal:
 Categories, Identification, roll nos.
 Ordinal:
 Rankings., Preferences, Most popular brand(s)
 Interval :
 Temperature, Grading in exams, User categories
 Ratio:
 Length, Marks in exams, Distance, Salaries

Scale
Nominal Numbers
Assigned
to Runners
Ordinal Rank Order
of Winners
Interval Performance
Rating on a
0 to 10 Scale
Ratio Time to
Finish, in
Seconds
Primary Scales of Measurement
7 3
8
Third
place
Second
place
First
place
Finish
Finish
8.2 9.1 9.6
15.2 14.1 13.4

Levels of Measurement
 A nominal scale classifies data into distinct
categories in which no ranking is implied.
Categorical Variables Categories
Personal Computer
Ownership
Type of Stocks Owned
Internet Provider
Yes / No
Microsoft Network / AOL
Growth Value Other

 An ordinal scale classifies data into distinct
categories in which ranking is implied
Categorical Variable Ordered Categories
Student class designation Freshman, Sophomore, Junior,
Senior
Product satisfaction Satisfied, Neutral, Unsatisfied
Faculty rank Professor, Associate Professor,
Assistant Professor, Instructor
Standard & Poor’s bond ratings AAA, AA, A, BBB, BB, B, CCC, CC,
C, DDD, DD, D
Student Grades A, B, C, D, F

 An interval scale is an ordered scale in which the
difference between measurements is a meaningful
quantity but the measurements do not have a true
zero point.
 A ratio scale is an ordered scale in which the
difference between the measurements is a
meaningful quantity and the measurements have a
true zero point.

Interval and Ratio Scales

Data Level, Operations,
and Statistical Methods
Data Level
Nominal
Ordinal
Interval
Ratio
Meaningful Operations
Classifying and Counting
All of the above plus Ranking
All of the above plus Addition,
Subtraction, Multiplication, and
Division
All of the above
Statistical
Methods
Nonparametric
Nonparametric
Parametric
Parametric

Scale Basic
Characteristics
Common
Examples
Marketing
Examples
Permissible Statistics
Descriptive Inferential
Nominal Numbers identify
& classify objects
Social
Security nos.,
numbering of
football
players
Brand nos.,
store types
Percentages,
mode
Chi-square,
binomial test
Ordinal Nos. indicate the
relative positions
of objects but not
the magnitude of
differences
between them
Quality
rankings,
rankings of
teams in a
tournament
Preference
rankings,
market
position,
social class
Percentile,
median
Rank-order
correlation,
Friedman
ANOVA
Interval Differences
between objects
can be compared,
zero point is
arbitrary
Temperature
(Fahrenheit,
Celsius)
Attitudes,
opinions,
index nos.
Range, mean,
standard
deviation
Product-
moment
correlation,
t tests,
regression
Ratio Zero point is
fixed, ratios of
scale values can
be compared
Length,
weight
Age, sales,
income,
costs
Geometric
mean,
harmonic
mean
Coefficient
of variation
Primary Scales of Measurement

1. Introduction to Data.pdf

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