This document provides an overview of quantitative techniques (QT) in management. It covers the following key points in 3 sentences: The syllabus covers 5 modules - introduction, probability and distributions, sampling and distributions, hypothesis testing, and forecasting techniques. The agenda discusses why managers need statistics, the growth of statistics, key definitions of populations/samples/parameters/statistics, descriptive versus inferential statistics, reasons for data collection, data types and sources, survey design, sampling methods, and survey errors. The document provides examples and explanations of descriptive statistics, inferential statistics, sampling methods like simple random and stratified sampling, sources of survey errors, and evaluating the worthiness of survey data.

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QT in
Managemen
t
Lecture 1
Introduction
and Data
Collection
AGBS,
Bangalore

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Syllabus
 Module I: Introduction
 Module II: Probability and Probability Distributions
 Module III: Sampling and Sampling Distribution
 Module IV: Tests of Hypothesis
 Module V: Forecasting Techniques

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Agenda
 Why a manager needs to know about statistics
 The growth and development of modern statistics
 Key definitions
 Descriptive versus inferential statistics
 Why data are needed
 Types of data and their sources
 Design of survey research
 Types of sampling methods
 Types of survey errors

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Why a Manager Needs to Know about Statistics?
 To know how to properly present information
 To know how to draw conclusions about populations based on
sample information
 To know how to improve processes
 To know how to obtain reliable forecasts

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Interpret this!

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Exit polls – right or wrong?

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The Growth and Development of Modern Statistics
Needs of government to
collect data on its citizens
The development of the
mathematics of probability
theory
The advent of the computer

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Key Definitions
• A population (universe) is the collection of
things under consideration
• A sample is a portion of the population
selected for analysis
• A parameter is a summary measure
computed to describe a characteristic of
the population
• A statistic is a summary measure
computed to describe a characteristic of
the sample

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Population and Sample
Population Sample
Use parameters to
summarize features
Use statistics to
summarize features
Inference on the population from the sample

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Statistical Methods

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Descriptive Statistics
• Collect data
– e.g. Survey
• Present data
– e.g. Tables and graphs
• Characterize data
– e.g. Sample mean =
iX
n
∑

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

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Why We Need Data?
• To provide input to survey
• To provide input to study
• To measure performance of service or production process
• To evaluate conformance to standards
• To assist in formulating alternative courses of action
• To satisfy curiosity

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Data Sources
Primary
Data Collection
Secondary
Data Compilation
Observation
Experimentation
Survey
Print or Electronic

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Types of Data
C a t e g o r i c a l
( Q u a l it a t iv e )
D i s c r e t e C o n t i n u o u s
N u m e r i c a l
( Q u a n t i t a t i v e )
D a t a

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Reasons for Drawing a Sample
• Less time consuming than a census
• Less costly to administer than a census
• Less cumbersome and more practical to administer than a
census of the targeted population

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Types of Sampling Methods
Quota
Sample
s
Judgement Chunk
Simple
Random
Systematic
Stratified
Cluster
Non-Probability
Samples
Probability
Samples

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Probability Sampling
Subjects of the sample are chosen based on known
probabilities
Probability
Samples
Simple
Random Systematic Stratified Cluster

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Simple Random Samples
• Every individual or item from the frame has an equal
chance of being selected
• Selection may be with replacement or without replacement
• Samples obtained from table of random numbers or
computer random number generators

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Systematic Samples
Decide on sample size: n
Divide frame of N individuals into groups of k
individuals: k=N/n
Randomly select one individual from the 1st
group
Select every k-th individual thereafter
N = 64
n = 8
k= 8
First Group

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Stratified Samples
• Population divided into two or more groups according to
some common characteristic
• Simple random sample selected from each group
• The two or more samples are combined into one

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Stratified Samples – Example
Suppose that in a company there are the following types of
staff:
 male, full-time: 90
 male, part-time: 18
 female, full-time: 9
 female, part-time: 63
Take a sample of 40 staff, stratified according to the above
categories. In the sample –
 male, full-time:
 male, part-time:
 female, full-time:
 female, part-time:

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Cluster Samples
• Population divided into several “clusters,” each
representative of the population
• Simple random sample selected from each
• The samples are combined into one
Population
divided
into 4
clusters.

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Advantages and Disadvantages
• Simple random sample and systematic sample
– Simple to use
– May not be a good representation of the population’s
underlying characteristics
• Stratified sample
– Ensures representation of individuals across the entire
population
• Cluster sample
– More cost effective
– Less efficient (need larger sample to acquire the same
level of precision)

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Evaluating Survey Worthiness
• What is the purpose of the survey?
• Is the survey based on a probability sample?
• Coverage error – appropriate frame
• Nonresponse error – follow up
• Measurement error – good questions elicit good responses
• Sampling error – always exists

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Types of Survey Errors
Coverage error
Non response error
Sampling error
Measurement error
Excluded from
frame.
Follow up on non
responses.
Chance differences
from sample to sample.
Bad Question!
 Coverage error
 Non response
error
 Sampling error
 Measurement
error

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Summary
• Addressed why a manager needs to know about statistics
• Discussed the growth and development of modern statistics
• Addressed the notion of descriptive versus inferential
statistics
• Discussed the importance of data
• Defined and described the different types of data and
sources
• Discussed types of sampling methods
• Described different types of survey errors

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Quiz
1. The average monthly salary of 12 workers and 3 managers in a factory was Rs. 600.
When one of the managers whose salary was Rs. 720, was replaced with a new
manager, the average salary of the team went down to Rs. 580. What is the salary of
the new manager?
2. The average temperature on Wednesday, Thursday and Friday was 250. The average
temperature on Thursday, Friday and Saturday was 240. If the temperature on
Saturday was 270, what was the temperature on Wednesday?
3. The average age of a group of 12 students is 20 years. If 4 more students join the
group, the average age increases by 1 year. The average age of the new students is
……..
4. The average age of a family of 5 members is 20 years. If the age of the youngest
member is 10 years, what was the average age of the family at the time of the birth
of the youngest member?
5. A student finds the average of 10 positive integers. Each integer contains two digits.
By mistake, the student interchanges the digits of one number say ba for ab. Due to
this, the average becomes 1.8 less than the previous one. What is the difference
between the two digits a and b?

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Thank You