1. Chapter 1
Basic Concepts of Scale of Measurement,
Central Tendencies and Dispersion
Prof. Suresh
suresh.suralkar@gmail.com
Phone: 40434399, 25783850, 9969982986

2. Chapter 1.1
Basic Concepts of Scale of Measurement
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Evaluation Pattern
• Class Participation & Attendance : 10%
• Group Case Study Presentation & Report : 15%
• Tests (written) : 15%
• Solve & Submit one Q. paper of any prev. year : 10%
• Final Exam : 50%

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Course Content - Syllabus
Contents
1. Basic Concepts of scale of measurement,
Central tendencies and dispersion
2. Probability and Probability Distributions
3. Sampling and sampling distributions
4. Estimation
5. Testing of Hypotheses
6. Chi-square
7. Analysis of Variance (One Way Anova)
8. Bivariate Analysis
9. Time series analysis
10. Decision Trees
11. Linear Programming, Transportation and Assignment Problems
Cases
Exercises using SPSS / Excel

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Quantitative Methods
• Quantitative Methods
• Other names to this subject:
• Statistical Methods for Management
• Statistics for Managers
• Business Statistics
• Statistics is a very ancient science, advanced to modern
level

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Quantitative Methods
• Definition: Statistics is a mathematical science
pertaining to the collection, analysis, interpretation or
explanation and presentation of data.
• Data Types: Quantitative & Qualitative

7. Population vs. Sample
a b c d
ef gh i jk l m n
o p q rs t u v w
x y z
Population Sample
b c
g i n
o r u
y
Measures used to describe
a population are called
parameters
Measures computed from
sample data are called
statistics
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8. Key Definitions
• A population (universe) is the collection of all
members of a group
• A sample is a portion of the population selected for
analysis
• A parameter is a numerical measure that describes a
characteristic of a population
• A statistic is a numerical measure that describes a
characteristic of a sample
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9. Two Branches of Statistics
• Descriptive statistics
– Collecting, summarizing, and presenting data
• Inferential statistics
– Drawing conclusions about a population based only
on sample data
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10. 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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11. Inferential Statistics
Drawing conclusions about a population
based on sample results.
• 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
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12. Collecting Data
Secondary Sources
Data Compilation
Observation
Experimentation
Print or Electronic
Survey
Primary Sources
Data Collection
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13. Types of Data
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)
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Scales of Measurement
• Scales of Measurement are of four types
Nominal scale
Ordinal scale
Interval scale
Ratio scale
• Weakest to strongest scale

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Nominal Scale
In Nominal Scale, numbers are used simply as labels for
groups, classes or categories
e.g. Blue, Green and Red numbered as 1, 2 and 3 resp.
Male, Female
Nominal stands for name of category
Nominal scale is used for Qualitative Data

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Ordinal Scale
In Ordinal Scale, data elements are ordered according to
their relative size or quality
e.g. Four products ranked as 1, 2, 3 and 4: worst to best

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Interval Scale
In the interval scale, the value of zero is assigned
arbitrarily and therefore we can not take ratio of two
measurements. But we can take ratio of intervals
e.g. 8am and 4am. We can not take ratio of two.
But we can take ratio of duration: 8am – 4am and
8am – 6am as 4/2.
Similarly Temp 20° C, 30° C and 50° C

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Ratio Scale
Used when the measurements are in Ratio Scale
e.g. 1, 2, 3, 4, 5…
Rs. 100 is twice of Rs. 50
Rs. 0 is absence of any money
Measurement of duration (but not time of day)