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MB0040-
STATISTICS FOR MANAGEMENT
Q1 Distinguish between
Classification and Tabulation. Explain the structure and components of a Table
with an example.
·
Meaning
of Classification and Tabulation
·
Differences
between Classification and Tabulation
·
Structure
and Components of a Table with an example
Answer.
Meaning of
Classification and Tabulation
Classification
According to Secrist,
“Classification is the process of arranging data into sequences and groups according
to their common characteristics or separating them into different but related
parts”. According to Stockton and Clark, “The process of grouping large number
of individual facts and observations, on the basis of similarity among the
items is called Classification”.
Q2
a) Describe the
characteristics of Normal probability distribution.
b) In a sample of 120
workers in a factory, the mean and standard deviation of wages were Rs. 11.35
and Rs.3.03 respectively. Find the percentage of workers getting wages between
Rs.9 and Rs.17 in the whole factory assuming that the wages are normally distributed.
·
Characteristics
of Normal probability distribution
·
Formula/Computation/Solution
to the problem
Answer.
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a)
Characteristics
of Normal probability distribution
The following are some of the
characteristics of Normal distribution:
1. Normal distribution is a
Continuous probability distribution
2.
Its probability density function is given by:
Q3 a) The procedure of
testing hypothesis requires a researcher to adopt several steps. Describe in
brief all such steps.
b) Distinguish between:
i. Stratified random
sampling and Systematic sampling
ii. Judgment sampling
and Convenience sampling
·
Hypothesis
testing procedure
·
Differences
Answer.
Steps for procedure of
testing hypothesis
Step 1: Specify the Null
Hypothesis
The null hypothesis (H0)
is a statement of no effect, relationship, or difference between two or more
groups or factors. In research studies, a researcher is usually
interested in disproving the null hypothesis.
Step 2: Specify the
Alternative Hypothesis
Q4 a) What is regression
analysis? How does it differ from correlation analysis?
b)
Calculate Karl Pearson’s coefficient of correlation between X series and Y
series.
|
x
|
110
|
120
|
130
|
120
|
140
|
135
|
155
|
160
|
165
|
155
|
|
y
|
12
|
18
|
20
|
15
|
25
|
30
|
35
|
20
|
25
|
10
|
Meaning of Regression
and Correlation
Differences
Formula/ Computation/
Solution to the problem
Answer.
Meaning of Regression
and Correlation
Regression analysis
According to M. M.
Blair, Regression is defined as, “the measure of the average relationship
between two or more variables in terms of the original units of the data”. Regression
analysis – in statistics, this includes any technique for learning about the
relationship between one or more dependent variables Y and one or more
independent variables X.
Q5 Briefly explain the
methods and theories of Business forecasting.
Meaning of Business
forecasting
Methods of Business
forecasting
Theories of Business
forecasting
Answer.
Meaning of Business
forecasting
Business forecasting provides a guide to
long-term strategic planning and helps to inform decisions about scheduling of
production, personnel and distribution. These are common statistical tasks in
business that are often done poorly and frequently confused with planning and setting
of goals. Forecasting of USB-ED introduces participants to forecasting
techniques and provides a practical understanding of the main forecasting tools
used by economists, and business, marketing and financial analysts.
Q6
Construct Fisher’s Ideal Index for the given information and check whether
Fisher’s formula satisfies Time Reversal and Factor Reversal Tests.
|
Items
|
P0
|
Q0
|
P1
|
Q1
|
|
A
|
16
|
5
|
20
|
6
|
|
B
|
12
|
10
|
18
|
12
|
|
C
|
14
|
8
|
16
|
10
|
|
D
|
20
|
6
|
22
|
10
|
|
E
|
80
|
3
|
90
|
5
|
|
F
|
40
|
2
|
50
|
5
|
Formula of Fishers
Ideal Index
Computation of Fisher’s
Ideal Index
Fisher’s formula
satisfies Time Reversal Test
Fisher’s formula
satisfies Factor Reversal Test
Answer.
Formula of Fishers
Ideal Index
This method is a
combination of Laspeyre’s and Paasche’s method. If we find out the geometric
average of Laspeyre’s
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