Business Research Methods Unit V

Business Research Methods
Unit V

Data Analysis: Editing, Coding, Tabular representation of data,
frequency tables, Construction of frequency distributions, Graphical
Representation of Data: Appropriate Usage of Bar charts, Pie charts,
Histogram.
Hypothesis: Qualities of a good Hypothesis –Framing Null Hypothesis
& Alternative Hypothesis. Concept of Hypothesis Testing – Logic &
Importance. Test of Significance: Small sample tests: t-test (Mean,
proportion) and F tests, Z test, Cross tabulations, Chi-square test;
Analysis of Variance: One way and two‐way Classifications.
Mechanism of Report Writing- Report Preparation: Types, Report
Structure: preliminary section, main report, interpretation of results,
suggestions and recommendations, limitations of the study,

I. Analysis of Data
• “in the process of analysis,
– relationships or differences supporting or conflicting
– with original or new hypotheses
– should be subjected to statistical tests of significance
– to determine with what validity data can be said to
indicate any conclusions”
• Analysis serves the purpose of
– Giving proper result.
– Data to be made available for interpretation.
– Establishes relationship between different Variables.

II. Processing Operations
1. Editing
2. Coding
3. Classification
4. Tabulation

i) Editing
• Editing is done to assure that the data are
accurate, consistent with other facts
gathered, uniformly entered, as completed as
possible and have been well arranged to
facilitate coding and tabulation.
a. Field Editing and Central Editing.
1. Editing
2. Coding
3. Classification
4. Tabulation

i) Editing
I. The Interviewer, Editor and coder
remain in constant touch.
II. Objective of Editing remain the
same throughout the editing
process.
III. Apart from numerical value we
may use colour for the same.
IV. They should notify with every
change made in answers.
V. Editor’s initials and the date of
editing should be placed on each
completed form or schedule
1. Editing
2. Coding
3. Classification
4. Tabulation

ii) Coding
• Coding refers to the process of
assigning numerals
• Coding is necessary for efficient
analysis and through it the
several replies may be reduced
to a small number of classes
which contain the critical
information required for
analysis.
• Computer based and manual
1. Editing
2. Coding
3. Classification
4. Tabulation

iii) Classification
• Classification according to attributes
– can either be descriptive (such as
literacy, sex, honesty, etc.) or
numerical (such as weight, height,
income, etc.).
• Classification according to class-
intervals
– Data relating to income, production,
age, weight, etc
– class-intervals
• How many classes should be there?
• How to choose class limits ?
– Exclusive/Inclusive.
• How to determine the frequency of each
class ?
1. Editing
2. Coding
3. Classification
4. Tabulation

iv) Tabulation.
• Tabulation is the process of summarizing raw
data and displaying the same in compact
form (i.e., in the form of statistical tables) for
further analysis.
• Tabulation is an orderly arrangement of data
in columns and rows.
Tabulation is essential because of the following
reasons.
1. It conserves space and reduces explanatory
and descriptive statement to a minimum.
2. It facilitates the process of comparison.
3. It facilitates the summation of items and
the detection of errors and omissions.
4. It provides a basis for various statistical
computations.
1. Editing
2. Coding
3. Classification
4. Tabulation

III. Various Kinds of Charts and Diagrams Used in
Data Analysis
Types of Graph
I. Bar Graph
II. Line Graph
III. Stacked Graph
IV. Pie Graph
V. Pictograph

I. Bar Graph
• It is used to make comparisons about groups of data
Factory A Factory B Factory C Factory D
Production 200 150 145 220
0
50
100
150
200
250
AxisTitle
Production

0
1
2
3
4
5
6
Category 1 Category 2 Category 3 Category 4
Series 1
Series 2
Series 3

2011 2012 2013 2014
Wheat 150 175 200 250
0
50
100
150
200
250
300
AxisTitle
Wheat

I. Bar Graph
0
50
100
150
200
250
2011 2012 2013 2014
Wheat
Rice
Cereals
Wheat Rice Cereals
2011 150 100 50
2012 175 125 75
2013 200 150 100
2014 225 175 125

II. Line Graph
0
50
100
150
200
250
2011 2012 2013 2014
Wheat
Wheat
Wheat
2011 150
2012 175
2013 200
2014 225

II. Line Graph
0
50
100
150
200
250
2011 2012 2013 2014
Wheat
Rice
Cereals
Wheat Rice Cereals
2011 150 100 50
2012 175 125 75
2013 200 150 100
2014 225 175 125

III. Stacked Graph
0 50 100 150 200 250
2011
2012
2013
2014
Cereals
Rice
Wheat
Wheat Rice Cereals
2011 150 100 50
2012 175 125 75
2013 200 150 100
2014 225 175 125

IV. Pie Chart
Sales
1st Qtr
2nd Qtr
3rd Qtr
4th Qtr

IV. Bar and Pie Diagrams and their
Significance
Cate
gory
1
Cate
gory
2
Cate
gory
3
Cate
gory
4
Series 1 4.3 2.5 3.5 4.5
0
0.5
1
1.5
2
2.5
3
3.5
4
4.5
5
AxisTitle
Series 1 • A bar chart is particularly useful
when one or two categories
'dominate‘ results.
– It can be very clear and easy to
read.
– Most people understand what is
presented without having to have
detailed statistical knowledge.
– It can represent data expressed as
actual numbers, percentages and
frequencies.
– A bar chart can represent either
discrete or continuous data.
– If the data is discrete there should
be a gap between the bars (as in
the diagram above).
– If the data is continuous there
should be no gap between the
bars.

Significance
1st Qtr
58%
2nd Qtr
23%
3rd Qtr
10% 4th Qtr
9%
Sales • A pie chart shows data
in terms of proportions
of a whole. The 'pie' is
divided into segments
that represent this
proportion. This is done
by dividing the angles at
the centre.

Significance
1st Qtr
58%
2nd Qtr
23%
3rd Qtr
10% 4th Qtr
9%
Sales • It is best used to present the
proportions of a sample.
• It is most useful where one or
two results dominate the
findings.
• It can represent data
expressed as actual numbers
or percentages.
• Do not use where there are a
large number of categories, or
where each has a small, fairly
equal share, as this can be
unclear.

Calculation
No. of Siblings No. of
Students
Conversion No. of Degrees
0 4 4/30X360 48
1 12
2 8
3 3
4 2
More 1

Parametric and Non Parametric Test
Assumptions Parametric Tests Non Parametric Test
Normality assumption is required Normality assumption is not
required
Uses the metric data Ordinal or Interval scale data is used
Can be applied to both small and
large samples
Can be applied to for small samples.
Applications: One sample using Z or t statistics One sample using the sign test
Two independent samples using a
t or z test
Two independent samples using the
Mann whitney U statistics
Two paired samples using a t test
or z test
Two paired samples usig the sign
test and Wilcoxon matched pair rank
test
Randomness – no test in
parametric is available
Randomness – using runs test.
Several independent samples using
F test in ANOVA
Several independent samples using
Kruskal-Wallis test

Hypothesis Testing
• Hypothesis is an assumption or a statement
that may or may not be true. The hypothesis is
tested on the basis of information obtained
from a sample. Hypothesis tests are widely
used in business and industry for making
decisions.
• Example: Sales Performance – Dependent on
Sales Training.

Hypothesis Testing
• Null Hypothesis: The hypothesis that are
proposed with the intent of receiving a
rejection for them are called null hypotheses.
This requires that we hypothesize the
opposite of what is desired to be proved.
• H0 = Null Hypothesis.
• Example: Sales Training ≠ Sales Performance

Hypothesis Testing
• Alternative Hypothesis: Rejection of null
hypothesis leads to the acceptance of
alternative hypothesis.
• H1 = Alternate hypothesis.
• Example: Sales > > > Sales Training - Direct
Relationship

Hypothesis Testing
• One Tailed and Two Tailed Tests:
– A test is called one tailed (one sided) only if the
null hypothesis gets rejected when value of the
test statistics falls in one specified tail of the
distribution.
– A test is called two tailed (two sided) if null
hypothesis gets rejected when a value of the test
static falls in either one or the other of the two
tails of its sampling distribution.

Hypothesis Testing
• Example: filling of 300ml bottle
• Two tailed hypothesis:
– H1 : 𝜇 = 300ml
– H0 : 𝜇 ≠ 300ml
• One tailed hypothesis: Right hand tail
– H1 : 𝜇 = 300ml
– H0 : 𝜇 > 300ml
• One tailed hypothesis: Left hand tail
– H1 : 𝜇 = 300ml
– H0 : 𝜇 < 300ml

Hypothesis Testing
• Type I and Type II error :
• The acceptance or rejection of a hypothesis is based on sample
results and there is always a possibility of sample not being
representative of the population. This could result in errors as
consequence of which inferences drawn could be wrong. The
situation could be depicted as given:
• The probability of committing a Type I error is denoted by(α) and is
termed as level of significance.
• The probability of committing a Type II error is denoted by(). The
expression 1 -  is called power of test.
Accept H0 Reject H0
H0 True Correct Decision Type I error
H0 False Type II error Correct Decision

Hypothesis testing steps
Making Decision
Computing the value of test statistics
Determination of critical region
Determination of a test statistic
Setting up suitable significance level
Setting up Hypothesis

• Setting up of a hypothesis:
– Hypothesis are generally assumptions about
the value of the population parameter.
– Hypothesis specifies a single value or a range
of values for two different hypothesis rather
than constructing a single hypothesis. These
hypothesis are
• H0 = Null Hypothesis
• H1 = Alternate Hypothesis
– A null hypothesis is set as the hypothesis of no
relationship between those two variables;
whereas the alternative hypothesis is the
hypothesis of the relationship between
variables.
Making Decision
Computing the value of test
statistics
Determination of critical
region
Determination of a test
statistic
Setting up suitable
significance level

• Setting up of suitable significance level:
– The level of significance is denoted by α is chosen
before drawing sample any sample. The level of
significance denotes the probability of rejecting the
null hypothesis when it is true.
– The value of α varies from problem to problem, but
usually it is taken as either 5 percent or 1 percent.
– A 5 percent level of significance means that there
are 5 chances out of hundred that a null hypothesis
will get rejected when it should be accepted. This
means that the researcher is 95 percent confident
that a right decision has been taken.
Making Decision
statistics
region
statistic
Setting up suitable
significance level

• Determination of a test statistic:
– The next step is to determine a suitable test
statistic and its distribution. As would be seen
later, the test statistic could be t, Z, 2 or F,
depending upon various assumptions.
Making Decision
statistics
region
statistic
Setting up suitable
significance level

• Determination of critical region:
– Before a sample is drawn from the population,
it is very important to specify the values of test
statistic that will lead to rejection or acceptance
of the null hypothesis. The one that leads to the
rejection of the null hypothesis is called the
critical region.
– Given a level of significance, α, the optimal
critical region for a two-tailed test consists of
that α/2 percent area in the right hand tail of
the distribution plus that α/2 percent in the left
hand tail of the distribution where that null
hypothesis is rejected.
Making Decision
statistics
region
statistic
Setting up suitable
significance level

• Computing the value of test-
statistic:
– The next step is to compute the value
of the test statistic based upon
random sample size n. Once the value
of test statistic is computed, one needs
to examine whether the sample results
fall in the critical region or in the
acceptance region.
Making Decision
statistics
region
statistic
Setting up suitable
significance level

• Making Decision:
– The hypothesis may be rejected or
accepted depending upon whether the
value of the test statistic falls in the
rejection or the acceptance region.
Management decisions are based upon
the statistical decision of either rejecting
or accepting the null hypothesis.
Making Decision
statistics
region
statistic
Setting up suitable
significance level

Hypothesis testing
• Test statistic for testing
Hypothesis about
Population mean: in case
sample size is large
(n>30),Z statistic would
be used. For a small
sample size(n≤30) a
further question
regarding the knowledge
of population standard
deviation() is asked.
Sample size
Knowledge of population
standard deviation ()
Known Not Known
Large (n>30) Z Z
Small (n≤30) Z t

Hypothesis testing - Case of Large Sample: Z – Test
• 𝑋 = Sample mean
•  = Population standard deviation
• H0 = The value of  under the assumption
that the null hypothesis is true.
• n = Size of sample.

Hypothesis testing
• Criteria for accepting or rejecting null
hypothesis under different cases of alternative
hypothesis

Problem 1 – page number 369 - Chawla and Sondhi
• A sample of 200 bulbs made by a company
give a lifetime mean of 1540 hours with a
standard deviation of 42 hours. Is it likely that
the sample has been drawn from a population
with a mean lifetime of 1500 hours? You may
use 5 percent level of significance.

Hypothesis Testing – Case of Small Sample
T-test
• In case the sample size is small(n<=30)and is drawn
from a population having a normal population with
unknown standard deviation , a t-test is used to
conduct the hypothesis for the test of mean.
• The t-distribution is a symmetrical distribution just
like the normal one.
• The t-distribution is flatter than normal distribution.
• With increase in the sample size(and hence degree of
freedom), t-distribution loses its flatness and
approaches the normal distribution whenever n>30.
• A comparative shape of t and normal distribution is

Question 4. page number373 – Chawla and Sondhi
• A sample of 16 graduating engineering
students of a college was taken and the
information was obtained on their starting
salary. The mean monthly starting salary was
found to be Rs. 30,200 with a standard
deviation of Rs. 960. The past data on the
starting salary has given a mean value of
Rs.30,000. Using a 5 per cent level of
significance, can we conclude that the average
starting salary is different from Rs.30,000

Chi-Square Test - 2
• For the use of chi-square test, the data is
required in the form of frequencies. The data
expressed in percentages or proportion can
also be used, provided it could be converted
into frequencies. The majority of the
applications of chi-square are with the
discrete data. The test could also be applied to
continuous data, provided it is reduced to
certain categories and tabulated in such a way
that the chi-square may be applied.

• The value of chi-square
are greater than or
equal to zero.
• The shape of a chi-
square distribution
depends upon the
degrees of freedom.
With the increase in
degree of freedom, the
distribution tends to
normal.

• Application of Chi-square
• A chi-square test for the goodness of fit
• A chi-square test for the independence of
variables
• A chi-square test for the equality of more that
two population proportions.

Chi square test for the Goodness of fit
• As discussed before, the data in chi-square tests
is often in terms of counts or frequencies. The
actual survey data may be on a nominal or higher
scale of measurement. If it is on a higher scale of
measurement, it can always be converted into
categories. The real world situtations in business
allow for the collection of count data i.e. gender,
marital status, job classification, age and income.
Therfore, a chi square becomes a much sought
after tool for analysis.

Chi square test for the Goodness of fit
Steps for chi square test
• State the null and the alternate
hypothesis about population
• Specify a level of significance
• Compare the expected frequencies of the
occurrence of certain events under the
assumption that the null hypothesis is
true.
• Make a note of the observed counts of
the data points falling in different cells
• Compute the chi-square value given by
the formula
• Compare the sample value of the statistic
as obtained in previous step with the
critical value at a given level of
significance and make the decision.
Oi = Observed frequency of ith cell
Ei = Expected frequency of ith cell
k = Total number of cells.
k – 1 = degree of freedom

Problem 14.1 Page number 457 of Chawla and Sondhi
• The manager of ABC icecream parlour has to take a decision
regarding how much of each flavour of icecream he should stock so
that the demands of the customers are satisfied. The icecream
supplier claims that among the four most popular flavours, 62
percent customers prefer vanilla,18 percent chocolate, 12 percent
strawberry and 8 percent mango. A random sample of 200
customers produces the results below. At the  = 0.05 significance
level, test the claim that the percentages given by the supplies are
correct.
Flavour Vanilla Chocolate Strawberry Mango
Number
Preferring
120 40 18 22

Meaning and Importance of Report Writing
• It is a study of facts & information.
• It is formed in a structured format Based on
observation and analysis.
• It helps the management in an organization
for making plans & solving issues in the
organization.
• It is an outcome of an any event.

Features of good Report Writing
• It has a clear thoughts.
• It is complete & self-explanatory.
• It is comprehensive but compact.
• It is accurate in all aspects.
• It has suitable format for readers.
• It has a clear thoughts
• It is complete & self-explanatory
• It is comprehensive but compact
• It is accurate in all aspects
• It has suitable format for readers

I. Report Preparation
• Research report is considered a major
component of the research study for the
research task remains incomplete till the
report has been presented and/or written.
• A research report can be based on practical
work, research by reading or a study of an
organization or industrial/workplace
situation.

1. Value for the Investment.
2. It should be shared so that
knowledge base can be
increased for the overall
development.
3. Human knowledge belongs to
all humanity, it must be
shared and made available
publicly.
4. Accuracy should be checked
4. It should be published so
that the actual author may
get actual credit.
5. We must publish as
researchers’ professional
value can only be evaluated
in terms of their output
6. Knowledge is the only
commodity in the world that
actually increases in value
and extent only when
shared. The researcher’s
duty is to increase
knowledge.

Research reports are the product of slow,
painstaking, accurate inductive work. The usual
steps involved in writing report are:
• (a) Logical analysis of the subject-matter;
• (b) Preparation of the final outline;
• (c) Preparation of the rough draft;
• (d) Rewriting and polishing;
• (e) Preparation of the final bibliography; and
• (f) Writing the final draft.

II. Report Layout
(A) Preliminary Pages
I. title and date
II. acknowledgements in the form of ‘Preface’ or ‘Foreword’
III. tables and illustrations
(B) Main Text
I. Introduction;
II. Statement of findings and recommendations;
III. The results;
IV. The implications drawn from the results; and
V. The summary.
(C) End Matter
I. Appendices(all technical data such as questionnaires, sample information,
mathematical derivations and the like ones.
II. Bibliography
III. Index

II Types of Reports
• Results of a research investigation can be
presented in a number of ways viz.,
a) a technical report,
b) a popular report,
c) an article,
d) a monograph or at times even in the form of oral
presentation(writing on a single subject)

II Types of Reports
a) technical report
• A technical report is used whenever a full
written report of the study is required
whether for record keeping or for public
dissemination.
• Covered under this
– the methods employed,
– assumptions made in the course of the study,
– the detailed presentation of the findings including
their limitations and supporting data

II Types of Reports
a) Technical report
IX) Index
VIII) Technical appendices
VII) Bibliography
VI) Conclusions
V) Analysis of data and presentation of findings
IV) Data
III) Methods employed
II) Nature of the study
I) Summary of results

II Types of Reports
b) Popular Report
• A popular report is used if the research results have policy
implications.
• The popular report is one which gives emphasis on
simplicity and attractiveness.
• The simplification should be sought through clear writing,
minimization of technical, particularly mathematical,
details and liberal use of charts and diagrams.
• Attractive layout along with large print, many subheadings,
even an occasional cartoon now and then is another
characteristic feature of the popular report.
• Besides, in such a report emphasis is given on practical
aspects and policy implications.

II Types of Reports
b) Popular Report
Technical appendices
Results
Methods employed
Objective of the study
Recommendations for action
The findings and their implications
A general outline of a popular report.

II. Types of Reports.
c) Oral Presentation
• Oral presentation of the results of the study is
considered effective, particularly in cases
where policy recommendations are indicated
by project results. The merit of this approach
lies in the fact that it provides an opportunity
for give-and-take decisions which generally
lead to a better understanding of the findings
and their implications.

Types of research articles:
1. A journal article
2. A conference paper
3. An article in a trade or scholarly periodical
4. A thesis
5. A research report
6. A research monograph

Journal papers:
• Each discipline has a number of “journals” where the findings in the
discipline are published. These come in two general categories:
1. Peer reviewed
a) Archival journals
b) Non-archival journals
2. Non- peer-reviewed
A peer-reviewed journal is one in which the articles submitted for
publication are sent to a panel of expert “peers” in the discipline
to read and evaluate the suitability of publication of the material
presented. Only peer-reviewed publications are of significance as
scientific research publication. We shall not discuss non-reviewed
publications.

Conferences and conference papers:
• Almost every discipline also has a number of
conferences associated with work in it.
Conferences may be:
1. Commercially or trade oriented.
2. Professionally/scholarly oriented, or
3. Research oriented.

Articles in trade or scholarly
periodicals:
• This means that they are a venue for the publication of material
that might be of value to the profession or the research community
which may not be publishable in an archival or research journal.
1. Material that is not original research but is an application of some
current or established research which highlights a new issue or opens
a question etc.
2. Material that proposes a research project of interest or a
collaboration for a project.
3. A case study or an experience report
4. A revalidation of previous research
5. Scholarly (non-research) work of an eminent authority
6. Advice, methodologies, and advocations of eminent authorities.

Theses:
• In recent years the meaning of the word has
somewhat changed and the role a thesis
played previously is now played by another
artifact called a research monogram.
• A thesis today is a major written work
reporting on an identifiable piece of research
conducted by a candidate for a higher degree.

Format of theses
Chapter 10: Conclusion and future work
Chapter 9: Findings and impact of work
Chapter 8: Analysis
Chapter 7: Results
Chapter 6: Conduct
Chapter 5: Methodology and research design
Chapter 4: The Hypothesis
Chapter 3: Literature review
Chapter 2: Scene setting/background
Chapter 1: Introduction

Research Report:
• A research report is also a report written usually
without a page number limitation or many other
restrictions for the purpose of documenting and
publishing research work where details of the work
that are not otherwise available in the journal paper
are required.
• A research report is usually shorter than a thesis but
longer than a typical archival paper (approximately 20-
100 pages) and contains the details of the
methodology, information on equipment, data
collected in raw or semi-processed form, and any other
piece of information needed to adequately understand
or to reproduce the work.

A Research Monogram:
• A research monogram is a book written by
the researcher and published by a publisher
for the purpose of general sale. It is usually
free from most stylistic and logistics
limitations otherwise imposed on publications
destined for journals or conferences.

Evaluating a research article:
A research article is usually evaluated by a panel
of experts before it is accepted for publication.
This process is called “The Peer Review Process”.
1. Technical robustness
2. Clarity
3. Style/language/presentation
4. Interest and relevance
5. Appropriateness for venue

Evaluating a research article:
• These ratings are usually:
1. Accept without any modifications
2. Accept with minor stylistic modifications to the
satisfaction of the editor
3. Accept with minor structural or technical
modifications as advised
4. Accept after modifications sought have been
reviewed
5. Reject out-right

III. Precautions For Writing Research Reports
• Research report is a channel of
communicating the research findings to the
readers.
• It should be long enough to cover the subject
but short enough to maintain interest.
• A research report should not be dull.
• Abstract terminology and technical jargon
should be avoided in a research report

• The layout of the report should match with the objective
of the research.
• Strictly in accordance with the techniques of composition
of report-writing.
• The report must present the logical analysis of the subject
matter.
• A research report should show originality.
• State the policy implications relating to the problem under
consideration.
• Appendices should be enlisted in respect of all the
technical data in the report.
• Bibliography of sources consulted is a must for a good
report and must necessarily be given.

• Index is also considered an essential part of a good
report and as such must be prepared and appended at
the end.
• Report must be attractive in appearance, neat and
clean, whether typed or printed.
• Have to mention Various constraints experienced in
conducting the research study may also be stated in
the report.
• Objective of the study, the nature of the problem, the
methods employed and the analysis techniques
adopted must all be clearly stated in the beginning of
the report in the form of introduction.

IV .Bibliography and Annexure in the Report : Their
Significance
• Bibliography:
• For books and pamphlets the order may be as under:
1. Name of author, last name first.
2. Title, underlined to indicate italics.
3. Place, publisher, and date of publication.
4. Number of volumes.
Example
Kothari, C.R., Quantitative Techniques, New Delhi, Vikas Publishing House Pvt. Ltd., 1978.
• For magazines and newspapers the order may be as under:
1. Name of the author, last name first.
2. Title of article, in quotation marks.
3. Name of periodical, underlined to indicate italics.
4. The volume or volume and number.
5. The date of the issue.
6. The pagination.
Example
Robert V. Roosa, “Coping with Short-term International Money Flows”, The Banker, London,
September, 1971, p. 995.

Significance
• Why Is A Bibliography So Important?
• It shows the lecturer/tutor/examiner what you have been
reading and also where you have been reading around the
subject.
• It demonstrates that the points you are making in your
work are supported by other people. Your arguments are
stronger if you can back up what you say with evidence.
• It enables other people reading your work to find the things
you have referred to quickly and easily.
• It recognizes the intellectual input someone else has made
to your work - passing off someone else's work as your own
is called plagiarism.

Significance
• Annexure is a term substituted in some legal
documents for Appendix.
• It gives the details about the content other
than literary work.
• It also include questionnaire.
• An appendix contains supplemental information such as a
glossary, historical time line, or list of resources. An index is an
alphabetized list of names (of people, places, recipes, etc.)
that are in the book and the page numbers where they can be
found.

V. Drawing Conclusions, Suggestions and Recommendations to
the Concerned Persons.
The most significant part of the report:
• Conclusion drawn for the data analysis.
• It relates to the finding drawn from every question.
• We try to emphasize on the gap for the research in our
conclusion.
• On the basis of Conclusion pertaining to every question
we try to generalize things.
• Then out of these conclusions we try to give
Suggestion or Recommendations to the Client
• Primary objective of Recommendation remains to give
problem solution to the problem in hand.

Assignment – IV
Opening Data –
Closing Date –
• What are the significance of Report Writing?
• What are the steps involved in writing a Report?
• What is the general Layout for writing a Report?
Give a detailed note on it.
• What are the different types of report?
• What are the precautions one should take while
writing a report ?

Business Research Methods Unit V

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Business Research Methods Unit V

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