BY
DR. Y. VINODHINI-PROFESSOR
ANWARUL ULOOM COLLEGE OF BUSINESS
MANAGEMENT,
HYDERABAD
DATA ANALYSIS AND INTERPRETATION

IMRaD
 The Introduction explains the scope and objective
of the study in the light of current knowledge on the
subject
 The Materials and Methods describes how the study
was conducted
 The Results section reports what was found in the
study and
 The Discussion section explains meaning and
significance of the results and provides suggestions
for future directions of research.

The purpose
 Data Analysis & Interpretation is to answer the research
questions and to help determine the trends and relationships
among the variables
 Data analysis is defined as a process of cleaning,
transforming, and modeling data to discover useful
information for business decision making.
 The purpose of Data Analysis is to extract useful
information from data and taking the decision based upon
the data analysis.
 Process of analyzing, evaluating, cleansing and
transforming data by utilizing different analytical tools for
finding out some useful information to make helpful
conclusions and empower decision-making processes is
understood as data analysis.

Types of data analysis
Descriptive data analysis
Descriptive Analysis is the type of analysis of data that helps describe,
show or summarize data points in a constructive way such that patterns
might emerge that fulfill every condition of the data.
It is one of the most important steps for conducting statistical data
analysis.
It gives you a conclusion of the distribution of your data, helps you
detect typos and outliers, and enables you to identify similarities
among variables, thus making you ready for conducting further
statistical analyses.
Examples: Rates (incidence, prevalence); close-ended questions
• Discrete Data: data in whole numbers (e.g., 47 years old)
• Continuous Data: measures on a continuous scale (e.g., 47.32 years old)
• Categorical: Descriptive data (no ordering, categories, preferences)
• Ordinal: data that has some kind of ordering like age or income level Q

Data Analysis: Types
Descriptive analytics looks at data statistically to tell you
what happened in the past. Descriptive analytics helps a
business understand how it is performing by providing
context to help stakeholders interpret information. This can
be in the form of data viualizations like graphs,
charts,reports, and dashboards.
 In a healthcare setting, for instance, say that an unusually
high number of people are admitted to the emergency room
in a short period of time. Descriptive analytics tells you that
this is happening and provides real-time data with all the
corresponding statistics (date of occurrence, volume,
patient details, etc.).

Data Analysis: Types contd…
 What is Diagnostic Analytics?
 Diagnostic analytics takes descriptive data a step further
and provides deeper analysis to answer the question: Why
did this happen? Often, diagnostic analysis is referred to as
root cause analysis.
 In the healthcare example mentioned earlier, diagnostic
analytics would explore the data and make correlations. For
instance, it may help you determine that all of the patients’
symptoms—high fever, dry cough, and fatigue—point to
the same infectious agent. You now have an explanation for
the sudden spike in volume at the ER.

Data Analysis: Types contd…
What is Predictive Analysis
 is the attempt to predict what could happen. It considers
key trends and patterns. The model is then applied to
current data to predict what will happen next.
 The most crucial purpose of data analysis is predicting
future events. The graph, pie charts, or any form of data
visualization is widely used in predictive analysis.
 Hospital example, predictive analytics may forecast a
surge in patients admitted to the emergency services in
the next several weeks. Based on patterns in the data, the
illness is spreading at a rapid rate

contd…Data Analysis: Types
 Prescriptive analysis takes predictive data to the next
level. Now that you have an idea of what will likely
happen in the future, what should you do? It suggests
various courses of action and outlines what the
potential implications would be for each.
 Back to our hospital example: now that you know the
illness is spreading, the prescriptive analytics tool may
suggest that you increase the number of staff on hand
to adequately treat the influx of patients.

statistical analysis
Conducting statistical analysis is one of the most useful techniques
for data analysis. It works by focusing upon different aspects
such as Cluster, Regression, Cohort, etc.
 In Cluster, the grouping of a set of elements is done in a way that
similar elements are grouped by forming a cluster.
Example: Customer segmentation: Clustering is widely used
in developing marketing strategies, for example, in targeting
different categories of customers for different kinds of
promotions.
A cohort is a group of users who share a common characteristic
over a certain period of time. Cohort analysis is the study of the
common characteristics of these users over a specific period.
In Cohort, you will have a subset of behavioural analytics that is
used for taking insights from the given set of data set. In this,
each of the elements would be broken into related groups, and
hence you will be having a wealth of info about consumers’
preferences.

contd..statistical analysis
 The third aspect is regression, and it revolves around a
fixed set of statistical processes that work upon making
the relationships amongst specific variables for gauging
a deeper understanding of contemporary trends.
 Regression analysis is a powerful statistical method that
allows you to examine the relationship between two or
more variables of interest.
Example: Regression analysis provides detailed insight
that can be applied to further improve products and
services.

Inferential analysis
 Inferential statistics to make judgments of the
probability that an observed difference between groups
is a dependable one or one that might have happened
by chance .
 Most of the major inferential statistics come from a
general family of statistical models includes the t-test,
Analysis of Variance (ANOVA), Analysis of
Covariance (ANCOVA), regression analysis, and many
of the multivariate methods like factor analysis,
multidimensional scaling, cluster analysis,
discriminant function analysis, and so on.

Inferential analysis
 It is about using data from sample and then making
inferences about the larger population from which the
sample is drawn. The goal of the inferential statistics is
to draw conclusions from a sample and generalize
them to the population. It determines the probability of
the characteristics of the sample using probability
theory. The most common methodologies used are
hypothesis tests, Analysis of variance etc.

Differences between descriptive and
inferential statistics

Data interpretation
 Data interpretation is drawing conclusions and inferences
from a comprehensive data presented numerically in tabular
form by means of an illustration, viz. Graphs, Pie Chart e
According to C. William Emory, “Interpretation has two
major aspects namely establishing continuity in the research
through linking the results of a given study with those of
another and the establishment of some relationship with the
collected data. Interpretation can be defined as the device
through which the factors, which seem to explain what has
been observed by the researcher in the course of the study,
can be better understood. Interpretation provides a
theoretical conception which can serve as a guide for the
further research work”.
 Data interpretation is an examination of those findings, and
through that examination, users need to make an informed
decision on what to do next.

Data Interpretation
 Data interpretation is the process of reviewing data through
some predefined processes which will help assign some
meaning to the data and arrive at a relevant conclusion. It
involves taking the result of data analysis.
 Data analysis is the process of ordering, categorizing,
manipulating, and summarizing data to obtain answers to
research questions. It is usually the first step taken towards
data interpretation. It is evident that the interpretation of
data is very important, and as such needs to be done
properly.

Data Interpretation Methods
 Data interpretation methods are how analysts help people
make sense of numerical data that has been collected,
analyzed and presented. Data, when collected in raw form,
may be difficult for the layman to understand, which is
why analysts need to break down the information gathered
so that others can make sense of it. For example, when
founders are pitching to potential investors, they must
interpret data (e.g. market size, growth rate, etc.) for better
understanding. There are 2 main methods in which this
can be done, namely; quantitative methods and qualitative
methods.

Qualitative Data Interpretation
Method
 The qualitative data interpretation method is used to
analyze qualitative data, which is also known as
 categorical data: This method uses texts, rather than
numbers or patterns to describe data.
 Qualitative data is usually gathered using a wide variety of
person-to person techniques, which may be difficult to
analyze compared to the quantitative research method.

..contd
Qualitative data needs to first be coded into numbers before it
can be analyzed. This is because texts are usually cumbersome,
and will take more time and result in a lot of errors if analyzed
in its original state.
Coding done by the analyst should also be documented so that
it can be reused by others and also analyzed. There are 2 main
types of qualitative data, namely; nominal and ordinal data.
These 2 data types are both interpreted using the same method,
but ordinal data interpretation is quite easier than that of
nominal data. In most cases, ordinal data is usually labelled
with numbers during the process of data collection, and coding
may not be required. This is different from nominal data that
still needs to be coded for proper interpretation.

CONTD..QUALITATIVE DATA
 Qualitative data is based on narrative
information, not numerically
‘measurable’ information (e.g., “What
does age 47 feel like?”)
•Perceptions •Quotations
•Experience/Observations
•Open ended questions

Quantitative analysis
 Numeric data collected in a research project can be analyzed quantitatively
using statistical tools in two different ways.
 Descriptive analysis refers to statistically describing, aggregating, and
presenting the constructs of interest or associations between these constructs.
 Inferential analysis refers to the statistical testing of hypotheses (theory
testing).
 Before initiating with analysis preparation has to be done such as
 Data coding.
 Data entry.
 Missing values.
 Data transformation
 statistical techniques for descriptive analysis
 statistical techniques for inferential analysis.
 Much of today’s quantitative data analysis is conducted using software
programs such as SPSS or SAS.

Some of the statistical methods used in
analyzing quantitative data are
 Mean The mean is a numerical average for a set of data and is
calculated by dividing the sum of the values by the number of values
in a dataset. It is used to get an estimate of a large population from
the dataset obtained from a sample of the population.
 Standard deviation This technique is used to measure how well
the responses align with or deviates from the mean. It describes the
degree of consistency within the responses; together with the mean, it
provides insight into data sets.
 Frequency distribution This technique is used to assess the
demography of the respondents or the number of times a particular
response appears in research. It is extremely keen on determining the
degree of intersection between data points. Advantages of Data
Interpretation
 It helps to make informed decisions and not just through guessing
or predictions.
 It is cost-efficient
 The insights obtained can be used to set and identify trends in data.

Quantitative Data Interpretation Method
 The quantitative data interpretation method is used to
analyze quantitative data, which is also known as numerical
data. This data type contains numbers and is therefore
analyzed with the use of numbers and not texts.
 Quantitative data are of 2 main types, namely; discrete and
continuous data.
 Continuous data is further divided into interval data and
ratio data, with all the data types being numeric. Due to its
natural existence as a number, analysts do not need to
employ the coding technique on quantitative data before it
is analyzed. The process of analyzing quantitative data
involves statistical modeling techniques such as standard
deviation, mean and median

Benefits of data interpretation
 Informed decision making - In order to take action and
implement new methods in either healthcare, retail, or other
industry,
 Anticipating needs and identifying trends - Data analysis
provides relevant insights which users can leverage to
predict trends based on customers' concerns and
expectations.
 Cost efficiency - It should come as no surprise that if a
company relies on data-driven decision-making that it will
also save money. It's true that analysis itself results in extra
costs
 Clear foresight - Lastly, those companies that aggregate
and analyze data gain better insight into their own
performance and how they are viewed by the consumers

Steps in data interpretation
 The three main steps in data interpretation are:
 Examining the findings
 Draw conclusions
 Come up with solutions
 So we need to examine the analyzed data, and based
on that, draw conclusions on the specific topic, which
is then followed by an actionable strategy that needs to
solve the issue or help us achieve the desired goal.

THANK YOU