2. • Univariate
• Bivariate
• Multivariate

3.  Descriptive Statistics
 Inferential Statistics

4.  Descriptive analysis refers to
transformation of raw data into a form that
will facilitate easy understanding and
interpretation. The ways of summarizing
data are by calculating average, range,
standard deviation, frequency etc.

5.  Inferential statistics is concerned with making
predictions or inferences about a population from
observations and analyses of a sample. That is,
we can take the results of an analysis using a
sample and can generalize it to the larger
population that the sample represents. Examples
of inferential statistics include t-test, regression
analysis, correlation analysis, ANOVA.

6.  the t-test compares the actual difference between
two means in relation to the variation in the data.
 Usually have two groups
 Pre-test and post–Test analysis
 dependent and independent variable
 Multiple test apply

7.  Correlation measures the degree of
association between two or more
variables. There are three types of
correlation:-
 Positive Correlation
 Negative Correlation
 Zero Correlation

8.  ASSOCIATED WITH CORRELATION
 HELP IN PREDICTING VALUES OF SINGLE OR
SET INDEPENDENT VARIABLE OR NUMERIC
DEPENDENT VARIABLE TO GET CERTAIN
OUTCOME

9.  Used to two and more than two groups
 Compare mean scores
 Comparison between different groups
Exp: suppose there is three groups- the differences
between 1 &2, 2 & 3, 3 &1

10.  One- way, two way, three way ANOVA
 Analysis of impact of one or more independent
variable (exp: BP is higher of male)
 Two types of factorial ANOVA
BETWEEN GROUPS
DIFFERENT GROUPS
REPEATED NUMBERS OF AVOVA

11. IDENTIFY AND DEFINE YOUR VARIABLE
Cause and effect relationship
Dependent (blood pressure and muscles pain) and
independent variables (comparison between male / female)
(comparision between public sector bank and private sector
banks) (comparision between football players and basket
ball players)
OPERATIONAL DEFINITION OF EACH VARIABLE (exp:
health measure 1.independent variables may be HAPPY
OR UNHAPPY)

12.  IDENTIFY THE NATURE OF VARIABLE
i. Level of measurement of each variable
ii. Develop measurement scale
• Normal / categorical
• Ordinal- ranking on five point scale
• Interval (exp: temperature, air, water temp)
• Ratios (no. of books you read out in library,
number of article you read, liquidity ratio, profit
earning ratio, assets turnover ratio)

13.  DRAW A DIAGRAM
• Summarize key points in a diagram
• Identify type of questions, Variables (exp; is there
a relationship between blood pressure and body
weight) scatter diagram
(exp: do peoples BMI values below 25 have lower
BP than people having BMI above 25)
BMI
GREATER 25
BMI LESS
THAN 25
MEAN BP

14. Exp: is the effect of sex on BP different for people
with BMI values below 25 than people with BMI
above 25.
Sex – independent category – ale / female
BMI – independent category
BP- dependent – mean range from 100- 220
S.NO PARTICULARS BMI LESS
25
BMI
GREATER
25
1 MEAN BP MALE
2 MEAN BP FEMALE

15.  DETERMINE NEED FOR PARAMATRIC AND
NON-PARAMETRIC TEST
• DOES YOUR DATA MEET THE ASSUMPTION
OF PARAMATRIC TESTING (exp: t’ test, ANOVA)
• What if it does’t?
 use parametric testing any way
 Possible in larger sample size
 Violate some assumption/ justify
 Data transform

16.  Make a final decision
• Make determination about your variables
• Make sure you meet all the assumptions
• Are there other approaches that could be taken
• What approaches have used by other studies with
similar design
• Exp: RQ- what is the relationship between gender
and having a diagnosis of clinical dieses

17.  One independent variable i.e; male/ female
 One category dependent variable i.e: diagnosis of
depression yes/ no
Test of independence – chi- square
patients male female
Have depression
Does not have
depression

18.  Exp: RQ: is there a relationship between age and
depression index? Does depression needed with
age.
Pearson's correlation
 exp: male more depressed than female
• Dependent variable- male
• Independent variable- female
Parametric- Independent t’ test

19.  Exp: will 10 week of exercise have reduced the
BP
 Independent variables: pre- test and post- test
 Dependent variable: BP
 Parametric test- t’ test
 Non- parametric test- rank test

20. Exp 6: what is the effect of age on BP score for
male/ female
 Two continuous independent variable
(gender: male/ female, age ≤ 30, 31-49, 50 &
above)
 Dependent variable: BP
 Parametric test: two ways ANOVA
 Non parametric test: none

21. Exp: do male have over all rating of psychological
health (depression, anxiety, perceived stress) than
female
 Independent variable (male/ female)
 Dependent variable (psychological health)
 Parametric test- MANOVA
 Non-parametrictest- none
male female
Mean anxiety score
Mean depression
Mean preceived stress

22. THANK YOU