DATA ANALYSIS
Making Sense of Data
ZAIDA RAHAYU YET
The type(s) of data collected
in a study determine the type
of statistical analysis used.
Types of Data
Qualitative Data
Quantitative Data
Nominal
Discrete
Ordinal
Continuous
Interval
Ratio
Terms Describing Data
Quantitative Data:
• Deals with numbers.
• Data which can measured.
(can be subdivided into interval and ratio data)
Example:- length, height, weight, volume
Qualitative Data (Categorical data ):
• Deals with descriptions.
• Data can be observed but not measured.
(can be subdivided into nominal and ordinal data)
Example:- Gender, Eye color, textures
Discrete Data


A quantitative data is
discrete if its possible
values form a set of
separate numbers:
0,1,2,3,….
Examples:
1. Number of pets in
a household
2. Number of children
in a family
3. Number of foreign
languages spoken
by an individual
Discrete data -- Gaps between possible values
0
1
2
3
4
5
6
7
Continuous Data



A quantitative data is
continuous if its possible
values form an interval
Measurements
Examples:
1.
2.
Continuous data -- Theoretically,
no gaps between possible values
0
1000
3.
Height/Weight
Age
Blood pressure
Qualitative (Categorical) data
Nominal data :
• A type of categorical data in which objects fall into
unordered categories.
• To classify characteristics of people, objects or events
into categories.
• Example: Gender (Male / Female).
Ordinal data (Ranking scale) :
• Characteristics can be put into ordered categories.
• Example: Socio-economic status (Low/ Medium/ High).

Depends on type of data:
◦ For categorical you will typically use either a
bar or pie graph
◦ For quantitative you can use dotplot,
stemplot, histogram, boxplot.
Parametric Assumptions
1. Independent samples
2. Data normally distributed
3. Equal variances
Normality test (MINITAB)
Equal variances test(MINITAB)
Regression analysis (MINITAB)
Correlation analysis (MINITAB)
Example One-way ANOVA
One-way ANOVA(MINITAB)
ANOVA (MINITAB output)
2 samples t-test (MINITAB)
2 Samples Dependent
(MINITAB)
OPTIMIZATION FLOWCHART

In the article “Sealing Strength
of Wax-Polyethylene Blends” by
Brown, Turner, & Smith, the
effects of three process
variables (A) seal temperature,
(B) cooling bar temperature, &
(C) % polyethylene additive on
the seal strength y of a bread
wrapper stock were studied
using a central composite
design.
Factor
A. Seal Temp
B. Cooling Bar Temp
C. Polyethylene Content
Range
225 - 285
46 - 64
0.5 – 1.7
RSM Design(MINITAB)
RSM Analysis(MINITAB)
Response Surface Regression: Response versus temp, cooling,
polyethylene
The analysis was done using uncoded units.
Estimated Regression Coefficients for Response
Term
Constant
temp
cooling
polyethylene
temp*temp
cooling*cooling
polyethylene*polyethylene
temp*cooling
temp*polyethylene
cooling*polyethylene
Coef
-28.7877
0.1663
0.6120
5.4495
-0.0003
-0.0045
-1.1259
-0.0005
-0.0098
0.0098
S = 1.089
R-Sq(adj) = 72.6%
R-Sq = 85.6%
SE Coef
11.3798
0.0646
0.1914
2.4698
0.0001
0.0013
0.2813
0.0005
0.0076
0.0252
T
-2.530
2.573
3.198
2.206
-2.647
-3.633
-4.003
-0.909
-1.298
0.389
P
0.030
0.028
0.010
0.052
0.024
0.005
0.003
0.385
0.223
0.705
Analysis of Variance for Response
Source
Regression
Linear
Square
Interaction
Residual Error
Lack-of-Fit
Pure Error
Total
DF
9
3
3
3
10
5
5
19
Seq SS
70.305
30.960
36.184
3.160
11.865
6.905
4.960
82.170
Adj SS
70.305
18.654
36.184
3.160
11.865
6.905
4.960
Adj MS
7.8116
6.2181
12.0615
1.0533
1.1865
1.3811
0.9920
F
6.58
5.24
10.17
0.89
P
0.003
0.020
0.002
0.480
1.39
0.363
Thank you