1.
Use of computer in data
analysis
DR SA Balogun ACC
HOU Research

2.
Introduction
• A Computer is an electronic devices which is
capable of accepting data in a prescribed
format, capable of processing the data, storing
the data and result of the processed and help to
release the information in a prescribed format.
• In recent times it has become imperative for
Road Safety practitioners to have the knowledge
of computer.
• This is because of the voluminous data often in
more than two dimensions which neither human
brain nor ordinary statistics can cope with.

3.
SV
GA – Supper V
ideo Graphic A
dapter
CU
M
M RA
M – Read/W
rite M
emory
, loses its data when
C
omputer is switch off
.
RO
M EPO
M – Enable Programmable RO
M
(Ca
nnot be a
ltered)
EA
RO
M
Pe
riphe
ra
ls
A
uxiliary M
emory
Input/O
utput (T
ransport) - T
erminal
Floppy 5 / or 1.2m, 3 / or 1.44, 2H
D Flash
H
ard disk – C driv
e, (1.2G – ov
er 80G
)
C
D RO
M
K
eyboard
V
D
U or M
onitor M
onochrome (B
lack and W
hite)
C
olour D
isplay
VGA – Vide
o Gra
phic Ada
pte
r
CGA – Co
lo
ur Gra
phic Ada
pte
r
EGA – Enha
nce
d Gra
phic Ada
pte
r
LCD – Liquid Crysta
l Displa
y
Printe
r
D
O
T
Inkjet/D
eskJet
LaserJet
Softw
a
re
H
a
rdw
a
re C
PU
Programs
O
perating System
Language
D
O
S
Low Lev
el
H
igh Lev
el
W
idow 98, 2000, M
il X
P
Unix
Linux
FO
R
T
RA
N
, Formula translation
BASIC – Be
ginne
r a
ll purpo
se sym
bo
l Instructio
ns
COBOL – Co
m
m
o
n Busine
ss Orie
nte
d La
ngua
ge
O
thers are PA
SC
A
L, c/Ett, A
D
A
File extension P
rog. (‘EK
E, .C
om, ‘B
A
T
T
ypes Source Prog. (Raw code)
O
bject Prog. (C
ode T
ranslation
EK
E
C
om – C
ommand
B
A
T – for running V
irus
IN
T ano, Sys etc.
A
lgorithm
T
ypes of C
omputer
Programing
System Programing
Applica
tio
n Pro
gra
m
ing (W
o
rd, Exce
l, SPSS e
tc
FIG 3
6
: CO
M
PO
N
EN
T O
F CO
M
PU
TER

4.
INTRODUCTION
• I shall concentrate on the last two
application systems;
• EXEL
• Statistical Package for Social Scientist
(SPSS)

5.
EXEL
• The Electronic spreadsheet is used for many
business applications such as, Accounting,
Financial analysis, payroll processing, statistical
analysis tool, budgeting etc.
• It consists of row and column.
• The intersection of row and column is known as
cell and a group of cell is workbook. Each cell
has address.
• The addition of content of cell C5 and B, is
written in another cell c3 but only the formula is
seen in the cell C3 hence spreadsheet is used
for auditing.

6.
An example of spreadsheet
packages are:
• - Microsoft EXCEL
• - Lotus 1,2,3
• - Quattro pro etc

7.
operation in spreadsheet environment
• financial operation,
• mathematical trigonometry,
• Date and time function,
• Data base management,
• logical (and or not) as well as
• statistical operation (such as Average, Beta
distribution, chi test, confidence level correlation
and Repression.
• To do this, open the computer, click F (x) on
tool, click statistical and financial and see at
least 3 functions.

8.
Starting and closing Exel-
• Click start button, programs then Exel. You can
also do this by double clicking Exel icon
• Other activities- A wide range of activities
similar to those in Word can be performed with
Exel.
• These include entering text, numbers, dates,
percentages, correcting mistakes either by
overwriting or using undo (ctrl z) and redo (ctrl
y), opening of work book from task pane and
other uses of task pane.
• The print dialogue box usually has default
instruction so one has to instruct it as desired.

9.
Auto fill
• It saves time while copying text, number etc in a
spreadsheet.
• To do this, click on the desired cell (i.e Jan) and
ensure that the cursor changes to plus sign then
drag the cursor over all the cell you wish to copy
to.
• With this all other cell reads Feb, March etc.
Formula can also be copied to different cells by
this method.
• A cell in the formula can be made absolute or
constant by pressing F4 after its reference then
ok before auto fill is carried out.

10.
column or row
• Selecting column or row- in a spreadsheet
you will click letters representing the
column or row
• Inserting row or column- Select the row,
click insert then rows.
• Changing column width and row height-
Place the mouse below the row or right to
the column one wish to resize then double
click.

11.
Cut, copy and paste-
• Cut, copy and paste- Press ctrl X or C, click the
desired cell (s), then control V. Microsoft XP has office
clipboard that can allow copying up to 24 items.
• To do this press control C twice to display clipboard,
select item to paste and select paste in clipboard.
• Cells can also be dragged or copied to a new location by
selecting the cells (holding down the control key in case
of copy) positioning cursor at the edge of the selection
so that the cursor can change to a cross sign and drag to
new location.
• Zoom- is found in the view menu

12.
Auto sum
• -It is an easy way to add figures.
• To do this click empty answer cell next to all
additions, click auto sum icon, and then click
enter.
• Alternatively, to do this with a formula,
select the answer cell, type{=sum ( }then click
first cell to reveal its cell reference, type colon
then answer and ok.
• It must be noted that +, -, *, -, (), and ^
stands for addition, subtraction, multiplication,
division, bracket and raise to power respectively.
• Where they occur together, using BODMAS
priority, bracket takes priority before raise to
power, then multiplication, division and plus and
minus respectively.

13.
Sheet manipulation
• Inserting sheet- Right click sheet tab abd select worksheet
from option displayed or select insert from menu and
then worksheet.
• Same for deleting a sheet.
• Renaming sheet- Double click the desired sheet tab then
rename and click any cell.
• Moving sheet- Click sheet tab of desired sheet and drag to
new location or select sheet, click edit, copy and select new location.
• Copying a sheet - Select the sheet, hold down control key, click
and drag the sheet to new location.
• Creating formulae across sheet- This is used to get grand total
of data.
• For instance if a RTA figure by month for each year occupy a sheet
each, the grand total for many years can be obtained on the last
sheet by typing =sum(, clicking on first sheet tab, then on required
cell location on last sheet, holding down shift key, and clicking on
last sheet tab.
• By this, the sheets are grouped together.
• If such sheets are not next to each other, rather than shift a control
key is used in the above exercise.

14.
Sending spreadsheet to Word-
• Follow the select, copy and paste procedure but a smart
tag would appear after pasting.
• You are at liberty to keep the surface formatting or
destination (Word) style or even maintain the link to Exel
in which case any change in the Exel would
automatically reflect in the Word table.
• It is possible to paste in Word with the Exel menu
available for use.
• This is done by selecting paste special from edit and
then selecting Exel worksheet object.
• Alternatively, double click the spreadsheet within the
word and Exel function appears.

15.
Function
• -This can be obtained from drop down arrow of auto sum
icon or insert icon.
• Click on cell of desired answer then the function and ok.
• An 'if' function request Exel to consider something i.e
true or false.
• In this case the condition {= (count if)}is stated followed
by the rule and a coma, then what one wants the
computer to do i.e if (B6>$B$3, 'NO' 'YES').
• Other operators used apart from equal to is <, >, >, >,
>=, and <= respectively.
• It is sometimes necessary to nest several 'ifs' together
hoping that Exel would pick the applicable answer as in
=if (((B2>65,' 'over65', if (B2>4, '45-65', If B2>24, '25-45',
UNDER 25'))).
• In this case if any of the 'if' is correct Exel picks it but if
all are wrong then it pick age under 25.

16.
Exel as database
• is another word for querying
the database to find
information.
• To do this, find auto filter from
filter in the data menu and a
drop down arrows appear on
all the headings.
• Click on the arrow of desired
heading and filter with chosen
criteria.
• For instance, the RTA table
shown below can be filtered to
show locations of fatal
accident and its causes.
• Filter can be customized to
find say day that begin with 'T'
Location Typeof
RTA
cause Sexof
victim
Ageof
victim
Hourof
occurrence
season day date
AbujA-
Lokoj
F DGD M 45 1300 Rain Mon 25th
etc S SPV M etc etc Dry etc etc
S etc F Rain
S F Rain
M M Dry
S M Rain
etc etc Rain

17.
Drawing diagram
• Click drawing tool bar, click insert diagram,
select desired diagram and click ok.
• Enter the words into the diagram and in
case additional shape is desired resize or
change the diagram, click insert shape
layout or change to respectively as found
on the diagram tool bar.

18.
EXEL FOR STATISTICS
• A lecture on different method of data
analysis shall be given by another lecturer
here but as a refresher see different areas
of analysis below;

19.
The Statistic Model

20.
STATISTICS IN EXEL
Confidence
5 0 v e h w i t h
A v s p e e d
3 0 k m p h S D
= 2 . 5
Data D
escription
0.05
2.5
50
Sig
SD
Sample
size
=
C
O
N
F
ID
E
N
C
E
(A
2,A
3,A
4)
0 .6 9 = B tw
2 9 .3 to
3 0 .7 k m p h
Binom ials (i.e F ailure /
Success of outcom e)
I.eProbof next baby
beingm
ale
Data
6
10
0.5
=
B
IN
O
M
D
IS
T
(A
2
,A
3
,A
4
,F
A
L
S
E
)
Probability
X Probability
0
1
2
3
0.2
0.3
0.1
0.4
=P
R
O
B
(A
2:A
5,B
2:B
5,2
Probx=2(0.1)
= P R O B
( A 2 :A 5 ,
B 2 :B 5 ,1 ,3
P r o x b tw
1 & 3 ( 0 .8 )
1
2
3
4
5
6
7
8
10
11
12
13
14
15
9
Frequency
How often
Scores Bin
79 70
85 79
89
78
85
50
81
95
88
97
=F
R
E
Q
U
E
N
C
Y
(A
2:A
10,B
2:B
4)
Description
Nocrash/
successat b/spot
No tips/
indep trial
Prob of
success
O
netail P
rofor
x=4(0.0906)
2 tail =
0.1812
O
netail P
rofor
x=6(0.863)
2 tail =
0.274
Show that sam ple
m ean will be greater
than average observation
D
e
s
c
rip
tio
n
Z Score
=2*MIN(ZTEST
(A2:A11,6),ZTEST
(A2:A11,6))
=2*M
IN
(Z
T
E
S
T
(A
2:A
11,4),Z
T
E
S
T
(A
2:A
11,4))
=ZTEST
(A2:A11,6)
7
9
=ZTEST
(A2:A11,4)
6
8
6
5
4
2
1
2

21.
STATISTICS IN EXEL
Chi test T test
Verify Hypothesis
M
e
n
(O
b
s
e
rv
e
d
)
W
om
en
(O
bserved) D
is
c
r
ip
tio
n
35
25
23
W
omen
(Expected)
Etc
Etc
Etc
T
estthat2sam
pleare
fromsam
epopulation
.Thedatacouldbe
1.Paired(to/fro)
2.equal variance(Hom
oscadacity)
3.Unequal variance(Heteroscadacity)
3
4
5
8
9
1
2
4
6
19
3
2
14
4
5
17
Correlation
5 1
=TTEST
(A2:A10,
B2:B10,2,1)
Pairedtest
=0.196
C o m p a re s tw o
p ro p e rtie s
Veh
speed
Fuel
consumed
3
2
4
5
6
9
7
12
15
17
0.997
=CORREL
(A2:A6,B2:B6)
F Test
T
estsigif2sam
ples
havediffvariance
Test scoreof
untrained
T
est scoreof
trained
7
9
15
21
20
28
31
38
40
6
=FTEST
(A2:A6,B2:B6)
58
11
10
45.35or
(79x93/162)
M
en
(E
xpected)
Etc
Etc
= C H IT E S T
(A 2 :B 4 ,
A 6 :B 8 )
1
2
3
4
5
6
7
8
10
11
12
13
14
15
9
6
Pearson
Ranges from -1 to 1 and
it reflect linear relationship
Indep
(driv exp)
Dep
(nocrash)
9
7
5
3
1
10
6
1
5
3
0.699
=PEARSON
(A2:A6,
B2:B60
Agree
Agree
Neutral
Neutral
D
isagree
D
isgree

22.
STATISTICS IN EXEL
F o re c a s t
P re d ic t fu tu re
fro m e x is tin g
Fuel
consum ed (Y)
V e h
s p e e d ( X )
6
7
9
1 5
2 1
2 0
2 8
3 1
3 8
4 0
=FORECAST
(30,A2:A6,B2:B6)
Predict y
given x=30
G ro w th
P r e d i c t e x p o n e n t i a l
( c u r v e ) g r o w t h u s i n g
e x i s t i n g d a t a
L o c a tio n T ra ffic
C o u n t
11
1 2
1 3
1 4
1 5
1 6
3 3 ,1 0 0
4 7 ,3 0 0
6 9 ,0 0 0
1 0 2 ,0 0 0
1 5 0 ,0 0 0
2 2 0 ,0 0 0
T re n d
U s e l e a s t s q u a r e
m e t h o d t o f i t l i n e a r
v a l u e o f Y f o r g i v e n X
M o n th
R e v e n u e
/ R T A
1
2
3
4
5
6
7
8
9
1 0
11
1 2 145,290
1 4 4 ,0 0 0
1 4 3 ,2 3 0
1 4 1 ,8 9 0
1 4 1 ,1 2 0
1 3 9 ,9 0 0
1 3 9 ,1 0 0
1 3 8 ,1 3 0
1 3 7 ,3 0 0
1 3 5 ,7 9 0
1 3 5 ,0 0 0
1 3 3 ,8 9 0
R e g re s s io n
=TREND
(A2:A13,A15:A19)
= G R O W T H
( B 2 : B 7 , A 2 : A 7 ,
A 9 : A 1 0 )

23.
SPSS
• The three stages in the use of SPSS in
data analysis are;
• 1. Data entry and data encoding
• 2. Data analysis
• 3. Data interpretation

24.
Parametric Test
1.Summary statistics using
frequency
2.Summary statistics using
descriptive
3.Exploratory data analysis
4.Analysis of cross classification
using crosstab
- To study normal-normal
relationship
- Ordinal-ordinal
- To measure relative risk of
events
- To measure agreements
5.The summarise procedure
6.The means procedure
7.The OLAP cubes procedure
8.T Test
-one sample T Test
a)each machine as separate
sample
b)sample mean against known
value
-paired sample Ttest
-independent sample Ttest
a)determining groups in an
independent sample Ttest
b)Testing two independent sample
means
c)using cut point to define

25.
Parametric Test
9.One way ANOVA
-Testing equality of group means
-Performing One way ANOVA
-All possible comparison between
means
-Robust ANOVA
10.GLM Univariate
11.Partial correlation
12.Linear regression
13.Ordinal regression
14.Curve estimation
-model law of diminishing returns
-Model viral growth
15.Discriminat analysis
-assess credit risk
-Classify customer
16.Factor analysis
-data reduction
-structure detection
17.Two step cluster analysis
-to classify
18.Hierachical cluster analysis
-use to classify
-used to study relationships
19.K-means cluster analysis
-to classify customers

26.
Non-Parametric Test
20.Chi-square test
a)Testing independence
b)Testing a specific range
c)Customising expected value
-Binomial test
a)comparing several distribution
b)using cut point to define the
samples
-Run test
a)examining usability of test result
b)testing multiple cut point
-One sample Kolmogorov-Smirnov
test
a)testing goodness of fit
-Two independent sample test
a)Using Man whitney to test
b)using two sample Komogorov-
smirnov test to compare
distribution
-Test for several independent
samples
a)using the median test to detect
group differences
b)using Kruskal-Wallis to test
ordinal outcomes
-Test related sample test
a)testing a sample median against
known value
b)using McNeman test in a pre-
post design
-Test for several related samples
a)i.e testing usability of a website
b)using Friedman test on related

27.
21.Multiple response analysis
22.Ratio statistics
23.ROC curve
-used to evaluate performance
-Used to choose between
competing classification
scheme
24.Measure of reliability in
scale problems
-used to analyze survey items
-used to analyze inter-rated
agreement
25.Control chart
-used to monitor
Used to track proportion of
detective unit
26.Scoring data with predictive
model
27.Select predictors
-used to mine customer
database
28.Naïve Bayes
-used for prediction, selection
and classification
-used for classify respondents

28.
modeling
21.Regression model option
-Binary logistic regression
a)to asses credit risk
-Multinomial logistic
regression
a)used to profile
consumer of packaged
gods
b)used to classify
customer
c)used to analyse a 1-1
matched cases control
study
-Non linear regression
a)used to model law of
diminishing returns
b)used to model viral growth
-Probit analysis
a)used to test promotional
effect on sales
-Weight estimation
a)to model cost of mall
construction
-Two stage least-square
regression
a)used to model cross sales

29.
modeling
22.Advanced model option
-Multivariate General Linear
Modeling
a)GLM Multivariate to profile
difference in amount spent by
two groups
b)GLM repeated measure to
measure effect of each
promotion on sales
Variance component
a)used to analyse product test
result from multiple markets
-Linear Mixed models
a)used to analyse product test
result fro multiple markets
b)used to analyse repeated
measurement of i.e weight and
alcohol level after a meal for 6
months
c)used to model (random
effects and repeated measure

30.
modeling
i. e banning helmet in few states
and using different
enforcement strategies to
know the best)
d)used to fit a random
coefficient model to find
change before and after
treatment-General linear
models
a)GLM to model poison
distribution of RT cases
b)to fit Gama regression of
insurance claims on RTC
c)to analyze internal censored
survival data of event whose
time of occurrence is
unknown i.e RTC
-Generalized Estimation
equation
a)to fit repeated measure
logistic regression i.e
effect of an observed
behavior on the subject
-Loglinear modeling
(relationship between
categorical variables)

31.
modeling
a)to model accident rate i.e
Age & Gender risk factors
b)paired data-response f
subject before and after
treatment
c)Logit Linear analysis to
model 1/more categorical
against 1/more predictors
i.e 800 drivers asked
which of 3 helmet they
like best
-Life tables
a)i.e to find relationship
between time spent before
becoming licenses driver
-Kaplan-Meievr survival
analysis
a)to study distribution of
time to event i.e time take
for drug to affect driving
-Cox regression
a)to model time to specific
event based on given
covariance

32.
modeling
23.Complex sample option
-sampling wizard
a)obtaining sample from full
sampling frame
b)obtaining samples from
partial frame
c)sampling with probability
proportional to size(PPS)
-Analysis wizard
a)used to ready NHIS data
b)used when sampling weights
are not in the data files
c)Tabulation
d)descriptive
-Complex sample
a)frequencies
b)descriptives
c)crosstab
d)ratio
e)GLM
f)logistic regression
g)ordinal regression

33.
24.Trend option-forecast and
modeling
-Bulk forecasting with expert
modeler
a)using expert modeler to
determine significant
predictors-Bulk forecasting
by applying saved models
a)experimenting with
predictors by applying
saved models
-seasonal decomposition
-spectral plots
25.Categories option
-Categorical regression
a)i.e effect of socio-eco on
driving habit
-Categorical Principal
component analysis
a)i.e effect of socio-eco on
driving habit of different
vehicle modes
-Non linear canonical
correlation analysis
a)i.e to find similarities in
the socio-eco factors

34.
-Corresponding analysis
a)analysis from cross
tab
-Multiple correspondence
analysis
a0i.e effect of socio-eco
on driving habit of
different vehicle types in
the mode
-Multidimensional scaling
-Multidimensional unfolding
26.Conjoint option
-used to model carpet
cleaner preference i.e
effect of socio eco on
driving habit
27.Tree option
-to evaluate credit risk
28.Data preparation
option

35.
Scale/numeric
variblewithin
category
Whatkindof
displaydo
youwant
Tables
whatkindof
summary
Chart/graph
kindofchart?
Box-grap,chart
buider,gallary
.boxplot,drop
thevariables
Discriptive-analyse
,report,casesummary
,selectvariable,display
cases

36.
C o m p a r e g r o u p
f o r s i g n d i f f
D a t a i n c a t e g o r i e s
( o r d i n ) - a n a l , d e s c ,
c r o s s t a b , v a r i a b l e
S c a l e / n u m e r
d i v i d e d i n t o
g r o u p s
O n e g r o u p - i . e c o m p a r i n g
v i o l a t i o n o f 1 0 0 k m p h a l o n g
8 d i f f r o a d s . 1 6 v a l u e s a r e
o b t a i n e d . D a t a , s p l i t f i l e ,
s e l e c t r o a d
T w o G r o u p s
/ v a r i a b l e s O n e s c a l e / n u m e r i c
v a r i a b l e d i v i d e d i n t o
t w o u n r e l a t e d g r o u p s .
W h i c h i n d e p s a m p l e
d o y o u w a n t t o t e s t
T e s t t h a t a s s u m e s d a t a a r e n o r m a l l y
d i s t r i b u t e d w i t h i n g r o u p s i . e f i n d i n g
a c c u r a c y o f 2 t y p e s o f t r a f f i c c o u n t e r o n
r x t n t i m e o f v e h i c l e s m o v i n g a t
h y p o t h e t i c a l s p e e d o f 1 0 0 k m p h .
T h e v e h w e r e r a n d o m l y a s s i g n e d
t o t r a i n e d & u n t r g d r i v & m a d e t o
t r a v e l e q u a l d i s t a n c e - a n a l y s e
c o m p m e a n s , i n d e p s a m p l e T t e s t
T e s t t h a t d o e s n o t
a s s u m e s d a t a a r e
n o r m a l l y d i s t r i b u t e d
w i t h i n g r o u p s -
2 i n d e p s a m p l e t e s t
T w o s c a l e / n u m e r i c
w h i c h p a i r e d s a m p l e
d o y o u w a n t ?
Assum es both variable norm ally
distributed-Analyse,com p m eans,
paired sam ple T test select paired
variable
D o n o t a s s u m e s b o t h v a r i a b l e
n o r m a l l y d i s t r i b u t e d - a n a l y ,
n o n p a r a , 2 r e l a t e d s e l e c t
p a i r e d v a r i a b l e
T h r e e / m o r e g r o u p .
H o w m a n y g r o u p i n g
( f a c t o r ) v a r i a b d o y o u
h a v e ?
O n e - i . e r e v e n u e f o r
t h r e e g r o u p s d e f i n e d
b y r e g i o n
T
e
s
t th
a
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ly
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A
N
O
V
A
D a t a n o t n o r m a l l y
d i s t r i b u t e d - a n a l y s e ,
n o n p a r a m e t r ,
k - i n d e p e n d e n t s a m p l e
N o r m a l i t y i s s k e w e d
T w o / m o r e - i . e r e v e n u e
f o r g r o u p s d e f i n e d b y
d i v i s i o n w i t h i n e a c h
r e g i o n - a n a l y s e , G L M ,
u n i v a r i a t e P u t s c a l e a s
d e p e n d e n t v a r i a b l e

37.
I d e n t i f y s i g r e l a
b t w v a r i a b l e . W h a t
k i n d o f d a t a d o y o u
h a v e ?
D a t a i n c a t e g o r i e s
( n o m / o r d i n ) - a n a l y ,
d e s c , c r o s s t a b
O r d i n a / r a n k o r d e r
o r n o n m o r m a l -
A n a l y , c o r r e l a t e ,
b i v a r i a t e , s p e a r m a n
/ k e n d a l
S c a l e / n u m e r i c ( i n t e / r a t i o )
- H o w m a n y v a r i a b l e d o
y o u w a n t t o e v a l u a t e ?
T w o ( o r m u l t i l p e
p a i r o f v a r i a )
T a b l e s & n u m b e r - A n a l ,
c o r r e l a t e , b i v a r i a t e ,
p e a r s o n . I . e c o m p a r e
d r i v i e x p w i t h c a u s e s
o f a c c i d l i k e i n d i s &
s p e e d
C h a r t s & g r a p h H o w
m a n y p a i r s o f v a r i a b l e
d o y o u w a n t t o l o o k a t ?
O n e - G r a p h ,
c h a r t b u i l d e r ,
g a l e r y s c a t t e r
T w o o r m o r e p a i r s
o f v a r i a b l e s - G r a p h ,
c h a r t b u i l d e r , s c a t t e r
T w o c o n t r o l l i n g f o r t h e
e f f e c t s o f o n e o r m o r e
a d d i t i o n a l v a r i a b l e s -
A n a l y , c o r r e l a t , p a r t i a l ,
E x a c t l y t h r e e ( 3 D s c a t t e r p l o t )
- G r a p h , c h a r t b u i l d e r , g a l l e r y
s c a t t e r , 3 D
O n e d e p e n d e n t v a r i a b l e a n d
t w o o r m o r e i n d e p e n d e n t
( p r e d i c t o r ) v a r i b l e - A n a l y ,
r e g r e s s i o n , l i n e a r s e l e c t s c a l e
a s d e p e n d e n t , 2 / m o r e s c a l e
a s i n d e
O r d i n a l d e p a n d s c a l e o r
c a t e g i c a l i n d e p v a r i a b l e
- A n a l y s e , r e g r e s s i o n ,
s e l e c t o r d n a l d e p e n d e
a s c a t e g o r i c a l a n d o t h e r s
a s f a c t o r / c o v a r i a t e
M u l t i v a r i a t e

38.
I d e n t i f y g r o u p s o f
s i m i l a r c a s e s .
W h a t k i n d o f d a t a
d o y o u h a v e ?
S c a l e , n u m e r i c
( i n t e r v a / r a t i o )
I d e n t i f y g r o u p s
L e s s t h a n 2 0 0 - A n a l y s e ,
c l a s s i f y , h i e r r a c h c a l c l u s t e r,
s e l e c t a l l s c a l e v a l u a b l e s t o u s e
2 0 0 o r m o r e c a s e s -
A n a l y , c l a s s i f y , K - m e a n s
c l u s t e r , s e l e c t s c a l e t o
b e u s e d , s p e c i f y n o c l u s t e r e t c
I d e n t i f y c h a r a c t r i s t i c o f k n o w n g r o u p s -
A n a l y, c l a s s i f y, d i s c r i m i n a n t , s e l e c t
c a t e g o r i c a l g r o u p i n g v a r i a b l e r a n g e t o
s p e c i f y t h e c a t e g o i e s o f i n t e r e s t t h e n
s e l e c t s c a l e i n d e p e n d e n t v a r i a b l e
( n o . C r a s h & e x p , a g e , i n c o m e e t c )
C a t e g o r i c a l ( n o m i / o r d i n a ) o r a
m i x o f s c a l e & c a t e g o r i c v a r i a b
t o u s e i n c l u s t e r a n a l y s i s
I d e n t i f y g r o u p s o f s i m i l a r
v a r i a b l e s - A n a l s , d a t a
r e d u c t i o , f a c t o r s e l e c t s c a l e
v a r i a b l e f o r f a c t o r a n a l y s i s

39.
TESTING
• This involves hypothesizing.
• Usually a hypothesis of no significance different (Ho)
is set.
• Ho is rejected if the calculated value is more than the
table value at 95% confidence level or 0.05% level of
significance (except in one parametric test known as
Wilconox test).
• Rejection of Hypothesis means that the result was
due to chance.
• The two levels used in hypothesizing are confidence
level and level of significance.

40.
Testing
These are the level within which our errors are
constrained.
They also compare figure from the sample to the
population.
Although the two levels are interchangeably used,
they are nonetheless different.
While confidence level is represented as 90%,
95%, 99%, the level of significance is represented
as 0.1, 0.5 and 0.01 respectively.
The 99% OR 0.01 means the result was wrong
one in hundred or 99% right respectively.

41.
Conclusion
• Both the EXEL and SPSS are useful in data analysis
• The way data are arranged in SPP is different from that
of EXEL
• While SPSS data do not require any preliminary
summary , the EXEL data sometimes require preliminary
summaries.
• The explanations in this paper does not fully cover all
bout the two application programmes.
• The more each and every one of you continue to
practice analysis with the above software the more you
become versatile in them-PRACTICE MEANS
PERFECT
• Thank you