correlation and scatter diagram

8/14/2019 Correlation and Scatter Diagram

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Firstly, I would like to thank whole-heartedly to ALLAH, THE ALMIGHTY.

Who gave me courage, knowledge and confidence to carry out & complete this

project. I am also thankful to our respected Sir, Mr. Khushnoor, who gave the

useful information and guidance to complete this project. I would also like to

thank my PARENTS who are there to motivate me and build my confidence

which helps me in walk of my life.

Scenario2


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CONTENTS

A Brief Introduction to SPSS..........................................................................................................5

VARIABLE VIEW....................................................................................................................11

Variable Name.......................................................................................................................12

Variable Types......................................................................................................................13

Variable Width and Decimal places.............................................................................................13

Variable Labels......................................................................................................................14

Value Labels.........................................................................................................................15

Data Display.........................................................................................................................17

Measurement Scale of Variables.................................................................................................18

DATA VIEW...........................................................................................................................20

Computing Variables..................................................................................................................22

Modifying Variables...................................................................................................................24

Again Recoding values into a new variable.......................................................................................28

Creating charts - drawing a scatter plot............................................................................................31

SCATTER MATRIX..................................................................................................................34

RESULTS and INTERPRETATIONS..........................................................................................37

CORRELATIONS.....................................................................................................................39RESULTS and INTERPRETATIONS..........................................................................................41

Mean and Standard Deviation........................................................................................................44


Measurements According to Gender................................................................................................46

SCATTER MATRIX for Males.....................................................................................................48


SCATTER MATRIX for Females..................................................................................................50


CORRELATION According to Gender............................................................................................52

RESULTS and INTERPRETATIONS for Males.............................................................................54

RESULTS and INTERPRETATIONS for Females..........................................................................57

MEAN AND Standard Deviation According to Gender........................................................................59

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RESULTS and INTERPRETATIONS for Females..........................................................................60

A Brief Introduction to SPSS

SPSS provides facilities for analyzing and displaying information using a variety of techniques.

This document uses version 15 of SPSS for Windows.

It looks a lot like Microsoft excel as they are both spread sheets. However there are at least two features

of SPSS which distinguish from excel which makes it particularly useful for employment in social

sciences.

Prerequisites

Basic familiarity with Windows and at least an elementary knowledge of simple statistics (statistical theory is notexplained).

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IMPACT OF VARIOUS EDUCATIONAL FACTORS ON CGPA of Students

Questionnaire

ABSTRAT

We are conducting a survey on how do various educational factors effect the CGPA

of students.

1

DATA VIEW VARIABLE VIEW

Standard Bar

Menu

Restore Down/ Maximize Window

Minimize

Close


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All questions contained in the questionnaire are highly confidential and will

be used only for research purposes.

Gender:Male Female

1. What is your current CGPA?

2. What percentage you obtained in intermediate?

3. What was medium of Instruction in intermediate?

4. Was the institute Public or Private?

5. What grades did you obtain in Quantitative Subjects of first three semesters

of BBA (HONS): Principles of Account 1, Principles of Accounting 2, and Cost

Accounting?

6. What grades did you obtain in Verbal Subjects of first three semesters of

BBA (HONS): Functional English 1, Functional English 2, and Oral

Communication?

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This dataset has 38 observations and 11 variables. The data is in fixed column format;each measurement forms a column and the values in each column make up a variable.

MOIM represents Medium of InstructionA1 represents Principles of Account 1A2 represents Principles of Accounting 2A3 represents Cost AccountingE1 represents Functional English 1E2 represents Functional English 2E3 represents Oral Communication

NOTEthat blanks indicate missing values.Each of the items recorded Gender, CGPA and so on - are data values.All the information about a single person makes up one observation.

Gend

er

CGP

A

Inte

r-

pct

MOI

M

Institu

te

A1 A2 A3 E1 E2 E3

1 M 3.9 78 E Pvt A A A B+ B+ A

2 M 3.4 70 E Pub A B+ B+ B+ B+ B+

3 M 3.9 82 E Pub A A A A B+ A

4 M 3.3 70 U Pub B C C A B+ A

5 M 3.08 80 E Pub B B C+ C+ B B+

6 M 3.0 73 E Pvt C C+ C B B+ B+

7 M 2.70 56 E Pub C B C C+ C B+

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8 M 3.54 81 E Pvt A A A B+ B+ A

9 M 3.87 85 E Pub A B+ B+ B+ B+ B+

10 M 2.23 68 E Pvt B+ C B+ B A B+

11 M 3.01 71 E Pub A B B+ C B B+

12 M 3.45 76 E Pvt C C B+ B+ B+ B+

13 M 3.41 80 U Pvt A C+ B+ B+ B+ A

14 M 3.5 83 E Pvt A C+ B+ B+ B+ B+

15 M 3.35 60 E Pub A B B+ B+ C+ A

16 M 3.50 70 E Pvt A B B B+ C+ A

17 M 2.96 75 E Pub C+ C C B+ C+ B

18 M 2.5 64 E Pub C C C+ B+ C B

19 M 3.83 78 E Pvt A C+ A A C+ A

20 F 3.4 75 E Pub A A C+ A A A

21 F 3.6 78 E Pub A B+ B+ A C B+

22 F 3.66 68 E Pvt A B+ A C

23 F 3.5 80 U Pvt A B+ B+ C+ C+ B+

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24 F 3.4 82 E Pub B+ B+ B+ B C+ B+

25 F 3.77 74 E Pub A B+ A A B+ A

26 F 3.25 70 E Pub B+ B+ B B C+ B+

27 F 3.69 76 E Pub A A A B+ C+ B+

28 F 3.36 79 E Pub B B B+ A B A

29 F 2.89 63 E Pub C+ C+ F A C+ B+

30 F 2.9 62 E Pub C B+ C B+ C B

31 F 2.83 70 E Pub C C C B+ C A

32 F 2.9 67 E Pub B C+ F B B+ B+

33 F 3.42 75 U Pub B+ C+ A B+ B B+

34 F 2.8 70 U Pub C+ C B+ B+ B B+

35 F 2.67 70 E Pub B C+ C B+ C+ B+

36 F 3.16 68 E Pub C B C+ C+ B+ A

37 F 3.04 73 U Pub C+ B+ C+ B B B+

38 F 2.6 75 E Pub C+ B F B B+ B+

Table

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VARIABLE VIEW

We will not enter data directly in the Data View; rather it is more preferable to first

give the name and other features of the variable going into the variable view.

1

Rows:No. of Variables

Columns:(We have description of the variables)


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Variable View has total 10 Columns:

Name, Type, Width, Decimals, Label, Values, Missing, Columns, Align and Measure.

Variable Name

The rules for names are:

The name must begin with a letter. The remaining characters can be any letter, any digit, aperiod, or the symbols @, #, _, or $;

Variable names cannot end with a period;

Variable names that end with an underscore should be avoided; The length of the name cannot exceed 64 bytes. Sixty-four bytes typically means 64 characters in

single-byte languages (eg, English, French, German) and 32 characters in double-byte languages

(eg, Japanese, Chinese, Korean);

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Spaces and special characters (eg !, ?, ', and *) cannot be used;

Each variable name must be unique; duplication is not allowed;

Point and click on the cell in row 1 and column 1. Type Genderin this cell. Use the down arrow to move

to row 2, column 1.

Type CGPA in this cell. Use the down arrow to move to row 3, column 1.

Continue with this process until all 12 variable names given in the Table 2

Variable T ypes

The Type column is showing Numeric for all rows. This means that numeric

(number) values will be expected in the dataset relating to these variables. This is correct for all the

variables.

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Variable Width and D ecimal places

The next column is headed Width and deals with the maximum number of characters that will be

displayed for a particular variable in all output relating to this variable. It does not control the display in

the Data View window, which is determined by Columns - see later. For a numeric variable it needs to be

considered alongside the next column labeled Decimals. The value in this column indicates the number

of decimal places that will be displayed in all output relating to this variable. By default the Width value

is set to 8 and Decimals to 2.

For finer control of your output, you can alter values as necessary.

For the example dataset, it would be better to choose:

Width 1, Decimals 0 for variables Gender, MOIM, Institute, A1, A2, A3, E1, E2, E3

Width 2, Decimals 0 for variable inter pct.

Width 3, Decimals 2 for variable GPA

Variable L abels

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The next column is headed Label and is used to inform SPSS about the details associated with each

variable name. The maximum length of any label is 256 characters and there are no restrictions on what

may appear. Spaces are entered just as typed. If you want to specify where a new line appears in a label,

type \n within the text and SPSS will wrap the label at this point.

Moving to the first row, fifth column, click on the cell and type in the words: Gender of the student, the

width of the column will expand to allow for the number of characters in the label.

Continue entering the labels for all the other variables as given in the earlier table.

To correct any existing labels, double-click on the entry and edit as you would in a wordprocessor.

Gender: Gender of the student CGPA: CGPA obtained by the respondents Inter pct: Inter percentage students MOIM: Medium Communication Institute: Institution respondents A1: Principles of Accounting 1 A2: Principles of Accounting 2 A3: Cost Accounting E1: Functional English 1 E2: Functional English 2 E3: Oral Communication

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Value L abels

The next task is to enter Value Labels for each variable if appropriate. These will appear in the Values

column. For Gender you can indicate that M is male and F is female.

Move to row 1 column 6 and click in the cell. A dropdown menu appears so you can provide Value Labelinformation

In the box by the word Value type M. In the box by the word Label type Male.

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Click on Add and watch the value and its label move to the bottom box.

In the box by the word Value now typeFand the wordFemale in the Label box.

Click on Add.

Click OK

Click on row 4 column 6. Enter the value 0 and labelE in the dropdown menu box and click Add. Enter

the Value 1 and Label Uand click on Add. Click OK.

Click on row 5 column 6. Enter the value 1and label Pub in the dropdown menu box and click Add.

Enter the Value 2 and LabelPvtand click on Add. Click OK.

Finally for the variables A1, A2, A3, E1, E2 and E3 you will need to provide six value labels for each.

Use the basic method to enter this information for the variable A1

Value 1 has the labelFail Value 2 has the labelC Value 3 has the labelC+

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Value 4 has the labelB Value 5 has the labelB+ Value 6 has the labelA

Now, copy and paste for the other five variables A2, A3, E1, E2 and E3.

Data Display

The next two columns (Columns and Align) are concerned with the display of data in the Data View

window. For the purposes of this example dataset, the default values of a column 8 characters wide and

the values right aligned are fine. When you have entered your data as instructed below, return to the

Variable View window and change one or more of these values. Then flip to the Data View window and

see the effect your choice has made.

Measurement Scale of Variables

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The final column is concerned with the measurement scale properties of your variable. In statistics

certain procedures are only appropriate for variables measured on specific scales of measurement. The

measure characteristics recognized by SPSS are as follows:

scale to represent a numeric variable that can take discrete or

continuous

values along a range

ordinal to represent values that, although numeric, only represent

an ordered

listing of such values

nominal to represent values that are simply names

You should be able to recognize that in the example dataset, there are:

3 nominal measures Gender, MOIM and Institute

6 ordinal measures A1, A2, A3, E1, E2, E3

2 scale measures - CGPA and Inter pct

Starting with Gender in row 1, column 10, click on the cell and choose the appropriate measure. (You

should not have to change this from the default).

We have now defined all the information that SPSS needs to know about the characteristics of your

specific dataset.

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We may return to the Variable View window at any time if further changes areneeded.

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DATA VIEW

The Data View pane of the Data Editor window is used to enter the data.

Displayed initially is an empty spreadsheet with the variable names you havedefined appearing as the

column headings.

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Columns: Questions, Item

Rows: Cases/Respondents


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To enter the first respondents data, click the first cell of Gender. Type 0.

Press the key or right arrow once and the heavy outline moves to the next column.

Type in 3.90 and press the key.

Type in 78and press the key.

Type in 0and press the key.

Follow the same procedure along the first row until all eleven data values are entered.

Move back to row 2, column 1 and start to enter the values for interview 2. Press the key twice to

skip over a column.

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Some people find it easier to enter data by column rather than by row. The methodis similar except that

you use the down arrow key instead of the key.

The and keys take the cursor to the first or last column of a particular case. will take you to row 1, column 1, and to the last used cell.

Computing Variables

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To gather up all quantitative in one and verbal subjects in another variable:

You can create a new variable to hold the new recoded values and preserve theoriginal values:

In the Data Editor window clickTransform.

From the Transform menu clickCompute.

A dialog box opens up. Enter Quant in Target Variable

Function group click All

Functions and transferable groups clickMean

Press

In Numeric expression, this would appearMEAN (?,?)

From source list enterA1, A2, A3 separating by commas.

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To gather all the verbal subjects, follow the same procedure by putting Verbal in

Target Variable

Function group clickAll

Functions and transferable groups clickMean

Press

In Numeric expression, this would appearMEAN (?,?)

From source list enterE1, E2, E3 separating by commas.

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Two more columns would be added in Data View: QUANTandVerbal

At this point the DATA would give us an inappropriate result because of the

wrong assigning of values in A1, A2, A3, E1, E2 and E3

Modifying Variables

TransformRecode into different variables

From the Source View, send A1 to Numeric view

EnterA11 in Output Variable

Press Change

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Press Old and new values and change the written values:

In Old Values enter2 and in New Values enter6 and press enter

In the same way substitute:

5 with 2 4 with 3 3 with 4 2 with 5 1 with 6

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ClickContinue

Apply the same procedure to produce

A22 from A2 A33 from A3 E11 from E1 E22 from E2 E33 from E3

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Again Recoding values into a new variable

Use A11, A22, and A33 to make a new variable: QUANT1

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Use E11, E22 and E33 to make another variable: Verbal1

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Two more columns would be added in Data View: QUANT1 andVerbal1

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Creating charts - drawing a scatter plot

A scatter plot graphs one variable against another, and often gives an idea of any associations in the

data.

Graph Legacy Dialogues Scatter plotMatrix Scatter

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EnterCGPA, Inter pct, MOIM, Institute, QUANT1 and Verbal1 in Matrix Variables

Gender in Set Markers by

Press OK

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SCATTER MATRIX

Verbal1QUANT1InstiuteMOIMInterceptCGPA

Verbal1

QUANT1

Instiute

MO

IM

Intercept

CGPA

Female

Male

Gender

Double click on the GRAPH to open Chat Editor

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In Chart Editor, click on the icon offit line

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Click Close

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Verbal1

QUANT1

In

stiute

MOIM

Intercept

CGPA

Fit line for Total

Female

Male

Gender

RESULTS and INTERPRETATIONS

A2: Result (Conclusion)

There is a positive correlation between CGPA and Intercept.

Interpretation (Discussion)

When Inter pct increases, CGPA also increases.

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There is a slightly negative relationship between CGPA and Urdu Medium of Instruction.


When the ratio of Urdu medium intermediate pass-outs increases, CGPA decreases.


There is a positive correlation between CGPA and Public Institutions.


When the ratio of Public Institution pass outs increases, CGPA increases.


There is a positive correlation between CGPA and Quantitative Results.


When marks obtained by students in quantitative subjects increase, CGPA also increases.


There is a positive correlation between CGPA and Verbal Results.


When marks obtained by students in verbal subjects increase, CGPA also increases.

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CORRELATIONS

The correlations procedure calculates the (Pearson parametric) correlation between variables and is used

to measure the strength of linear association between 2variables.

To obtain the Pearson correlation coefficients of CGPA, Inter pct, MOIM, Institute, QUANT and Verbal

Select Analyze Correlate Bivariate

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First enter the dependent variable: CGPA

Then Independent Variables: Inter pct, MOIM, Institute, QUANT1, Verbal1

ClickOK

Correlations

CGPA Intercept MOIM Instiute QUANT1 Verbal1

CGPA Pearson Correlation1 .593(**) -.010 .489(**) .791(**) .419(**)Sig. (2-tailed) .000 .955 .002 .000 .009

N 38 38 38 38 38 38

Intercept Pearson Correlation .593(**) 1 .037 .422(**) .505(**) .334(*)

Sig. (2-tailed) .000 .825 .008 .001 .040

N 38 38 38 38 38 38

MOIM Pearson Correlation -.010 .037 1 -.030 -.086 .228

Sig. (2-tailed) .955 .825 .859 .609 .168

N 38 38 38 38 38 38

Instiute Pearson Correlation .489(**) .422(**) -.030 1 .309 .147

Sig. (2-tailed) .002 .008 .859 .059 .379

N 38 38 38 38 38 38

QUANT1 Pearson Correlation .791(**) .505(**) -.086 .309 1 .309

Sig. (2-tailed) .000 .001 .609 .059 .059

N 38 38 38 38 38 38

Verbal1 Pearson Correlation .419(**) .334(*) .228 .147 .309 1

Sig. (2-tailed) .009 .040 .168 .379 .059

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N 38 38 38 38 38 38

** Correlation is significant at the 0.01 level (2-tailed).

* Correlation is significant at the 0.05 level (2-tailed)


Result (Conclusion)

rci (38) = .593 ; p


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As .05


As > , o we do not reject Ho, the results are not statistically significant at 5% level of significance.

The sample data does not support the alternative hypothesis (HA). I.e. the population correlation

coefficient is not significantly different from zero. (The relationship between the variables in the sample

does not hold for same variables in the population). To put it in other words we cannot generalize the

sample results for the whole population. In the current situation, the correlation between the CGPA and

Institution for students is -0.10 which only holds for the sample and not for the whole population of

students.

Result (Conclusion)

rci (38) = .489; p


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significantly different from zero. (The relationship between the variables in the sample also holds for

same variables in the population). To put it in other words we can generalize the sample results for the

whole population. In the current situation, the correlation between the CGPA and intermediate

percentage for students is .489 which holds for the sample as well for the whole population of students.

Result (Conclusion)

rcq (38) = .791; p


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Mean and Standard Deviation

Select Analyze Correlate Bivariate OptionsMeans and Standard Deviations Continue

Descriptive Statistics

Mean Std. Deviation N

CGPA 3.2439 .41850 38

Intercept 73.03 6.780 38

MOIM .11 .311 38

Institute 1.29 .460 38

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QUANT1 4.1535 1.29817 38

Verbal1 4.6316 .68790 38



When mean of Intercept is 73.04, mean CGPA is 3.2439.


As mean intercept increases, CGPA increases.


When mean of MOIM is .11, mean CGPA is 3.2439.


As mean of Urdu MOIM increases, CGPA decreases.


When mean of Public is 1.29, mean CGPA is 3.2439.


As mean of Urdu MOIM increases, CGPA decreases.


When mean of Quantitative subjects is 4.15, mean CGPA is 3.2439.

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As mean of Quantitative subjects increases, CGPA increases.


When mean of Verbal subjects is 4.63, mean of CGPA is 3.2439.


As mean of verbal subjects increases, CGPA increases.

Measurements According to Gender

Data Split File Compare Groups

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Select Gender from source list

Press

ClickOK

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SCATTER MATRIX for MalesGraph Legacy Dialogues Scatter plotMatrix Scatter


Verbal1

QUAN

T1

Instiute

MOIM

Inter

cept

CGPA

Gender: Male

Fit line for Total

Male

Gender


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A2: Result (Conclusion)There is a positive correlation between CGPA and Intercept.

Interpretation (Discussion)When Inter pct of male students increases, CGPA also increases.

A3: Result (Conclusion)There is a slightly negative relationship between CGPA and Urdu Medium of Instruction.

Interpretation (Discussion)When the ratio of Urdu medium intermediate pass-outs of males increases, CGPA decreases.

A4: Result (Conclusion)There is a positive correlation between CGPA and Public Institutions.

Interpretation (Discussion)When the ratio of Public Institution pass outs of males increases, CGPA increases.

A5: Result (Conclusion)There is a positive correlation between CGPA and Quantitative Results.

Interpretation (Discussion)When marks obtained by male students in quantitative subjects increase, CGPA also increases.

A6: Result (Conclusion)There is a positive correlation between CGPA and Verbal Results.

Interpretation (Discussion)When marks obtained by male students in verbal subjects increase, CGPA also increases.

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SCATTER MATRIX for Females


Verbal1

QUANT1

Instiute

MOIM

Intercept

CGPA

Gender: Female

Fit line for Total

Female

Gender

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A2: Result (Conclusion)There is a positive correlation between CGPA and Intercept.

Interpretation (Discussion)When Inter pct of female students increases, CGPA also increases.

A3: Result (Conclusion)There is a negative relationship between CGPA and Urdu Medium of Instruction.

Interpretation (Discussion)When the ratio of Urdu medium intermediate pass-outs of females increases, CGPA decreases.

A4: Result (Conclusion)There is a positive correlation between CGPA and Public Institutions.

Interpretation (Discussion)When the ratio of Public Institution pass outs of females increases, CGPA increases.

A5: Result (Conclusion)There is a positive correlation between CGPA and Quantitative Results.

Interpretation (Discussion)When marks obtained by female students in quantitative subjects increase, CGPA also increases.

A6: Result (Conclusion)There is a positive correlation between CGPA and Verbal Results.

Interpretation (Discussion)3


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When marks obtained by female students in verbal subjects increase, CGPA also increases.

CORRELATION According to GenderCorrelations

Gender CGPA Intercept MOIM Institute QUANT1 Verbal1

Male CGPA PearsonCorrelation

1 .635(**) .052 .558(*) .675(**) .598(**)

Sig. (2-tailed) .003 .833 .013 .002 .007

N 19 19 19 19 19 19

Intercept PearsonCorrelation

.635(**) 1 .058 .557(*) .448 .505(*)

Sig. (2-tailed) .003 .812 .013 .054 .027

N 19 19 19 19 19 19

MOIM PearsonCorrelation

.052 .058 1 .018 -.154 .400

Sig. (2-tailed) .833 .812 .941 .530 .089

N 19 19 19 19 19 19

Institute PearsonCorrelation

.558(*) .557(*) .018 1 .282 .398

Sig. (2-tailed) .013 .013 .941 .242 .091

N 19 19 19 19 19 19

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QUANT1 PearsonCorrelation

.675(**) .448 -.154 .282 1 .486(*)

Sig. (2-tailed) .002 .054 .530 .242 .035

N 19 19 19 19 19 19

Verbal1 PearsonCorrelation

.598(**) .505(*) .400 .398 .486(*) 1

Sig. (2-tailed) .007 .027 .089 .091 .035

N 19 19 19 19 19 19Female CGPA Pearson

Correlation1 .505(*) -.088 .363 .952(**) .184

Sig. (2-tailed) .027 .719 .127 .000 .451

N 19 19 19 19 19 19

Intercept PearsonCorrelation

.505(*) 1 .008 .104 .597(**) .085

Sig. (2-tailed) .027 .973 .671 .007 .730

N 19 19 19 19 19 19

MOIM PearsonCorrelation

-.088 .008 1 -.118 -.017 .062

Sig. (2-tailed) .719 .973 .631 .946 .800

N 19 19 19 19 19 19

Institute PearsonCorrelation

.363 .104 -.118 1 .368 -.360

Sig. (2-tailed) .127 .671 .631 .121 .130

N 19 19 19 19 19 19

QUANT1 PearsonCorrelation

.952(**) .597(**) -.017 .368 1 .118

Sig. (2-tailed) .000 .007 .946 .121 .630

N 19 19 19 19 19 19

Verbal1 PearsonCorrelation

.184 .085 .062 -.360 .118 1

Sig. (2-tailed) .451 .730 .800 .130 .630

N 19 19 19 19 19 19

** Correlation is significant at the 0.01 level (2-tailed).

* Correlation is significant at the 0.05 level (2-tailed).

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RESULTS and INTERPRETATIONS for Males

Result (Conclusion)

rci (19) = .635 ; p


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As .05



The sample data does not support the alternative hypothesis (HA). I.e. the population correlationcoefficient is not significantly different from zero. (The relationship between the variables in the sample



Institution for male students is 0.5 which only holds for the sample and not for the whole population of

male students.

Result (Conclusion)

rci (19) = .558 ; p


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As


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same variables in the population). To put it in other words we can generalize the sample results for the

whole population. In the current situation, the correlation between the CGPA and intermediate

percentage for male students is .598 which holds for the sample as well for the whole population of male

students.

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RESULTS and INTERPRETATIONS for Females

Result (Conclusion)

rci (19) = .505 ; p , o we do not reject Ho, the results are not statistically significant at 5% level of significance.





Institution for female students is -.088 which only holds for the sample and not for the whole population

of female students.

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Result (Conclusion)

rci (19) = .363 ; p>.05







Institution for female students is .363 which only holds for the sample and not for the whole population

of female students.

Result (Conclusion)

rcq (19) = .952; p


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does not hold for same variables in the population). To put it in other words we cannot generalize thesample results for the whole population. In the current situation, the correlation between the CGPA and

Institution for female students is .184 which only holds for the sample and not for the whole population

of female students.

MEAN AND Standard Deviation According to

Gender

Descriptive Statistics

Gender Mean Std. Deviation N

Male

Female

CGPA 3.2858 .47080 19

Intercept 73.68 7.945 19

MOIM .11 .315 19


QUANT1 4.2456 1.32796 19

Verbal1 4.7193 .68730 19

CGPA 3.2021 .36698 19Intercept 72.37 5.520 19

MOIM .11 .315 19


QUANT1 4.0614 1.29721 19

Verbal1 4.5439 .69576 19

RESULTS and INTERPRETATIONS for Males

A2: Result (Conclusion)When mean of Intercept is 73.68, mean CGPA is 3.2858

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Interpretation (Discussion)As mean intercept increases, CGPA increases.

A3: Result (Conclusion)When mean of MOIM is .11, mean CGPA is 3.2858

Interpretation (Discussion)As mean Urdu MOIM increases, CGPA decreases.


When mean of Institution is 1.47, mean of CGPA is 3.2858 Interpretation (Discussion)As mean of Urdu MOIM increases, CGPA decreases.

A5: Result (Conclusion)When mean of Quantitative subjects is 4.24, mean CGPA is 3.2858

Interpretation (Discussion)As mean of Quantitative subjects increases, CGPA increases

A6: Result (Conclusion)When mean of Verbal subjects is 4.63, mean of CGPA is 3.2858

Interpretation (Discussion)As mean of verbal subjects increases, CGPA increases

RESULTS and INTERPRETATIONS for Females


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When mean of Intercept is 72.47, mean CGPA is 3.202

Interpretation (Discussion)As mean intercept increases, CGPA increases.

A3: Result (Conclusion)When mean of MOIM is .11, mean of CGPA is 3.202

Interpretation (Discussion)As mean of MOIM increases, CGPA decreases.

A4: Result (Conclusion)When mean of Public is 1.11, mean of CGPA is 3.202

Interpretation (Discussion)As mean of Urdu MOIM increases, CGPA decreases.

A5: Result (Conclusion)When mean of Quantitative subjects is 4.06, mean of CGPA is 3.202

Interpretation (Discussion)As mean of Quantitative subjects increases, CGPA increases

A6: Result (Conclusion)When mean of Verbal subjects is 4.54, mean of CGPA is 3.202

Interpretation (Discussion)As mean of verbal subjects increase, CGPA increases

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REMARKS

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SIGNATURES

correlation and scatter diagram

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