spss presentation1

8/2/2019 SPSS Presentation1

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Welcome to Our

Presentation

Group: 16


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Multiple Regression & CorrelationAnalysis of Investments


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Variables

Dependent Variable

Investments ()

Independent Variables

Government Investments (X1) Other Investments (X2)


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General Multiple Regression

Equation

=1.131+1X1+1X2

Investment=Constant+b1GovernmentInvestments+b2Other Investments

= a+b1X1+b2X2


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Correlations (5 Years)

Investments Government

Investments

Other

Investments

PearsonCorrelation

Investments 1.000 0.953 0.913

Government Investments 0.953 1.000 0.747

Other Investments 0.913 0.747 1.000

Investment

GovernmentInvestment

OtherInvestment

GovernmentInvestments

Investments OtherInvestments

OtherInvestments

Investments GovernmentInvestments


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R

Measures thestrength of the linearrelationship between

variables. Here R is 1.00. Direct or positive

association betweenthe variables.

R Square

Here R square is1.00 (strongassociation).

We can say thatindependentvariables caninfluence thedependent variableby 100%.

Std. Error ofthe Estimate

We expect 68% willbe within 0.671,

About 95% will bewithin 1.342

About 99% of theresiduals will bewithin 2.013.

Model SummaryaModel R R Square Adjusted R Square Std. Error of the Estimate

1

1.000a

1.000

1.000

0.671

a. Predictors: (Constant), 5 years other investments, 5 years government

investments


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Global Test

H0: 1= 2= 0H1: Not all the

is are 0

Critical value ofF is 19 (Fromappendix B.4)

Decision Rule:When Fc > 19,H0 should be

rejected.

ANOVAbModel Sum of

Squaresdf Mean

SquareF Sig.

1 Regression 7.013E7 2 3.506E7 7.795E7 0.000aResidual 0.900 2 0.450Total 7.013E7 4

a. Predictors: (Constant), 5 years other investments, 5 years government investmentsb. Dependent Variable: 5 years investments


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H0:1= 0

H1:1 0

GovernmentInvestments

H0:1= 0

H1: 1 0

OtherInvestments

t is 4.303(FromappendixB.2).

Critical

value of t

Reject H0 if-4.303 > t >4.303.

DecisionRule

Coefficientsa (5 Years)Model Unstandardized

CoefficientsStandardized

Coefficientst Sig.

B Std. Error Beta1 (Constant) 1.131 1.053 1.074 0.395

Government Investments 1.000 0.000 0.612 5.087E3 0.000Other Investments 1.000 0.000 0.456 3.786E3 0.000

a. Dependent Variable: 5 years investments

Evaluating Individual Regression

Coefficients


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= a+b1X1

Investment=Constant+b1Government

Investments

=1.131+1X1

New Regression Equation


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General Rule:Correlation between two

independent variableswithin 0.70 (no problemof using the independent

variables).

Correlation exceedsthe range

There ismulticollinearity

Chance of providingincorrect results in the

hypothesis tests forindividual independent

variables.

Multicollinerity

Coefficient Correlationsa(5 Years)Model Other

InvestmentsGovernment

Investments1 Correlati

ons Other Investments 1.000 -0.747Government Investments -0.747 1.000a. Dependent Variable: 5 years investments


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Multiple Regression & CorrelationAnalysis of EAT


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Variables:

Operating Income(X1)

OperatingExpense(X2)

IndependentVariables

Earnings After Tax()

Dependent

variables


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Regression Equation:

= a+ b1X1+ b2X2

EAT= Constant + b1 Operating

Income+b2Operating Expense

= 398.126 + 0.524 X1-0.659 X2

Intercept

a=398.126

EAT is 398.126

Million whenoperating incomeand operatingexpense is zero

b1= 0.524

EAT will increase

by 0.524 Million,regardless of theoperating expense.

b2= -0.659

EAT will decrease

by 0.659 Million,regardless of theoperating income.


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Correlation:

EAT OperatingIncome OperatingExpensePearsonCorrelation

EAT 1.000 .966 .700OperatingIncome .966 1.000 .848OperatingExpense .700 .848 1.000

Positive and strongcorrelation withOperating Incomeand

Positive but lessstrong correlationwith OperatingExpense.

EAT

Positive and strongcorrelation withEAT and OperatingExpense

OperatingIncome Positive but less

strong correlationwith EAT

Strong positivecorrelation withOperating Income.

OperatingExpense


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Model Summary:

Model R R SquareAdjustedR Square

Std. Error of theEstimate

1 .993a .985 .971 215.693

R is 0.993 which isquiet near to 1.

Direct or positiveassociation betweenthe variables

R

Operating incomeand operatingexpense can explain98.5% variation independent variableEAT

R Square

68% of the residualswill be within 215.693

95% of the residualswill be within 431.386and

99% of the residualswill be within647.079

Std. Errorof Estimate


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Global Test: Hypothesis:

H0:1= 2= 0

H1:Not all the isare 0

Critical value: Critical value of F is 19.

Decision Rule:Reject H0 if (Fc> 19), calculated valueof F is greater than 19.

ANOVAModel

Sum of

Squares df Mean Square F Sig.

1

Regression 6251884.240 2 3125942.120 67.191 .015a

Residual 93046.560 2 46523.280

Total 6344930.800 4


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Individual Test Of Hypothesis For Operating Income:

H0:

1= 0 H

1:

10

For Operating Expense:

H0:2= 0 H1:2 0

Critical value: Critical value of t is 4.303

Decision Rule:Reject H0 if tc< -4.303 or tc> 4.303

ModelUnstandardizedCoefficients StandardizedCoefficients t Sig.

B

Std.

Error Beta

1

(Constant) 398.126 311.857 1.277 .330

Operating

Income.524 .064 1.330 8.221 .014

Operating

Expense-.659 .249 -.428 -2.647 .118


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New RegressionEquation

= a+ b1X1

EAT= Constant +

b1Operating Income

= 398.126 + 0.524 X1


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MulticollinearityModel OperatingExpense OperatingIncome

CorrelationsOperatingExpense 1.000 -.848

OperatingIncome -.848 1.000

No problem withusing variable

havingcorrelationbetween0.70and +.70

GeneralRule Correlation

between operatingincome andoperating expense

is -0.848.

MulticollinearityExist

TestResult


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Multiple Regression & CorrelationAnalysis of Loans and Advances


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Variables

Total Loansand advances

Dependent

Variable()

Loans, cashcredits (X1)

Independentvariable

Billspurchased(X2)

Independent

variable

R i E ti


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Regression Equation:

= a+ b1X1+ b2X2

Total loans and advances= Constant+b1 cash credits+

b2Bills Purchased= -205.375+ 1.042 X1+0.039 X2Loans

Intercept

a = -205.375

Total loansand advancesis tk.-205.375

when loans,cash creditsand billspurchased iszero.

b1= 1.042

Total loansandadvanceswouldincrease per

million by1.042,regardlessof the billspurchased

b2= 0.039

Total loansandadvanceswouldincreaseper 0.039,regardlessof theloans, cashcredits


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CorrelationsTotal loans

and advances

Loans Cash

credits

Bills

purchased

PearsonCorrelationTotal loansand advances 1.000 1.000 -.521

Loans Cash

credits1.000 1.000 -.530

Bills

purchased-.521 -.530 1.000

Total loansThere is a positive

and strong

relationship withloans, cash creditsand negativerelationship withbills purchased.

Loans cash creditsThere is a positive

and strong

relationship withtotal loans andadvances. There isa positive but lessstrong relationshipwith billspurchased

Bills purchasedThere is a negative

relationship withboth total loansadvances andloans, cash credits.


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Model SummaryModel R R Square Adjusted RSquare Std. Error ofthe Estimate

1 1.000a 1.000 1.000 385.10153a. Predictors: (Constant), Bills purchased; Loans, Cash credits

R

Here R is01.00. Itindicates

that there isa direct orpositiveassociationbetween thevariables.

R square

Here Rsquare is1.00 which

indicatesthat thereare strongassociationbetween thevariables

Std. error of

estimate

68% residualswill be between385.105,

95 %residualswill be between770.203

99 % residualswill be between 1155.304

G i i


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Global Test: Testing the Multiple

Regression Model Hypothesis:

H0:1= 2= 0

H1: Not all the isare 0

Critical value : of F is 19.

Decision Rule: Reject H0 if (Fc> 19), calculated value of

F is greater than 19.

ANOVA

Model Sum of Squares df Mean Square F Sig.

1 Regression 2.693E9 2 1.346E9 9.078E3 .000a

Residual 296606.375 2 148303.187

Total 2.693E9 4


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For Loans, cash creditsH0: 1=0 H1:1 0

For bills purchasedH0: 2= 0 H1: 2 0

Critical value: Critical value of t is 4.303

Decision Rule:Reject H0 if tc< -4.303 or tc> 4.303

Model Un standardizedCoefficients StandardizedCoefficients t Sig.B Std. Error Beta

1

(Constant) -205.375 649.868 -.316 .782

Loans Cash

credits1.042 .009 1.006 115.008 .000

Bills purchased.039 .028 .012 1.377 .302

Individual Test Of Hypothesis


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New RegressionEquation

= a+b1X1Total loans and advances=Constant+b1loans, cash credit=-205.375+1.042X1


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MulticollinearityModel Billspurchased Loans andcash credits

CorrelationsBills purchased 1.000 .530Loans Cashcredits .530 1.000

No problem with using variablehaving correlation between0.70and +.70General Rule

Correlation betweenBills purchased and loans Cashcredits is .530.

Multicollinearity does not exist.

Test Result


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Multiple Regression & CorrelationAnalysis of Income


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Variables

Dependent variables:

Dependent variables:

Total income, represented by

Independent variables:Other income, represented by X1Commission, exchange &brokerage,

represented by X2Investment income, represented by X3come, represented by Independent variables:

Other income, represented by X1


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Regression Equation

= a+ b1 X1+ b2 X2+ b3 X3

Total income= b1 Investment income + b2

Commission, exchange & brokerage+ b3 other

income

= -152.335 -3.238 X1 +6.219 X2+4.028 X3


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Correlation:

TotalIncome InvestmentIncome CommissionExchange&BrokerageOtherIncome

PearsonCorrelation

Total

income1.000 0.588 0.840 -0.243

Investmentincome 0.588 1.000 0.928 -0.801Commission, exchange&brokerage

0.840 0.928 1.000 -0.654

Otherincome -0.243

-0.801

-0.654

1.000


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Model Summary

Model SummaryModel R R Square Adjusted R

Square Std. Errorof theEstimate

1 0.988a 0.975 0.901 597.12121a. Predictors: (Constant), Other income, commission, exchange &brokerage, Investment income

Gl b l T


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Global Test

ANOVAModel Sum of

Squares

df Mean

Square

F Sig.

1

Regression 1.409E7 3 4696202.

487

13.171 0.199a

Residual 356553.74

0

1 356553.7

40

Total 1.445E7 4

H0: 1= 2= 3= 0

H1: Not all the is are 0Critical value: Critical value of F is 216.

Decision Rule: Reject H0if (Fc > 216), calculated value

of F is greater than 216.

I di id l H th i T t


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Individual Hypothesis Test

CoefficientsaModel

Unstandardized

CoefficientsStandardiz

ed

Coefficientst Sig.

B Std. Error Beta1 (Constant) -152.335 3324.171 -0.046 0.971

Investment

income-3.238 1.561 -1.205 -2.074 0.286

Commission,

exchange &

brokerage6.219 1.402 2.042 4.436 0.141

Other income 4.028 9.028 0.128 0.446 0.733a. Dependent Variable: Total income


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New Regression Equation

= a+ b1 X1+ b2 X2+ b3 X3= -152.335 - 3.238 X1 +6.219

X2+4.028 X

3

= -152.335 - 3.238 (0) +6.219 (0)+4.028 (0)

= -152.335


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Multicollinearity

Coefficient CorrelationsModel Other

Income

Commission

Exchange &Brokerage

InvestmentIncome

1

Correlations

Other income1.000 -0.399 0.687

Commission,exchange&brokerage

-0.399 1.000 -0.892

Investmentincome

0.687 -0.892 1.000

Covariance

Other income81.505 -5.044 9.686

Commission,exchange&brokerage

-5.044 1.965 -1.953

Investmentincome

9.686 -1.953 2.438

a. Dependent Variable: Total income


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Multiple Regression&Correlation Analysisof Deposits


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Dependent variables:Total deposits, represented by

Independent variables:Current deposits, represented by X1

Savings Bank deposits, represented

by X2

Fixed deposits, represented by X3

Variables


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General Multiple Regression

Equation = a+ b1X1+ b2X2+b3X3

Total deposits = b1 current deposits+ b2savings bank deposits + b3 fixed deposits

= -39237.267-6.294X1+10.676 X2+0.900 X3


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PEARSONCORRELATIONTotal

DepositsCurrentDeposits

FixedDeposits

SavingsBank

Deposits

TotalDeposits

1.000 0.945 0.954 0.980

CurrentDeposits 0.945

1.000 0.916 0.975

FixedDeposits

0.954 0.916 1.000 0.903

SavingsBankDeposits

0.980 0.975 0.903 1.000


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OPINION

In case of total deposits there is a positive and

strong relationship with current, savings bank and

fixed deposits

In case of current deposits there is a positive and


fixed deposits

In case of fixed deposits there is a positive and

strong relationship with current, savings bank andfixed deposits

In case of savings deposits there is a positive and


fixed deposits


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MODEL

SUMMARY

Model R R Square Adjusted RSquare Std. Errorof theEstimate1 0.999a 0.998 0.992 1894.995

Predictors: (Constant), fixed deposit, savings bank

deposit, current deposit


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GLOBAL TEST(ANOVA)Model Sum of

Squaresdf Mean

SquareF Sig.

Regression

Residual

Total

1.827E9

3591005.111

1.830E9

3

1

4

6.089E8

3591005.111

169.553 0.56a


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a. Predictors:(Constant) fixed deposit,

savings bankdeposit, current deposit

b. Dependent Variable:Total deposits

H0: 1= 2=3=0

H1: Not all the isare 0 Decision Rule:Reject H0if (Fc>

216), calculated value of F is greater

than 216.


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General MultipleRegression EquationCoefficients

Model Un standardizedCoefficients StandardizedCoefficients t Sig.

B Std. Error Beta1 (Constant) -39237.267 7875.027 -4.982 0.126

Current Deposit -6.294 2.532 -0.529 -2.486 0.244Savings Bank

Deposit 10.676 1.995 1.067 5.351 0.118Fixed Deposit 0.900 0.211 0.474 4.272 0.146

a. Dependent Variable: total deposits


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= aTotal deposits= Constant

=-39237.267

New Equation:


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Coefficient Correlations

Model Fixeddeposit

Savingsbank

deposit

Currentdeposit

1 Correlations Fixed deposit 1.000 -0.122 -0.366

Savings bankdeposit

-0.122 1.000 -0.855

Currentdeposit

-0.366 -0.855 1.000

a. Dependent Variable: Total deposits

Multicollinerity:


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Thank You

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