GDP INF EXPO PG 1

7/31/2019 GDP INF EXPO PG 1

1/21

IMPACT OF EXPORT POPULATION GROWTH AND INFLATION ON GDP

1

ARM TERM REPORT


(MBA EVENING SUMMER 2012)

TERM REPORT OF RESEARCH METHODS

Submitted to:

Sir Tehseen Jawed

Presented By

Faiz Ullah Khan (3052)


2/21


2

This report is based on detail summary to run E-views software for research based analysis. In this reportI will judge my econometric model with the help of different analytical technique to predict my model.Data is collected from state bank of Pakistan, handbook of statistics 2005.

My analysis is based on casual effect. So I am using Regression Analysis to estimate my econometricmodel. Variables of model are INFLATION , EXPORT, GROSS DOMESTIC PRODUCT ANDPOPULATION GROWTH.

DATA IS COLLECTED FROM 1981 -2010 (30 OBSERVATIONS)

YEARS INF Expo GDPG PG1981 11.949 2,957.5 6.4 3.35

1982 5.862 2,489.2 7.6 3.37

1983 6.446 2,710.6 6.8 3.38

1984 6.056 2,769.1 43.36

1985 5.564 2,504.1 8.7 3.32

1986 3.467 3,072.8 6.4 3.28

1987 4.692 3,687.8 5.8 3.24

1988 8.835 4,457.2 6.4 3.15

1989 7.882 4,693.2 4.8 3.03

1990 9.051 4,964.7 4.6 2.88

1991 12.628 6,167.0 5.6 2.72

1992 4.851 6,912.2 7.7 2.58

1993 9.825 6,819.3 2.3 2.49

1994 11.272 6,812.8 4.5 2.491995 13.022 8,137.2 4.1 2.53

1996 10.789 8,707.1 6.6 2.59

1997 11.803 8,320.3 1.7 2.62

1998 7.812 8,627.7 3.5 2.58

1999 5.736 7,779.3 4.2 2.44

2000 3.584 8,568.6 3.9 2.26

2001 4.41 9,201.6 2 2.06

2002 2.504 9,134.6 3.1 1.89

2003 3.102 11,160.2 4.7 1.78

2004 4.568 12,313.3 7.5 1.752005 9.276 14,391.1 9 1.76

2006 7.921 16,451.2 5.8 1.78

2007 7.771 16,976.2 6.8 1.78

2008 11.998 19,052.3 5.8 1.79

2009 20.775 17,688.0 5.8 1.79

2010 11.73 19,290.0 5 1.78


3/21


3

Here I am using Regression analysis in our Econometric model. Data is time series collected from Handbook of Statistics 2005. Published by state bank of Pakistan (SBP).

Our Model of the above variables is

REGRESSION ANALYSIS

A statistical technique use to determine the strength of the relationship between one dependent variable(usually expressed as Y) and more than one independent variables (predictors). Regressions are of twotypes Linear and multiple.

Linear Regression (one Dependent Variable and One Independent Variable)

Multiple Regressions (One DV and more than one IVs) model for multiple regressions can be written as

Y=a +b1X1 +b2X2+b3X3..+BtXt + u

Our model is

Where:

Gdp: variable we are trying to predict

Inf : Predictor of gdp

Pg: predictor of gdp

Expo:predictor of gdp

= Intercept (Constant)

= slope of model (define how much predictor effect on Dependent Variable)

= regression residual (er ror term)

To Run Regression model in e-views first we need to create new work file to enter data. Steps of runningregression are mention below:

FileNewWorkfile enter (now select Annual data write yours starting date and ending date)


4/21


4

In white space write Variables (GDP INF EXPO PG) Press Enter .Fill the data variable vise from excelsheet and run Regression Analysis by the following equation.

Quick Estimate Equatio n gdp c pg expo inf and press ENTER from keyboard

To run Multiple regression analysis we usually used ordinary least square method to minimize error term.

OLS method to run equation Dependent Variable: GDP

Method: Least SquaresDate: 07/28/12 Time: 22:45Sample: 1981 2010Included observations: 30

Variable Coefficient Std. Error t-Statistic Prob.

EXPO 0.000199 6.40E-05 3.104753 0.0044INF -0.081291 0.095864 -0.847983 0.4039PG 1.723572 0.253442 6.800663 0.0000

R-squared 0.121354 Mean dependent var 5.370000Adjusted R-squared 0.056269 S.D. dependent var 1.865873S.E. of regression 1.812617 Akaike info criterion 4.122060Sum squared resid 88.71070 Schwarz criterion 4.262180Log likelihood -58.83090 Hannan-Quinn criter. 4.166886Durbin-Watson stat 1.598787

Results shows that the value of Durbin-watson stat is 1.5. If it is nearer to 2 or equal to 2 it means there isno auto correlation. To confirm this we have to run Serial correlation LM Test for our hypothesis.

H: - Auto correlation does not existHa:- Auto Correlation exits:

Where is 5% significant level

Result explained that our Independent variables inflation have insignificant effect on GDP (DependentVariable) whereas Export and Population Growth have significant impact on GDP.

TEST OF MULTICOLLINEARITY:


5/21


5

Case of multiple regression in which the predictor variables are themselves highly correlated. When thereis multicollinearity exits chances of error are higher significant value become insignificant.

DETECTION OF MULTICOLLINEARITY If value of Independent Variables is greater than 0.7 (correlation exist) VIF(variance inflation factor) is less than 10 or 5 (means no auto correlation) When theories go against each other (i.e universal truth become change)

REMOVAL OF MULTICOLLINEARITY Remove that VIF which has high VIF VIF is high but significant do not exclude Change variables with their ratio which show high correlation i.e. if we have taken FDI as

percentage of GDP.

Fetch excel file in spss and Run the following Procedure ANALLYZEREGRESSIONLINEAR click on STATISTICS mark on COLLINEARITY

DIAGNOSTICS .Now check value of VIF (variance inflation factor).

Coefficients a

Model

UnstandardizedCoefficients

StandardizedCoefficients

t Sig.

CollinearityStatistics

B Std. Error Beta Tolerance VIF

1 (Constant) -5.414 5.004 -1.082 .289

INF -.122 .103 -.260 -1.190 .245 .676 1.478

Expo .000 .000 1.084 2.080 .048 .119 8.394

PG 3.345 1.520 1.081 2.200 .037 .134 7.453

a. Dependent Variable: GDPG

Serial Correlation LM test

Go to View Residual T estLM test

Breusch-Godfrey Serial Correlation LM Test:


6/21


6

F-statistic 0.988030 Prob. F(1,26) 0.3294Obs*R-squared 1.092685 Prob. Chi-Square(1) 0.2959

COCHRAN ORCKUT METHOD

It is a proposed theory by which we can easily remove auto correlation from our model without addingany variable. To run this test we first create Residual series (error) to run for that we take lag(t-1) of error. Purpose of taking this is to create transpose variable to remove trend from auto correlation. Becauseone reason of existence of auto correlation is that it creates trend in error term which create baseness inour model. Now we run our procedure from auto correlation in e-views by Cochran Orckut Method.

Go to Proc click make residual series click OrdinaryER/resid Press EnterNow error series will generate on workfile page . Now we will do our next step, we find out value of byestimate error series.

Chochran test

It is a method of removal of auto correlation. There are three ways to remove auto correlation.

Add variable which can remove auto correlation Using Cochran Orkut method for creating residual error Using AR (1) Method.

Here we enter ER er (-1) method. For this we:

Go to Quick click Estimate equation write er er(-1) Press enter from keyboard

Dependent Variable: ERMethod: Least SquaresDate: 07/28/12 Time: 23:02Sample (adjusted): 1982 2010Included observations: 29 after adjustments


ER(-1) 0.191727 0.185350 1.034409 0.3098

R-squared 0.035675 Mean dependent var -0.059592Adjusted R-squared 0.035675 S.D. dependent var 1.768667S.E. of regression 1.736831 Akaike info criterion 3.975876Sum squared resid 84.46432 Schwarz criterion 4.023024Log likelihood -56.65020 Hannan-Quinn criter. 3.990642Durbin-Watson stat 2.023470


7/21


7

From the above output we can see coefficient of error(-1) which is 0.191727 ,will use this to maketranspose of variables. Now we will generate series for all the variables of our model and again runregression on these transpose variables.

Quick click Generate series write tgdp=gdp-(0.191727)*gdp(-1) (all other variables will begenerate in a similar manner ) press Enter

Dependent Variable: TGDPMethod: Least SquaresDate: 07/28/12 Time: 23:10Sample (adjusted): 1982 2010Included observations: 29 after adjustments


TEXP 80.93919 62.29243 1.299342 0.2052

TINF -0.010484 0.048306 -0.217033 0.8299TPG -17.79209 35.50261 -0.501149 0.6205

R-squared 0.013371 Mean dependent var 32.45069Adjusted R-squared -0.062524 S.D. dependent var 20.47351S.E. of regression 21.10385 Akaike info criterion 9.034485Sum squared resid 11579.68 Schwarz criterion 9.175930Log likelihood -128.0000 Hannan-Quinn criter. 9.078784Durbin-Watson stat 1.515994

Now we see value of DW is still looking suspicious though it is more than 1.5 consider nearer to 2. But

we check again our same hypothesis by LM test.

Go to View click Residual test click LMtest

Breusch-Godfrey Serial Correlation LM Test:

F-statistic 1.523314 Prob. F(1,25) 0.2286Obs*R-squared 1.665558 Prob. Chi-Square(1) 0.1969

AR (1) METHOD

We can also use AR1 method to remove auto correlation in this method we do not need to add new valueof error term. We simply run equation in a OLS manner by using Lag 1.

Quick click Estimate equation write gdp c expo pg inf AR(1) press Enter

Dependent Variable: GDPMethod: Least SquaresDate: 07/28/12 Time: 23:14Sample: 1981 2010


8/21


8

Included observations: 30


C -5.497098 4.992753 -1.101016 0.2810EXPO 0.000392 0.000187 2.099839 0.0456

INF -0.123065 0.102751 -1.197702 0.2418PG 3.369884 1.516428 2.222251 0.0352

R-squared 0.160496 Mean dependent var 5.370000Adjusted R-squared 0.063630 S.D. dependent var 1.865873S.E. of regression 1.805535 Akaike info criterion 4.143157Sum squared resid 84.75887 Schwarz criterion 4.329983Log likelihood -58.14735 Hannan-Quinn criter. 4.202924F-statistic 1.656886 Durbin-Watson stat 1.719396Prob(F-statistic) 0.200699

Heteroskedasticity Test: White

Heteroskedasticity Test: White

F-statistic 1.408901 Prob. F(6,23) 0.2537Obs*R-squared 8.062789 Prob. Chi-Square(6) 0.2335Scaled explained SS 5.393309 Prob. Chi-Square(6) 0.4944

DUMMY VARIABLE

We use dummy variables when we cannot quantify our data in econometric model. We randomly assign 1to the any year where we want to see the effect.

How to create dummy variable in e-views

Go to Data dy(variable for which you want to create dummy) click Quick click estimate equationwrite gdp c expo inf pg dy Press enter

Dependent Variable: GDPMethod: Least SquaresDate: 07/28/12 Time: 23:58Sample: 1981 2010Included observations: 30


C -3.828800 6.647008 -0.576019 0.5698INF -0.122479 0.104482 -1.172258 0.2521


9/21


9

PG 2.898483 1.961470 1.477710 0.1520EXPO 0.000348 0.000221 1.577176 0.1273

DY -0.399318 1.027111 -0.388778 0.7007

R-squared 0.165541 Mean dependent var 5.370000Adjusted R-squared 0.032027 S.D. dependent var 1.865873

S.E. of regression 1.835751 Akaike info criterion 4.203796Sum squared resid 84.24950 Schwarz criterion 4.437329Log likelihood -58.05694 Hannan-Quinn criter. 4.278505F-statistic 1.239881 Durbin-Watson stat 1.687795Prob(F-statistic) 0.319593

Unit root test

Unit root test is used to remove the trend in time series data. First we need to check trend in the data. If trend exist then we will take 1 st difference to remove the trend from the data. If trend did not removedthen we have to take 2 nd difference as well. Data is of two types in unit root test.

STATIONARY : Data in which trend doesn t exist. Non-STATIONARY : Data in which trend exist.

Null Hypothesis: GDP has a unit rootExogenous: ConstantLag Length: 0 (Automatic based on SIC, MAXLAG=1)

t-Statistic Prob.*

Augmented Dickey-Fuller test statistic -3.924160 0.0055Test critical values: 1% level -3.679322

5% level -2.96776710% level -2.622989

*MacKinnon (1996) one-sided p-values.

Null Hypothesis: EXPO has a unit rootExogenous: ConstantLag Length: 0 (Automatic based on SIC, MAXLAG=1)

t-Statistic Prob.*


10/21


10

Augmented Dickey-Fuller test statistic 1.461211 0.9987Test critical values: 1% level -3.679322

5% level -2.96776710% level -2.622989


Null Hypothesis: INF has a unit rootExogenous: ConstantLag Length: 0 (Automatic based on SIC, MAXLAG=1)

t-Statistic Prob.*


5% level -2.967767

10% level -2.622989*MacKinnon (1996) one-sided p-values.

Null Hypothesis: PG has a unit rootExogenous: ConstantLag Length: 1 (Automatic based on SIC, MAXLAG=1)

t-Statistic Prob.*


5% level -2.97185310% level -2.625121


AUGMENTED DICKEY FULLER AT 1 ST DIFFERENCE:

Null Hypothesis: D(GDP) has a unit rootExogenous: ConstantLag Length: 0 (Automatic based on SIC, MAXLAG=1)


5% level -2.97185310% level -2.625121


11/21


11


Null Hypothesis: D(EXPO) has a unit rootExogenous: ConstantLag Length: 1 (Automatic based on SIC, MAXLAG=1)

t-Statistic Prob.*


5% level -2.97626310% level -2.627420


Null Hypothesis: D(INF) has a unit rootExogenous: ConstantLag Length: 0 (Automatic based on SIC, MAXLAG=1)

t-Statistic Prob.*


5% level -2.97185310% level -2.625121


Null Hypothesis: D(PG) has a unit rootExogenous: ConstantLag Length: 1 (Automatic based on SIC, MAXLAG=1)

t-Statistic Prob.*


5% level -2.97626310% level -2.627420



12/21


12

Now we can see at 1 st difference trend is remove from GDP .In a similar way we will show you 1 St difference of all the variable to confirm that trend doesnt exist at 1 st difference also and our data isStationary and best fit to run OLS( spurious regression).

COINTEGRATION TEST

This test is use to predict long term relationship among variables when data is Time series.To run testthere are two ways to run cointegration in e-views

1. Quick Group statistics gdp c inf expo pg summary lag 1 1Enter 2. Open variables as group than go to View Cointegration test summary lag 1 1Enter

Date: 07/29/12 Time: 00:13Sample: 1981 2010Included observations: 28Series: GDP INF PG EXPOLags interval: 1 to 1

Selected(0.05 level*)Number of

Cointegrating Relationsby Model

Data Trend: None None Linear Linear QuadraticTest Type No Intercept Intercept Intercept Intercept Intercept

No Trend No Trend No Trend Trend TrendTrace 1 1 1 2 2

Max-Eig 0 1 1 2 2

*Critical values based on MacKinnon-Haug-Michelis (1999)

InformationCriteria byRank and

Model

Data Trend: None None Linear Linear QuadraticRank or No Intercept Intercept Intercept Intercept Intercept

No. of CEs No Trend No Trend No Trend Trend Trend

LogLikelihood

by Rank (rows) and

Model(columns)

0 -306.6708 -306.6708 -300.1670 -300.1670 -298.9645


13/21


13

1 -294.7700 -285.0019 -278.9830 -274.9595 -273.99472 -288.2346 -276.1614 -273.1853 -259.3250 -258.68033 -285.1221 -270.4189 -268.2365 -254.2864 -253.96704 -284.9403 -268.1617 -268.1617 -251.7777 -251.7777

Akaike

InformationCriteria byRank (rows)and Model(columns)

0 23.04792 23.04792 22.86907 22.86907 23.068891 22.76929 22.14300 21.92736 21.71139 21.856772 22.87390 22.15439 22.08466 21.23750* 21.334313 23.22301 22.38706 22.30260 21.52046 21.569074 23.78145 22.86869 22.86869 21.98412 21.98412

SchwarzCriteria by

Rank (rows)and Model(columns)

0 23.80918 23.80918 23.82065 23.82065 24.210781 23.91118 23.33246 23.25956 23.09118 23.379292 24.39642 23.77206 23.79750 23.04549* 23.237463 25.12616 24.43295 24.39607 23.75666 23.852854 26.06523 25.34279 25.34279 24.64853 24.64853

Granger Causality Tests

This test is used to check the causal effects of variable.


14/21


14

Quick click Group statistics write gdp c inf expo pg click summary write lag 1 Press Enter

Pairwise Granger Causality TestsDate: 07/29/12 Time: 00:23Sample: 1981 2010Lags: 5

Null Hypothesis: Obs F-Statistic Prob.

INF does not Granger Cause GDP 25 2.06361 0.1312GDP does not Granger Cause INF 0.36433 0.8645

PG does not Granger Cause GDP 25 0.57561 0.7180GDP does not Granger Cause PG 1.73074 0.1923

EXPO does not Granger Cause GDP 25 0.75964 0.5934GDP does not Granger Cause EXPO 0.55349 0.7336

PG does not Granger Cause INF 25 1.05696 0.4241INF does not Granger Cause PG 1.67334 0.2056

EXPO does not Granger Cause INF 25 0.47452 0.7893INF does not Granger Cause EXPO 2.14127 0.1203

EXPO does not Granger Cause PG 25 0.46824 0.7937PG does not Granger Cause EXPO 1.13597 0.3868

Granger causality tests are used to check the cause and effect in the data.

FORECASTING

In simple forecasting means prediction of future value with the help of trend or available of seasonal orannual data. Forecasting which I am going to predict in my model is of two types.

IN SAMPLE FORECASTINGIn in-sample forecasting we compare few samples from our available data with remaining samples. Wesimply reduce our data size and then forecast and compare our result with actual. In sample forecastingwe forecast our Dependent Variable..If forecasting is done through Dependent variable is known astrend forecasting. If we predicting in tine series data than R 2 Should be greater for better prediction.

TO RUN IN SAMPLE FORECASTING


15/21


15

Go to Quick click Estimate equationEnter Forecast(on tab window)Forecaste varaible(GDPF )Enter

Now we have the output we see value of Theil Inequality to predict either our model is fit for in sampleforecasting or not. Values( 0 to 1) if it is nearer to 0 we say our model is good for forecasting here youcan see value of Theil Inequality is 0.04. Our model is good for forecasting.

OUT SAMPLE FORECASTING

Dependent Variable: GDPMethod: Least SquaresDate: 07/29/12 Time: 01:37Sample (adjusted): 1981 2005Included observations: 25 after adjustments


INF -0.112292 0.129049 -0.870155 0.3936EXPO 0.000186 9.62E-05 1.935329 0.0659

PG 1.825652 0.362685 5.033711 0.0000

R-squared 0.126916 Mean dependent var 5.276000Adjusted R-squared 0.047545 S.D. dependent var 2.020784S.E. of regression 1.972160 Akaike info criterion 4.308302Sum squared resid 85.56711 Schwarz criterion 4.454567

0

2

4

6

8

10

12

82 84 86 88 90 92 94 96 98 00 02 04

GDPF 2 S.E.

Forecast: GDPF Actual: GDPForecast sample: 1981 2005Included observations: 25

Root Mean Squared Error 1.850050Mean Absolute Error 1.520412Mean Abs. Percent Error 38.85766Theil Inequality Coefficient 0.168828

Bias Proportion 0.000190Variance Proportion 0.659586Covariance Proportion 0.340224


16/21


16

Log likelihood -50.85378 Hannan-Quinn criter. 4.348870Durbin-Watson stat 1.400444

PANELED /POOLED DATA

FIXED EFFECT MODEL

Dependent Variable: INFMethod: Panel Least SquaresDate: 07/29/12 Time: 04:54Sample: 1981 2000Periods included: 20Cross-sections included: 2Total panel (balanced) observations: 40


C 10.66587 1.316850 8.099534 0.0000GDP -0.000443 0.000290 -1.524657 0.1447

Effects Specification

Cross-section fixed (dummy variables)Period fixed (dummy variables)

R-squared 0.650929 Mean dependent var 8.736000Adjusted R-squared 0.243680 S.D. dependent var 2.641298S.E. of regression 2.297048 Akaike info criterion 4.802619

0

2

4

6

8

10

12

82 84 86 88 90 92 94 96 98 00 02 04

GDPF 2 S.E.

Forecast: GDPF Actual: GDPForecast sample: 1981 2015

Adjusted sample: 1981 2005Included observations: 25

Root Mean Squared Error 1.850050Mean Absolute Error 1.520412Mean Abs. Percent Error 38.85766Theil Inequality Coefficient 0.168828

Bias Proportion 0.000190Variance Proportion 0.659586Covariance Proportion 0.340224


17/21


17

Sum squared resid 94.97576 Schwarz criterion 5.731503Log likelihood -74.05239 Hannan-Quinn criter. 5.138474F-statistic 1.598357 Durbin-Watson stat 2.217281Prob(F-statistic) 0.159380

RANDOM EFFECT MODEL

Dependent Variable: INFMethod: Panel EGLS (Two-way random effects)Date: 07/29/12 Time: 05:39Sample: 1971 2000Periods included: 30

Cross-sections included: 3Total panel (balanced) observations: 90Swamy and Arora estimator of component variances


C 8.056929 1.245254 6.470110 0.0000GDP 0.000256 0.000197 1.300911 0.1967

Effects SpecificationS.D. Rho

Cross-section random 1.389398 0.0674Period random 3.826692 0.5111Idiosyncratic random 3.475606 0.4216

hausman test

Correlated Random Effects - Hausman TestEquation: EQ03Test cross-section and period random effects

Test SummaryChi-Sq.Statistic Chi-Sq. d.f. Prob.

Cross-section random 0.988866 1 0.3200Period random 3.840737 1 0.0500Cross-section and period random 3.398207 1 0.0653

Cross-section random effects test comparisons:


18/21


18

Variable Fixed Random Var(Diff.) Prob.

GDP 0.000419 0.000256 0.000000 0.3200

Factor Analysis

Factor analysis is an interdependency technique whose primary objective is to define the undelyingstructure among the variables in the analysis. There are two types of Factor analysis

Exploretry factor Analysis : It is the type of data in which variables are not defined and extracted afterdata reduction.

Confirmatory factor Analysis : It is type of data analysis in which variables are defined .

Now we will run Factor analysis on SPSS

Collinearity Diagnosticsa

ModelDimension Eigenvalue

ConditionIndex

Variance Proportions

(Constant) Work Supervision Co-workers Promotion

1 1 4.731 1.000 .00 .00 .00 .00 .00

2 .134 5.949 .01 .32 .00 .32 .03

3 .055 9.237 .17 .63 .18 .41 .04

4 .045 10.253 .63 .04 .06 .15 .44

5 .034 11.720 .18 .00 .76 .11 .49

a. Dependent Variable: Pay


19/21


19

KMO and Bartlett's Test

Kaiser-Meyer-Olkin Measure of Sampling Adequacy. .662

Bartlett's Test of Sphericity Approx. Chi-Square 60.522

df 10

Sig. .000

Now you can see value of KMO and Barletts is significant it means data is reliable and we can Run

factor on it.

Communalities

Initial Extraction

pay 1.000 .467

work 1.000 .653

supervision 1.000 .656

Coworkers 1.000 .826

Promotion 1.000 .799

Extraction Method: PrincipalComponent Analysis.


20/21


20

Component Matrix a

Component

1 2

Promotion .857

supervision .798

Coworkers .673 -.611

pay .581 .360

work .480 .650

Extraction Method: Principal

Component Analysis.

a. 2 components extracted.

Rotated Component Matrix a

Total Variance Explained

Component

Initial EigenvaluesExtraction Sums of Squared

LoadingsRotation Sums of Squared

Loadings

Total % of Variance

Cumulative % Total

% of Variance

Cumulative % Total

% of Variance

Cumulative %

1 2.392 47.832 47.832 2.392 47.832 47.832 1.828 36.560 36.560

2 1.010 20.203 68.035 1.010 20.203 68.035 1.574 31.475 68.035

3 .814 16.290 84.325

4 .479 9.588 93.912

5 .304 6.088 100.000

Extraction Method: Principal Component Analysis.


21/21


21

Component

1 2

Coworkers .908

Promotion .822 .351

work .807

pay .648

supervision .527 .615

Extraction Method: Principal

Component Analysis.Rotation Method: Varimax with

Kaiser Normalization.

a. Rotation converged in 3

iterations.

Component Transformation

Matrix

Component 1 2

1 .769 .639

2 -.639 .769

Extraction Method: PrincipalComponent Analysis.

Rotation Method: Varimaxwith Kaiser Normalization.

Date post:	04-Apr-2018
Category:	Documents
Upload:	umair-ashraf
View:	215 times
Download:	0 times

GDP INF EXPO PG 1

Documents