Research Question 
: 
What are the factors that influence the economic performance of a company? Economic performance is measured by the return on capital employed. 
Methodology 
: 
Multiple Regression Analysis 
Dataset 
: 
FIN.DAT 
$RUN REGRESSN $FILES PRINT = FIN2.LST DICTIN = FIN.DIC DATAIN = FIN.DAT $SETUP MULTIPLE REGRESSION ANALYSIS BADDATA=MD1  MDHANDLING=20  PRINT =(DICT,MATRIX) METHOD =STANDARD  DEPVAR=V2 VARS=(V3V14) PARTIALS = (V4,V6) 
After filtering 40 cases read from the input data file Number of cases = 40 


General statistics Variable Standard Range Number Sum Average Deviation Max Min Variable name 3 1220.00000 30.50000 22.94028 91.0000 .0000 GEARRAT 4 6107.00000 152.67500 83.14257 346.0000 .0000 CAPINT 5 1501.00000 37.52500 99.04415 625.0000 32.0000 WCFTDT 6 17400.00000 435.00000 101.31469 629.0000 .0000 LOGSALR 7 17481.00000 437.02500 57.28762 625.0000 341.0000 LOGASST 8 7241.00000 181.02500 194.75408 1298.0000 29.0000 CURRAT 9 4761.00000 119.02500 203.46316 1298.0000 14.0000 QUIKRAT 10 1336.00000 33.40000 19.63879 94.0000 .0000 NFATAST 11 1062.00000 26.55000 17.27597 74.0000 .0000 INVTAST 12 2087.00000 52.17500 27.45522 110.0000 .0000 FATTOT 13 1283.00000 32.07500 31.34865 183.0000 .0000 PAYOUT 14 1029.00000 25.72500 31.16045 147.0000 58.0000 WCFTCL 2 573.00000 14.32500 13.47816 57.0000 18.0000 RETCAO Total correlation matrix, R (i , j)


1 Analysis 1 Standard regression dependent variable is V2 RETCAP The partial correlation matrix


Partial

IDAMS reports analysis specifications: 


Descriptive statistics of predictor variables Matrix of correlation coefficients between the variables Matrix of second order partial correlation coefficients; Variables V4 and V6 held constant. 



The most striking feature of the analysis is the extremely low value of the determinant of the correlation matrix: .79478E0.5, which is almost close to zero. This implies multicollinearity. Covariance ratio of a variable is the square of multiple correlation coefficient, R^{2}, with other p1 predictor variables in the equation. It is a measure of intercorrelation. Variables, which contribute to multicollinearity can be identified from the values of Covariance Ratio. Covariance ratios of several predictor variables are particularly high: LOGSALE: 0.8927 The standard error of the dependent variable (7.371) is about one half of the mean, implying that the reliability of prediction by the multiple regression model. is poor. The regression model explains 70% of the adjusted variance of the dependant variable. As we shall see in example EX5(2), regression model with just three variables in the equation explains 80% of the adjusted variance in the dependent variable. The F ratio of the fullscale model, though statistically significant, is much less that for the reduced model (See Example:{EX5 (2) }.
In view of high multicollinerity, there is hardly any point in interpreting the statistical significance of individual predictors. 
Research Question 
: 
What are the factors that influence the economic performance of a company? Economic performance is measured by the return on capital employed 
Methodology 
: 
Stepwise Multiple Regression Analysis 
Dataset 
: 
FIN.DAT 
$RUN REGRESSN $FILES PRINT = FINANCE.LST DICTIN = FIN..DIC DATAIN = FIN.DAT $SETUP MULTIPLE REGRESSION ANALYSIS BADDATA=MD1  MDHANDLING=20  PRINT=(DICT,MATRIX) METHOD=STEP  DEPVAR=V2  VARS=(V3V14)  FINRATIO=4.0  FOUTRATIO=3.9  PRINT=STEP
After filtering 40 cases read from the input data file 1 Number of cases = 40 General statistics Variable Standard Range Number Sum Average Deviation Max Min Variable name 3 6.51000 .16275 .29387 .6300 1.2800 WCFTCl 4 5.50000 .13750 .26862 .6000 1.2800 WCFTDT 5 186.62000 4.66550 .56091 5.7600 3.8500 LOGSALE 6 178.48000 4.46200 .54956 5.7800 3.5400 LOGASST 7 12.18000 .30450 .29506 1.7800 .0000 GEARRAT 8 72.61000 1.81525 .96339 5.4400 .3600 CAPINT 9 12.48000 .31200 .16801 .7200 .0400 NFATAST 10 18.64000 .46600 .24250 1.1600 .0700 FATTOT 11 10.31000 .25775 .12751 .5000 .0000 INVTAST 12 20.99000 .52475 .67732 4.2100 .0000 PAYOUT 13 32.78000 .81950 .43338 2.6300 .2400 QUIKRAT 14 56.81000 1.42025 .57876 3.9800 .5400 CURRAT 2 5.89000 .14725 .13832 .3800 .5000 RETCAP Total correlation matrix,R(i,j)



Step No. 0
Variable numbers 3 4 5 6 7 8 9 10 11 12 13 14 2


Step No 1 Variable entered 4 WCFTDT Flevel 97.610 Tlevel 9.880
Partial
**************** Listing of marginal Rsquares for all potential predictors ***


Step No 2 Variable entered 9 NFATAST Flevel 11.513 Tlevel 3.393
Partial
**************** Listing of marginal Rsquares for all potential predictors ***


Step no 3 Variable entered 14 CURRAT Flevel 6.367 Tlevel 2.523
Partial
**************** Listing of marginal Rsquares for all potential predictors ***
Completed 3 steps of regression 
IDAMS reports analysis specifications:



Step No. 0 No variables in the model. Correlation matrix shows that V_{4 }(WCFTDT) has the highest correlation with the dependent variable (0.8480); Marginal RSQD (0.7198) Hence V_{4} is entered at Step 1. 

Step No. 1


Step No. 2


Step No. 3
Adequacy of the fitted model
Standard error of the estimate of the dependent variable = .06135 which is quite low Þ high reliability of estimation. Determinant of the correlation matrix = .77647, Value close to 0 Þ Multicolinearity Recall that for the full scale model the determinants of the correlation matrix was .79478E05, which is close to 0 Þ high multicollinearity. The standard error of the estimate is now much less than that for the full  scale model:

Research Question 
: 
What is the influence of institutional setting and rank of academic scientists on percentage of work time devoted to teaching? 
Methodology 
: 
Multiple Classification Analysis 
Dataset 
: 
ANJU. DAT 
$RUN MCA $FILES PRINT = MCA.LST DICTIN = ANJU.DIC DATAIN = ANJU.DAT $SETUP INCLUDE V9 = 13 TIME SPENT ON TEACHING BADDATA=MD1  PRINT=CDICT DEPVAR=V2 CONVARS=(V9,V14) PRINT=TABLES DEPVAR=V2 CONVARS=(V9,V14) OUTLIERS=EXCL, OUTDIS=2.0
After filtering 1055 cases read from the input data file Dependent variable



1 Weighted frequency table # 1
1Results based on test 2 Iteration 3 

Dependent variable statistics Dependent variable (y) = 2: v262:teaching 

Predictor 9: v204:rank Unadjusted
Etasquare = .10896630 Betasquare = .53927850E01
Etasquare(adj)= .10724440
Unadjusted deviation SS = 37045.990 Predictor 14: sv:inst type Unadjusted
Etasquare = .16380060 Betasquare = .11275420
Etasquare(adj)= .16137450
Unadjusted deviation SS = 55688.370 Adjusted deviation SS = 38333.800 

1 Analysis summary statistics Rsquared (unadjusted) = proportion of variation explained by fitted
model = .21208 Listing of Betas in descending order

IDAMS reports analysis specifications:
a = Parameters for convergence Variables:



Bivariate frequency table 

Dependent variable statistics 

Predictor Summary Statistics


Summary Statistics R^{2} unadjusted indicates that approximately 21% of the
variance in the dependent variable is explained by the fitted model. There is not much difference between the values of the unadjusted and adjusted R^{2}, which implies that there is hardly any interaction between the two variable categories. The predictors are ranked by Beta values, which show relative importance of the two predictors. Institutional setting of an academic scientist seems to have greater effect on the time spent by an academic scientist than his rank. 
1 Analysis  2 TIME SPENT ON TEACHING



Dependent variable statistics Dependent variable (y) = 2: v262:teaching 

1 Predictor summary statistics Predictor 9: v204:rank Unadjusted
Etasquare = .11281920 Betasquare = .62690240E01
Etasquare(adj)= .11101050
Unadjusted deviation SS = 27710.290 Predictor 14: sv:inst type Unadjusted
Etasquare = .15333510 Betasquare = .10420150
Etasquare(adj)= .15074330
Unadjusted deviation SS = 37661.680 

1 Analysis summary statistics ***Multiple R (adjusted) = .45344 Multiple Rsquared (adjusted) = .20561 Listing of Betas in descending order
***Multiple R (adjusted) = .45344 Multiple Rsquared (adjusted) = .20561 
In this analysis outlier cases have been eliminated.
a ‘Parameters’, for convergence



Dependent variable statistics 

Predictor Summary Statistics Coefficient = Deviation of the class mean from the grand mean after holding constant the other predictors (in this case V14 ) Adjusted mean = Grand mean + Coefficient


Summary Statistics There is not much difference between the values of the unadjusted and adjusted R^{2} which implies that there is hardly any interaction between the two variable categories. The predictors are ranked by Beta values, which show relative importance of the two predictors. Institutional setting of an academic scientist seems to have greater effect on the time spent by an academic scientist than his rank. 