Comparing free statistical software
For data sets with no missing values

Click here to return to the free software page

This page shows some output from the programs listed below. The output is correlations and regression. I did this in November 2006 using the most recent versions of the software at that time. I used a dataset with 4 variables, a subset from PD-Plus, available on my data page.

Easyreg   http://econ.la.psu.edu/~hbierens/EASYREG.HTM
Epidata     http://www.epidata.dk/
Instat    http://www.ssc.rdg.ac.uk/software/instat/instat.html 
Stat4U, replaced by openstat   http://www.statpages.org/miller/openstat/   which I haven't reviewed yet.
WinIDAMS   http://www.unesco.org/webworld/idams/  
MicrOsiris  http://www.microsiris.com/
Epi Info 2000 Windows    http://www.cdc.gov/epiinfo/index.htm


Using a data set with all cases (no missing values):
http://gsociology.icaap.org/methods/fourvars.csv

1. All programs read .csv files, except epi info, which imports excell files, among other formats.


2. When using MicrOsiris, import the .csv file, then call up commands.


3. When using WinIDAMS, all values of each variable should have the same number of decimal places. So you need to open the file above to excel, format each variable to, say, 2 decimal places. Also, WinIDAMS can't handle variables with more than 10 digits.


4. Stat4U seems to have a problem when there is too much variation in a variable, like population varying from a billion to a thousand.


5. When using regression in epistat, make the dependent variable the first in the list.


6. Correlation: all programs give exactly the same correlation coefficients.


7. Regression: all programs give the same results for basic regression and some same results for backward/forward stepwise.

Misc notes:

1. Epi info doesn't have a menu command for getting means of multiple variables, can only seem to get means for one variable at a time.



Who uses these packages?
Return to top

This is a sample of papers that use these packages. Many sites link to them as well. I just list some places that use EasyReg, Stat4U, EpiData, Instat, Irristat, MicrOrsiris, as the other programs (WinIDAMS, EpiInfo) are from major institutions (UNESCO, CDC) so are pretty well used.

EasyReg

Spatial and Temporal Transferability of Trip Generation Demand Models in Israel, A Cotrus, J Prashker, Y Shiftan
Journal of Transportation and Statistics,
http://www.bts.gov/publications/journal_of_transportation_and_statistics/volume_08_number_01/html/paper_04/
mentions using Easyreg when calculating R squared, in table 10.

On the relationship between the market risk premium and the risk-free interest rate. Confidence W. Amadi
A Journal of Applied Topics in Business and Economics, 2004
http://www.westga.edu/~bquest/2004/relationship.htm

Cushman, David O., (2003) "Further evidence on the size and power of the Bierens and Johansen cointegration
procedures." Economics Bulletin, Vol. 3, No. 25 pp. 1−7
http://economicsbulletin.vanderbilt.edu/Abstract.asp?PaperID=EB-03C30001

Predicting Financial Time Series by Genetic Programming with Trigonometric Functions and High-Order Statistics
R. Schwaerzel and T Bylander, http://www.cs.utsa.edu/~rschwaer/AppliedSoftComputing.pdf

Assessing the Impact of the September 11 Terrorist Attacks on U.S. Airline Demand
H Ito and D Lee, http://www.brown.edu/Departments/Economics/Papers/2003/2003-16_paper.pdf


Stat4U (or previous version, OpenStat)

Differential gene expression patterns in cyclooxygenase-1 and cyclooxygenase-2 deficient mouse brain
Christopher D Toscano, Vinaykumar V Prabhu, Robert Langenbach, Kevin G Becker, and Francesca Bosetti
Genome Biol. 2007; 8(1): R14.
http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=1839133   Uses OpenStat

Cytolethal Distending Toxin from Shiga Toxin-Producing Escherichia coli O157 Causes Irreversible G2/M Arrest, Inhibition of Proliferation, and Death of Human Endothelial Cells. Bielaszewska et al. Infection and immunity, Jan. 2005, p. 552–562 Vol. 73, No. 1
http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=538959
and
Chromosomal Dynamism in Progeny of Outbreak-Related Sorbitol-Fermenting Enterohemorrhagic Escherichia coli O157:NM, Bielaszewska et al, Appl Environ Microbiol. 2006 March; 72(3): 1900–1909. http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=1393231 
Both journals are from the American Society for Microbiology, and both use OpenStat.

Endothelium histological integrity after skeletonized dissection of the left internal mammary artery with ultrasonic scalpel. Cañadas et al, Interact CardioVasc Thorac Surg 2005;4:160-162.  http://icvts.ctsnetjournals.org/cgi/content/full/4/3/160    from the European Association of Cardio-Thoracic Surgery.  Uses OpenStat.

Stat4U has a nice brief mention in this article "Software you can use", in the Sept 2006 Newsletter of the Society for Judgment and Decision Making, http://www.sjdm.org/content/newsletters

Are interleukin-16 and thrombopoietin new tools for the in vitro generation of dendritic cells? Bella et al, Blood, 15 December 2004, Vol. 104, No. 13, pp. 4020-4028.  http://bloodjournal.hematologylibrary.org/cgi/content/full/104/13/4020   A journal of the American Society of Hematology. Uses OpenStat.


Epidata

Epidata was funded at least in part by WHO   http://www.who.int/tdr/publications/tdrnews/news67/epidata.htm

Epidata is also listed in a CDC MMWR report
http://www.cdc.gov/mmwr/preview/mmwrhtml/su5501a6.htm   
and here
Houston JM, Martin M, Williams JE, Hill RL. The Annual African American Conference on Diabetes: evolving program evaluation with evolving program implementation. Prev Chronic Dis [serial online] 2006 Jan [date cited]. Available from: URL: http://www.cdc.gov/pcd/issues/2006/jan/05_0119.htm 


Instat
Return to top

The relationship between pyrethrins and the yellow pigmentation in pyrethrum flowers. Wenwa A. Odinga and Charles A. Angedu. 2003. African Journal of Science and Technology (AJST), Science and Engineering Series Vol. 4, No. 2, pp. 116-123.   http://www.ansti.org/volume/Odinga-final.pdf   This journal is from the African Network of Scientific and Technological Institutions, part of UNESCO and UNDP.

A STAT5 modifier locus on murine chromosome 7 modulates engraftment of hematopoietic stem cells during steady-state hematopoiesis
Christine Couldrey, Heath L. Bradley, and Kevin D. Bunting.  Blood, 15 February 2005, Vol. 105, No. 4, pp. 1476-1483. http://bloodjournal.hematologylibrary.org/cgi/content/full/105/4/1476

Metabotropic Glutamate Receptors and Dopamine Receptors Cooperate to Enhance Extracellular Signal-Regulated Kinase Phosphorylation in Striatal Neurons. Voulalas et al. The Journal of Neuroscience, April 13, 2005, 25(15):3763-3773.  http://www.jneurosci.org/cgi/content/full/25/15/3763

Association of the XRCC1 gene polymorphisms with cancer risk in Turkish breast cancer patients. Deligezer and Dalay. Experimental and molecular medicine, Vol. 36, No. 6, 572-575, December 2004. http://www.e-emm.org/article/article_files/EMM036-06-10.pdf   This journal is published by  The Korean Society of Medical Biochemistry and Molecular Biology.


Irristat

I haven't used it yet, but IRRISTAT is used here

FAO Plant Production and Protection Paper No. 174, Rome, 2003, Genotype x environment interactions. Challenges and opportunities for plant breeding and cultivar recommendations,  listed here  http://www.fao.org/catalog/bullettin/07_03.htm
The report is here    http://www.fao.org/DOCREP/005/Y4391E/y4391e00.htm   and Irristat is listed in the forward and the following pages.
http://www.fao.org/docrep/005/Y4391E/y4391e0b.htm, http://www.fao.org/DOCREP/005/Y4391E/y4391e08.htm


Yield and Soil Nutrient Changes in a Long-Term Rice-Wheat Rotation in India
A. L. Bhandari, J. K. Ladha, H. Pathak, A. T. Padre, D. Dawe and R. K. Gupta
Soil Science Society of America Journal 66:162-170 (2002)
http://soil.scijournals.org/cgi/content/full/66/1/162

Effects of Residue Decomposition on Productivity and Soil Fertility in Rice–Wheat Rotation
Yadvinder-Singh, Bijay-Singh, J. K. Ladha, C. S. Khind, T. S. Khera and C. S. Bueno
Soil Sci. Soc. Am. J. 68:854-864 (2004).
http://soil.scijournals.org/cgi/content/full/68/3/854

An agro-economic analysis of grain production in Estonia after its transition to market economy
N. Vasiliev, A. Astover, H. Roostalu, E. Matveev
Agronomy Research, Vol 4 (1)
http://www.eau.ee/~agronomy/

Analysis of Magnaporthe grisea population structure in Côte d’Ivoire as a prerequisite for the deployment of varieties with durable blast resistance.
Y. Séré et al, in Department for International Development-Crop Protection Programme (DFID-CPP) funded project - Rice blast in West Africa workshop papers
http://www2.warwick.ac.uk/fac/sci/whri/about/staff/sprasad/


MicrOsiris

History and Potential of Binary Segmentation for Exploratory Data Analysis. James N. Morgan.
Journal of Data Science, v.3, no.2, 123-136
http://www.sinica.edu.tw/~jds/A198.html   

Part of WinIDAMS has sources from MicrOsiris
http://www.unesco.org/webworld/idams/newsletter_sep.html

CDC mentions that one state data system was converted to be available for MicrOsiris
http://ftp.cdc.gov/pub/Software/RegistryPlus/Inventory_Assessment/Assessment2005.xls
and here http://www.cdc.gov/cancer/npcr/npcrpdfs/assessment2005.pdf

The decision tree is mentioned here
http://onlineacademics.org/CA517/Practitioner/Unit5CorrelationPractitioner.ppt
and here
http://www.thoracic.org/sections/clinical-information/best-of-the-web/pages/research/choosing-the-best-statistical-test.html
   as an important site


PAST – Palaeontological Statistics
http://folk.uio.no/ohammer/past/
(I haven't tried this out yet, but this seems like a well used program)

Genetic variation in the threatened medicinal tree prusus africana in Cameroon and Kenya. Alice Muchugi, Ard G. Lengkeek, Caroline A.C. Agufa, Geoffrey M. Muluvi, Eliud N.M. Njagi and Ian K. Dawson. FAO International workshop, 5-7 March 2005, Turin, Italy.
http://www.fao.org/biotech/torino05.htm

Is appropriate appropriate? An investigation of interpersonal semantic stability
H.P.L. Molloy    Temple University Japan
Proceedings of the 2nd Annual JALT Pan-SIG Conference.   May 10-11, 2003. Kyoto, Japan: Kyoto Institute of Technology.
http://jalt.org/pansig/2003/HTML/Molloy.htm

Seriation in Paleontological Data Using Markov Chain Monte Carlo Methods
Kai Puolamäki, Mikael Fortelius, and Heikki Mannila
Computational Biology, 2006 February; 2(2): e6
http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=1361357

Lipid composition of sesame seeds (Sesamum indicum L.) using multivariate analysis
Auristela Malavé Acuña y Jesús Rafael Méndez Natera
Bioline International, Revista Científica UDO Agrícola Vol. 5, Núm. 1, 2005, pp. 48-53
http://www.bioline.org.br/request?cg05006



Just Correlations
Return to top

*****************
EPIDATA
*****************

        free00    clit    gdpcap00 imr2000
free00        1.000               
clit         -0.425    1.000          
gdpcap00     -0.483    0.416    1.000     
imr2000       0.524   -0.758   -0.519    1.000

*****************
INSTAT PLUS
*****************

       imr2000    gdpcap0    clit       free00
imr2000    1.0000
gdpcap0   -0.5190    1.0000
clit      -0.7580    0.4158     1.0000
free00     0.5236   -0.4827    -0.4252    1.0000

*****************
STAT4U
*****************

             Correlations
             imr2000   gdpcap00       clit     free00
imr2000         1.000     -0.519     -0.758      0.524
gdpcap00       -0.519      1.000      0.416     -0.483
clit           -0.758      0.416      1.000     -0.425
free00          0.524     -0.483     -0.425      1.000


*****************
WINIDAMS
*****************
                           VAR      2        3        4

3 gdpcap00                   3  -0.5190
4 clit                       4  -0.7580   0.4158
5 free00                     5   0.5236  -0.4827  -0.4252

*****************
EASYREG
*****************

Sample correlation matrix
            1 -0.5189745339 -0.7579766302  0.5235762816
-0.5189745339             1  0.4158181979 -0.4827020603
-0.7579766302  0.4158181979             1 -0.4252419584
 0.5235762816 -0.4827020603 -0.4252419584

*****************
MicrOsiris
*****************

**CORRELATIONS**
 
                  V2       V3       V4
             imr2000 gdpcap00     clit
gdpcap00 V3  -0.5190
clit     V4  -0.7580   0.4158
free00   V5   0.5236  -0.4827  -0.4252


Regressions
Return to top

Using literacy rate, gdp per capita and infant mortality rate to predict freedom.

*****************
EPIDATA
*****************

        free00    clit    gdpcap00 imr2000
free00        1.000               
clit        -0.425    1.000          
gdpcap00    -0.483    0.416    1.000     
imr2000     0.524    -0.758    -0.519    1.000

Source        Sum Sq    Mean Sq    df         Number of obs    172
Model        228.49    76.16    3         F(3,168)    28.36
Residual     451.18    2.69    168         Prob > F    0.00
Total        679.67    3.97    171         R-squared    0.34
                         Root MSE    1.64
                               
Variable    Beta    LCL    UCL    SE    t    P>|t|
imr2000      0.02    0.01    0.03    0.01    3.27    0.00
gdpcap00    -0.05   -0.08   -0.03    0.01   -3.90    0.00
clit        -0.01   -0.02    0.01    0.01   -0.53    0.59
Intercept    3.59    1.70    5.49    0.96    3.75    0.00
Total N = 172 Included: N= 172

****
JUST IMR AND GDPCAP

Source        Sum Sq    Mean Sq    df         Number of obs    172
Model        227.72    113.86    2         F(2,169)    42.58
Residual     451.94    2.67    169         Prob > F    0.00
Total        679.67    3.97    171         R-squared    0.34
                         Root MSE    1.64
                               
Variable    Beta    LCL    UCL    SE    t    P>|t|
imr2000      0.02    0.01    0.03    0.00    5.09    0.00
gdpcap00    -0.05   -0.08   -0.03    0.01   -3.93    0.00
Intercept    3.10    2.59    3.61    0.26   11.98    0.00
Total N = 172 Included: N= 172

*****************
INSTAT PLUS
*****************

       imr2000    gdpcap0    clit       free00
imr2000    1.0000
gdpcap0   -0.5190     1.0000
clit      -0.7580     0.4158     1.0000
free00     0.5236    -0.4827    -0.4252    1.0000

ANOVA for regression of free00
on imr2000 gdpcap0 clit
-------------------------------------------------------------------
Source      df            SS            MS      F value     Prob>F
-------------------------------------------------------------------
Regression   3       228.492        76.164        28.36     0.0000
Residual   168       451.176        2.6856
-------------------------------------------------------------------
Total      171       679.667
-------------------------------------------------------------------

R-squared = 0.3362  (adjusted = 0.3243)


*****************
STAT4U
*****************
Return to top
             Correlations
             imr2000   gdpcap00       clit     free00
imr2000         1.000     -0.519     -0.758      0.524
gdpcap00       -0.519      1.000      0.416     -0.483
clit           -0.758      0.416      1.000     -0.425
free00          0.524     -0.483     -0.425      1.000
No. of valid cases = 172

BEST FIT REGRESSION
Variables entered in step  3
 1 imr2000
 2 gdpcap00
 3 clit

Squared Multiple Correlation = 0.3362
Dependent variable = free00
ANOVA for Regression Effects :
SOURCE      df           SS           MS            F             Prob
Regression   3       228.4915        76.1638        28.3604         0.0000
Residual   168       451.1756         2.6856
Total      171       679.6672

Variables in the equation
VARIABLE            b        s.e. b    Beta    t    prob. t
         imr2000    0.01698   0.0052   0.3355  3.269 0.0013
        gdpcap00   -0.05281   0.0135  -0.2872 -3.902 0.0001
            clit   -0.00503   0.0094  -0.0516 -0.535 0.5936
(Intercept)         3.59431

Increase in squared R for this step = 0.001129
F =   0.2858 with D.F. 1 and 168 with Probability = 0.5936
----------------------------------------------------------

Stat4U BACKWARD STEPWISE

----------------- STEP 3 ------------------
Determinant of correlation matrix =   0.7259

SOURCE    DF        SS      MS        F        Prob.>F
Regression   1   186.319   186.319    64.202     0.000
Residual   170   493.349     2.902
Total      171   679.667

Dependent Variable: free00

       R        R2         F     Prob.>F  DF1  DF2
   0.524     0.274    64.202     0.000    1  170
Adjusted R Squared = 0.270

Std. Error of Estimate =      1.704

Variable       Beta      B         Std.Error t         Prob.>t   VIF       TOL
   imr2000     0.524     0.026     0.003     8.013     0.000     1.000     1.000

Constant =      2.407

Partial Correlations

Variables    imr2000 
                0.524 
-----------------------------------------
Stat4U FORWARD STEPWISE
-----------------------------------------

-------------FINAL STEP-----------
SOURCE    DF        SS      MS        F        Prob.>F
Regression   2   227.724   113.862    42.578     0.000
Residual   169   451.943     2.674
Total      171   679.667

Dependent Variable: free00

       R        R2         F     Prob.>F  DF1  DF2
   0.579     0.335    42.578     0.000    2  169
Adjusted R Squared = 0.327

Std. Error of Estimate =      1.635

Variable       Beta      B         Std.Error t         Prob.>t   VIF       TOL
   imr2000     0.374     0.019     0.004     5.093     0.000     1.369     0.731
  gdpcap00    -0.289    -0.053     0.013    -3.935     0.000     1.369     0.731

Constant =      3.101


*****************
WINIDAMS STEPWISE (I think this is best fit)
*****************
Return to top

      F-level            0.286
      T-level            0.535


          Standard error of estimate                 1.639   
          F ratio for the regression                28.360
          Multiple correlation coefficient         0.57981          adjusted        0.56950
          Fraction of explained variance (RSQD)    0.33618          adjusted        0.32433
          Determinant of the correlation matrix    0.31037   
          Residual degrees of freedom (N-p-1)          168
          Constant term                             3.5943   

                                                            Partial
  Var. no.        B       Sigma(B)     Beta    Sigma(Beta)   RSQD     Marg RSQD  T-ratio  Cov. ratio  Variable name
     2          0.0170     0.0052     0.3355     0.1026     0.0598     0.0422     3.2692     0.6247  imr2000                     3         -0.0528     0.0135    -0.2872     0.0736     0.0831     0.0602     3.9018     0.2705  gdpcap00                    4         -0.0050     0.0094    -0.0516     0.0964     0.0017     0.0011     0.5346     0.5752  clit                  

 **************** Listing of marginal R-squares for all potential predictors ***

    Step no.     Var. no.     Variable name              Marg rsqd     Categorical variables (all codes)        Previously

in (*)
                                                                             Marg RSQD         T-ratio

        3           2      imr2000                         0.0422                                                       *
        3           3      gdpcap00                        0.0602                                                       *
        3           4      clit                            0.0011                                                       *


*****************
WINIDAMS Decending
*****************

          Standard error of estimate                 1.639   
          F ratio for the regression                28.360
          Multiple correlation coefficient         0.57981          adjusted        0.56950
          Fraction of explained variance (RSQD)    0.33618          adjusted        0.32433
          Determinant of the correlation matrix    0.31037   
          Residual degrees of freedom (N-p-1)          168
          Constant term                             3.5943   
                                                            Partial
  Var. no.        B       Sigma(B)     Beta    Sigma(Beta)   RSQD     Marg RSQD  T-ratio  Cov. ratio  Variable name
     2          0.0170     0.0052     0.3355     0.1026     0.0598     0.0422     3.2692     0.6247  imr2000                     3         -0.0528     0.0135    -0.2872     0.0736     0.0831     0.0602     3.9018     0.2705  gdpcap00                    4         -0.0050     0.0094    -0.0516     0.0964     0.0017     0.0011     0.5346     0.5752  clit                  

 **************** Listing of marginal R-squares for all potential predictors ***

    Step no.     Var. no.     Variable name              Marg rsqd     Categorical variables (all codes)        Previously

in (*)
                                                                             Marg RSQD         T-ratio
        0           2      imr2000                         0.0422                                                       *
        0           3      gdpcap00                        0.0602                                                       *
        0           4      clit                            0.0011                                                       *



**************
Easyreg
**************
Return to top

X variables:

X(1) = imr2000
X(2) = gdpcap00
X(3) = clit
X(4) = 1

OLS estimation results
Parameters  Estimate    t-value    H.C. t-value
                         (S.E.)     (H.C. S.E.)
                      [p-value]  [H.C. p-value]
b(1)         0.01698      3.269           3.485
                      (0.00519)       (0.00487)
                      [0.00108]       [0.00049]
b(2)        -0.05281     -3.902          -5.441
                      (0.01353)       (0.00971)
                      [0.00010]       [0.00000]
b(3)        -0.00503     -0.535          -0.554
                      (0.00941)       (0.00909)
                      [0.59293]       [0.57960]
b(4)         3.59431      3.749           3.889
                      (0.95867)       (0.92417)
                      [0.00018]       [0.00010]

Effective sample size (n):                          172
Variance of the residuals:                     2.685569
Standard error of the residuals (SER):         1.638771
Residual sum of squares (RSS):               451.175601
(Also called SSR = Sum of Squared Residuals)
Total sum of squares (TSS):                  679.667151
R-square:                                        0.3362
Adjusted R-square:                               0.3243

Overall F test: F(3,168) = 28.36
p-value = 0.00000

******
EASYREG JUST USING IMR AND GDPCAP

X variables:
X(1) = imr2000
X(2) = gdpcap00
X(3) = 1

OLS estimation results
Parameters  Estimate    t-value    H.C. t-value
                         (S.E.)     (H.C. S.E.)
                      [p-value]  [H.C. p-value]
b(1)         0.01891      5.093           5.566
                      (0.00371)       (0.00340)
                      [0.00000]       [0.00000]
b(2)        -0.05310     -3.935          -5.505
                      (0.01349)       (0.00965)
                      [0.00008]       [0.00000]
b(3)         3.10092     11.982          12.041
                      (0.25881)       (0.25753)
                      [0.00000]       [0.00000]

Effective sample size (n):                          172
Variance of the residuals:                      2.67422
Standard error of the residuals (SER):         1.635304
Residual sum of squares (RSS):                451.94311
(Also called SSR = Sum of Squared Residuals)
Total sum of squares (TSS):                  679.667151
R-square:                                        0.3351
Adjusted R-square:                               0.3272

Overall F test: F(2,169) = 42.58
p-value = 0.00000

*****************
MicrOsiris
*****************
Return to top

****************
(this is default, best fit.)
****************
Total case count:       172
 
 
STANDARD REGRESSION
 
THE DEPENDENT VARIABLE IS V: free00
 
     STANDARD ERROR OF ESTIMATE                1.64
     F-RATIO FOR THE REGRESSION              28.360    PROBABILITY  0.00
     MULTIPLE CORRELATION COEFFICIENT        0.5798    ADJUSTED   0.5695
     FRACTION OF EXPLAINED VARIANCE          0.3362    ADJUSTED   0.3243
     DETERMINANT OF THE CORRELATION MATRIX  0.31037
     RESIDUAL DEGREES OF FREEDOM (N-K-1)        168
 
     CONSTANT TERM    3.5943                           STD. ERROR  0.958673
 
 VARIABLE     NAME                   B         SIGMA(B)      BETA       SIGMA(BETA)
 
    V2  imr2000                  0.16977E-01  0.51931E-02  0.33546      0.10261
    V3  gdpcap00                -0.52809E-01  0.13534E-01 -0.28717      0.73597E-01
    V4  clit                    -0.50331E-02  0.94148E-02 -0.51560E-01  0.96447E-01

 
                               PARTIAL  PART  MARGINAL               COVARIANCE
 VARIABLE     NAME                R       R     RSQD    T-RATIO(PROB)   RATIO
 
    V2  imr2000                  0.245  0.205  0.0422   3.2692 (.002)   0.624
    V3  gdpcap00                -0.288  0.245  0.0602   3.9018 (.000)   0.270
    V4  clit                    -0.041  0.034  0.0011   0.5346 (.600)   0.575


*****************
Epi Info
*****************
Return to top

Linear Regression


Variable Coefficient Std Error F-test P-Value
clit -0.005 0.009 0.2858 0.593642
gdpcap00 -0.053 0.014 15.2243 0.000138
imr2000 0.017 0.005 10.6876 0.001310
CONSTANT 3.594 0.959 14.0569 0.000244


Correlation Coefficient: r^2= 0.34


Source df Sum of Squares Mean Square F-statistic
Regression 3 228.492 76.164 28.360
Residuals 168 451.176 2.686  
Total 171 679.667  











Return to top
Click here to return to the free software page

last verified 1/13/08