comparing freestat programs

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.

Stat4U also gives statistical significance levels for the correlations. WinIDAMS gives t-tests. MicrOsiris also gives significance levels, t-tests.

Epi Info doesn't do simple correlations. You have to do regression with just two variables to get the correlation.

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

Epidata, Epi Info and Instat+ doesn't allow choices of what type of output.

Output from Epidata, Epi Info and Instat+ is the same from Stat4U best fit regression. Also the same as WinIdams stepwise and an Easyreg regression.

If you use any program to run a full model or best fit with all variables, then eliminate the variable that isn't significant, and then rerun the regression, the results are the same as the forward regression from Stat4U (which eliminated the insignificant variable).

When I use WinIDAMS stepwise and WinIDAMS decending, I get the same results.

I can't match Stat4U backwards regression with any other output. I don't know why.

MicrOsiris has stepwise, but I haven't figured it out yet.

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?

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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
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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

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*****************
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

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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
*****************
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             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)
*****************
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      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
**************
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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
*****************
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****************
(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

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Epi Info
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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

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last verified 1/13/08