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.  I updated this in November 2008 with OpenStat and added Excel, and updated again in March 2012 with PSPP

Easyreg   http://econ.la.psu.edu/~hbierens/EASYREG.HTM
Epidata     http://www.epidata.dk/
Instat   http://www.reading.ac.uk/ssc/resourcepage/instat.php  
LazStats   http://statprogramsplus.com/.index.html        
WinIDAMS  http://portal.unesco.org/ci/en/ev.php-URL_ID=2070&URL_DO=DO_TOPIC&URL_SECTION=201.html    
MicrOsiris  http://www.microsiris.com/
Epi Info 2000 Windows   http://wwwn.cdc.gov/epiinfo/    
PSPP   http://www.gnu.org/software/pspp/    (downloaded March 2012)

I also added in Excel, and Gnumeric  http://www.gnumeric.org/   But gnumeric no longer available for windows.


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,
a. import the .csv file, then call up commands. 
b. for blanks, Microsiris assigns 1.5 and 1.6 billion, but automatically recognises these values as missing.
c. the data dictionary shows 0 decimal places, but if the data actually have decimal places, like 1.23, the number is read as 1.23, with the decimal place.  The data dictionary shows how many decimal places are implied, if there isn't one.

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


5. Correlation:

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?
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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, Instat, 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://www.economicsbulletin.com/  

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


Epidata


Epidata is 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 

EpiData Introduction Guide – A Canadian Example   http://www.apheo.ca/resources/projects/epidata/Preliminary%20EpiData%20Introduction_fieldguide%20v-2%20Oct18.pdf     


Epi Info  

Seroprevalence of hepatitis C and associated risk factors in urban areas of Antananarivo, Madagascar
Charles E Ramarokoto and others. BMC Infectious Diseases 2008, 8:25  
http://www.biomedcentral.com/1471-2334/8/25/  

ME Gyasi, WMK Amoaku, and MA Adjuik. Epidemiology of Hospitalized Ocular Injuries in the Upper East Region of Ghana. Ghana Med J. 2007 December; 41(4): 171–175.   
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2350113/   

Nocardial infections: report of 22 cases
Maria Bernadete F. Chedid; Marcio F. Chedid; Nelson S. Porto; Cecília B. Severo; Luiz Carlos Severo
Rev. Inst. Med. trop. S. Paulo vol.49 no.4 São Paulo Jul./Aug. 2007  
http://www.scielo.br/scielo.php?pid=S0036-46652007000400009&script=sci_arttext&tlng=en  

Intestinal parasitosis and nutritional status in schoolchildren of Sahar district, Yemen
Y.A. Raja’a and J.S. Mubarak
Eastern Mediterranean Health Journal, Vol. 12 (Supplement 2), 2006 S189  
http://www.emro.who.int/Publications/EMHJ/12_S2/article18.htm   

 Saskia et al.
Managing dental caries with atraumatic restorative treatment in children: successful experience in three Latin American countries.
Rev Panam Salud Publica [online]. 2013, vol.33, n.4 [cited  2013-11-03], pp. 237-243 .
http://dx.doi.org/10.1590/S1020-49892013000400001  

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.ajol.info/index.php/ajst/issue/view/2035   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/content/105/4/1476.full   

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://bloodjournal.hematologylibrary.org/content/105/4/1476.full   

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.



MicrOsiris

A Study of Multidimensional Religion Constructs and Values in the United Kingdom
Miriam Pepper, Tim Jackson, David Uzzell
Journal for the Scientific Study of Religion, Volume 49, Issue 1, pages 127–146, March 2010  
http://onlinelibrary.wiley.com/doi/10.1111/j.1468-5906.2009.01496.x/full  

An examination of the values that motivate socially conscious and frugal consumer behaviours
Miriam Pepper, Tim Jackson, David Uzzell, International Journal of Consumer Studies, Volume 33, Issue 2, pages 126–136, March 2009  
http://onlinelibrary.wiley.com/doi/10.1111/j.1470-6431.2009.00753.x/abstract  

Locking Plate Fixation for Proximal Humerus Fractures: A Comparison With Other Fixation Techniques
Darin M. Friess, MD; Albert Attia, MD; Heather A. Vallier, MD
ORTHOPEDICS December 2008;31(12):1183.  
http://www.orthosupersite.com/view.aspx?rid=34698  

Predicting Law School Success: A Study of Goal Orientations, Academic Achievement, and the Declining Self-Efficacy of Our Law Students
Leah M. Christensen, TJSL Legal Studies Research Paper No. 1235528
Law and Psychology Review, Vol. 33, Spring 2009   
http://papers.ssrn.com/sol3/papers.cfm?abstract_id=1235528    

History and Potential of Binary Segmentation for Exploratory Data Analysis. James N. Morgan.
Journal of Data Science, 2005, v.3, no.2, 123-136  
http://www.jds-online.com/v3-2     

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



LazStats  

Fundamental Statistics for the Behavioral Sciences. By David C. Howell. Cengage Learning, Mar 1, 2013. https://books.google.com/books?id=iYkWAAAAQBAJ&dq=lazstats&lr=&source=gbs_navlinks_s  

Correlation, Path Analysis and Stepwise Regression in Durum Wheat (Triticum Durum Desf.) under Rainfed Conditions. H Abderrahmane, F Zine El Abidine, B Hamenna and B Ammar. Journal of Agriculture and Sustainability. ISSN 2201-4357. Volume 3, Number 2, 2013, 122-131.   http://www.infinitypress.info/index.php/jas/article/viewFile/108/129  

Durum Wheat (Triticum durum Desf.) Evaluation under Semi Arid Conditions in Eastern Algeria by Path Analysis. A. Guendouz, M. Djoudi, S. Guessoum, K. Maamri, Z. Fellahi, A. Hannachi and
M. Hafsi. Journal of Agriculture and Sustainability. ISSN 2201-4357. Volume 3, Number 2, 2013, 238-246   http://infinitypress.info/index.php/jas/article/viewFile/93/436  

See this page for a list of articles using Openstat, another version of LazStats  http://statprogramsplus.com/citations.htm   


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


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.ncbi.nlm.nih.gov/pmc/articles/PMC1361357/   

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


WinIdams   

High Differentiation among Eight Villages in a Secluded Area of Sardinia Revealed by Genome-Wide High Density SNPs Analysis
Giorgio Pistis and a lot of others.... 
PLoS ONE. 2009; 4(2): e4654.
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2646134/   

Computational Modeling of Substitution Effect on HIV–1 Non–Nucleoside Reverse Transcriptase Inhibitors with Kier–Hall Electrotopological State (E–state) Indices
Nitin S. Sapre,1 Nilanjana Pancholi,1 and Swagata Gupta
Internet Electronic Journal of Molecular Design, March 2008, Volume 7, Number 3, Pages 55–67
http://biochempress.com/Files/iejmd_2008_7_0055.pdf   

Counting Clusters Using R-NN Curves
Rajarshi Guha, Debojyoti Dutta, David J. Wild, and Ting Chen
J Chem Inf Model. 2007; 47(4): 1308–1318.
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2543137/   

Bibliometric indicators of the Brazilian scientific production: an analysis from Pascal base
Rogério Mugnaini; Paulo de Martino Jannuzzi; Luc QuoniamIII
Ci. Inf. vol.33 no.2 Brasília May/Aug. 2004
http://www.scielo.br/scielo.php?pid=S0100-19652004000200013&script=sci_arttext&tlng=en   

Chapter IX, Prospects and Scopes of Data Mining Applications in Society Development Activities
Hakikur Rahman
in: Data Mining Applications for Empowering Knowledge Societies
Hakikur Rahman, Sustainable Development Networking Foundation (SDNF), Bangladesh    
http://www.academia.edu/1346110/Prospects_and_Scopes_of_Data_Mining_Applications_in_Society_Development_Activities   

Nitin S. Sapre, Nilanjana Pancholi, and Swagata Gupta
Computational Modeling of Substitution Effect on HIV-1 Non-Nucleoside Reverse Transcriptase Inhibitors with Kier-Hall Electrotopological State (E-state) Indices
Internet Electron. J. Mol. Des. 2008, 7, 55-67
http://biochempress.com/av07_0055.html     

Dioxin in the Atmosphere of Denmark, A Field Study at Selected Locations
NERI Technical Report No. 565, 2005
Jørgen Vikelsøe, Helle Vibeke Andersen, Rossana Bossi, Elsebeth Johansen, Mary-Ann Chrillesen
Mads F. Hovmand, Science Consultant
National Environmental Research Institute, Ministry of the Environment, The Danish Dioxin Monitoring Programme II
http://www2.dmu.dk/1_viden/2_Publikationer/3_fagrapporter/rapporter/FR565.pdf   


PEPI for Windows
(I haven't use this either yet.)

Estrogen receptor 1 gene polymorphisms and coronary artery disease in the Brazilian population.  S. Almeida and M.H. Hutz
Braz J Med Biol Res, April 2006, Volume 39(4) 447-454  
http://www.scielo.br/scielo.php?pid=S0100-879X2006000400004&script=sci_arttext&tlng=en   

Nitrite inhalant use among young gay and bisexual men in Vancouver during a period of increasing HIV incidence
Thomas M Lampinen, Kelly Mattheis, Keith Chan and Robert S Hogg. BMC Public Health 2007, 7:35  
http://www.biomedcentral.com/1471-2458/7/35/   

Association of a Bovine Prion Gene Haplotype with Atypical BSE
Michael L. Clawson and a bunch of other people.  PLoS ONE. 2008; 3(3): e1830.  
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2263129/   

Perinatal Outcomes Associated With Preterm Birth at 33 to 36 Weeks’ Gestation: A Population-Based Cohort Study.  Minesh Khashu and others
PEDIATRICS Vol. 123 No. 1 January 2009, pp. 109-113  
http://pediatrics.aappublications.org/content/123/1/109.full    



Just Correlations
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*****************
EPIDATA
*****************

            free00     lit    gdpcap00 imr2000
free00        1.000               
literacy     -0.425    1.000          
gdpcap00     -0.483    0.416    1.000     
imr2000       0.524   -0.758   -0.519    1.000

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

           imr2000    gdpcap0     lit       free00
imr2000    1.0000
gdpcap0   -0.5190    1.0000
literacy  -0.7580    0.4158     1.0000
free00     0.5236   -0.4827    -0.4252    1.0000

*****************
LazStats
*****************

             Correlations
             IMR2000     GDPCAP        lit       free
imr2000         1.000     -0.519     -0.758      0.524
gdpcap00       -0.519      1.000      0.416     -0.483
literacy       -0.758      0.416      1.000     -0.425
free00          0.524     -0.483     -0.425      1.000
No. of valid cases = 172

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

3 gdpcap00                   3  -0.5190
4 literacy                   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      literacy
gdpcap00 V3  -0.5190
literacy V4  -0.7580   0.4158
free00   V5   0.5236  -0.4827  -0.4252

*****************
PSPP
*****************

Correlations
#============================#=======#========#====#======#
#                            #imr2000|gdpcap00| lit|free00#
#--------+-------------------#-------+--------+----+------#
#imr2000 |Pearson Correlation#   1.00|    -.52|-.76|   .52#
#        |Sig. (2-tailed)    #       |     .00| .00|   .00#
#        |N                  #    172|     172| 172|   172#
#--------+-------------------#-------+--------+----+------#
#gdpcap00|Pearson Correlation#   -.52|    1.00| .42|  -.48#
#        |Sig. (2-tailed)    #    .00|        | .00|   .00#
#        |N                  #    172|     172| 172|   172#
#--------+-------------------#-------+--------+----+------#
# lit    |Pearson Correlation#   -.76|     .42|1.00|  -.43#
#        |Sig. (2-tailed)    #    .00|     .00|    |   .00#
#        |N                  #    172|     172| 172|   172#
#--------+-------------------#-------+--------+----+------#
#free00  |Pearson Correlation#    .52|    -.48|-.43|  1.00#
#        |Sig. (2-tailed)    #    .00|     .00| .00|      #
#        |N                  #    172|     172| 172|   172#
#========#===================#=======#========#====#======#



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

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

            free00     lit    gdpcap00 imr2000
free00        1.000               
  lit        -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
  lit        -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     lit       free00
imr2000    1.0000
gdpcap0   -0.5190     1.0000
  lit      -0.7580     0.4158     1.0000
free00     0.5236    -0.4827    -0.4252    1.0000

ANOVA for regression of free00
on imr2000 gdpcap0  lit
-------------------------------------------------------------------
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)


*****************
LazStats
*****************
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** block regression **
Block Entry Multiple Regression by Bill Miller

----------------- Trial Block 1 Variables Added ------------------
SOURCE    DF        SS      MS        F        Prob.>F
Regression   3   228.492    76.164    28.360     0.000
Residual   168   451.176     2.686
Total      171   679.667

Dependent Variable: free00

       R        R2         F     Prob.>F  DF1  DF2
   0.580     0.336    28.360     0.000    3  168
Adjusted R Squared = 0.324

Std. Error of Estimate =      1.639

Variable       Beta      B         Std.Error t         Prob.>t   VIF       TOL
   imr2000     0.335     0.017     0.005     3.269     0.001     2.665     0.375
  gdpcap00    -0.287    -0.053     0.014    -3.902     0.000     1.371     0.729
      clit    -0.052    -0.005     0.009    -0.535     0.594     2.354     0.425

Constant =      3.594
Increase in R Squared =  0.336
F = 28.360 with probability =  0.000
Block 1 met entry requirements






*****************
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       lit                  


 **************** 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       lit                            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   lit                  


 **************** 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       lit                            0.0011                                                       *



**************
Easyreg
**************
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X variables:

X(1) = imr2000
X(2) = gdpcap00
X(3) =  lit
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   lit                    -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   lit                    -0.041  0.034  0.0011   0.5346 (.600)   0.575


*****************
Epi Info
*****************
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Linear Regression


Variable Coefficient Std Error F-test P-Value
 lit -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  












*****************
PSPP
*****************

#====#========#=================#==========================#
#  R #R Square|Adjusted R Square|Std. Error of the Estimate#
##===#========#=================#==========================#
#|.58#     .34|              .33|                      1.64#
##===#========#=================#==========================#

ANOVA
#===========#==============#===#===========#=====#============#
#           #Sum of Squares| df|Mean Square|  F  |Significance#
##==========#==============#===#===========#=====#============#
#|Regression#        228.49|  3|      76.16|28.36|         .00#
#|Residual  #        451.18|168|       2.69|     |            #
#|Total     #        679.67|171|           |     |            #
##==========#==============#===#===========#=====#============#

Coefficients
#===========#====#==========#====#=====#============#
#           #  B |Std. Error|Beta|  t  |Significance#
##==========#====#==========#====#=====#============#
#|(Constant)#3.59|       .96| .00| 3.75|         .00#
#|   clit   #-.01|       .01|-.05| -.53|         .59#
#| gdpcap00 #-.05|       .01|-.29|-3.90|         .00#
#|  imr2000 # .02|       .01| .34| 3.27|         .00#
##==========#====#==========#====#=====#============#





*****************
Excel (2007)
*****************

                              
Regression Statistics                               
Multiple R          0.579811642                           
R Square            0.33618154                           
Adjusted R Square   0.324327639                           
Standard Error      1.638770593                           
Observations        172                           
                               
ANOVA                               
              df    SS             MS            F                Significance F           
Regression    3    228.4915497    76.16384991    28.36041387    6.86936E-15           
Residual    168    451.1756014    2.685569056                   
Total       171    679.6671512                       
                               
                Coefficients    Standard Error    t Stat       P-value   
Intercept        3.594308852     0.958673059      3.749254054   0.000243724
IMR 2000         0.016977269     0.005193115      3.269187971   0.001308309
GDPCAP 2000     -0.052809159     0.01353443      -3.90183839    0.000137904
Literacy        -0.005033087     0.009414797     -0.534593257   0.593637938


*****************
Gnumeric
*****************
                              
SUMMARY OUTPUT        Response Variable:    Column 5           
                       
Regression Statistics                       
Multiple R        0.5798                   
R^2               0.3362                   
Standard Error    1.6388                   
Adjusted R^2      0.3243                   
Observations    172.0000                   
                       
ANOVA                       
                df        SS            MS       F       Significance of F   
Regression    3.0000    228.4915    76.1638    28.3604    0.0000   
Residual    168.0000    451.1756    2.6856           
Total       171.0000    679.6672               
                       
        Coefficients    Standard Error    t-Statistics    p-Value    0.9500    0.9500
Intercept    3.5943        0.9587        3.7493            0.0002    1.7017    5.4869
IMR          0.0170        0.0052        3.2692            0.0013    0.0067    0.0272
GDP capita  -0.0528        0.0135       -3.9018            0.0001   -0.0795   -0.0261
Literacy    -0.0050        0.0094       -0.5346            0.5936   -0.0236    0.0136




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last updated 11/3/13
last verified 11/3/13

try packages using data sets here
http://pages.stern.nyu.edu/~jsimonof/classes/1305/pdf/excelreg.pdf