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 PSPP, and added
Excel.
Easyreg http://econ.la.psu.edu/~hbierens/EASYREG.HTM
Epidata http://www.epidata.dk/
Instat http://www.reading.ac.uk/ssc/n/n_instat.htm
OpenStat http://www.statpages.org/miller/openstat/
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/
(October 2009 version)
I also added in Excel, and Gnumeric http://projects.gnome.org/gnumeric/
(spreadsheets)
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 OpenStat,
I can't get OpenStat to read my data sets without errors. I
don't know why. Bill Miller was kind enough to take my data set and save
it in openstat format http://gsociology.icaap.org/methods/fourvars_nonames.TEX
When I use this data set, I have no problems, and
get the same results as with other programs. In addition, as seen below, plenty of
people use OpenStat to generate statistics and publish papers in
peer reviewed journals, using the results from OpenStat. So the
package is well used and can give useful results.
5. When using epistat,
regression make the dependent variable the first in the
list.
6. Correlation:
- All programs give
exactly the same correlation coefficients.
- OpenStat 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.
- PSPP does not yet
have correlation (Nov 7, 2009). They just developed
correlation and will include it in the next release.
I'll do another review then.
7. Regression:
all programs give the same results for basic regression and some
same results for backward/forward stepwise.
- Epidata, Epi Info, Instat+ and PSPP don't allow choices of
what type of output.
- Output from Epidata, Epi Info, Instat+ and PSPP are the same,
and the same as excel and gnumeric. Also the same as WinIDAMS
stepwise and an Easyreg regression, and the same as OpenStat
Block regression (forcing all the variables into the equation).
- 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.
- When I use WinIDAMS stepwise and WinIDAMS decending, I get the
same results.
- I can't match OpenStat backwards regression with any other
output. I don't know why.
- MicrOsiris has
stepwise, but I haven't figured it out yet.
- WinIDAMS seems to
use B and Beta opposite from other packages. For
example, OpenStat's B is WinIDAMS's beta.
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.
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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, 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/
Rahav G, Gabbay R, Ornoy A, Shechtman S, Arnon J, Diav-Citrini O.
Primary versus nonprimary cytomegalovirus infection during
pregnancy, Israel. Emerg Infect Dis [serial on the Internet]. 2007
Nov [May 15, 2009]. Available from http://wwwnc.cdc.gov/eid/article/13/11/06-1289_article.htm
Chan P-C, Huang L-M, Wu Y-C, Yang H-L, Chang I-S, Lu C-Y, et al.
Tuberculosis in children and adolescents, Taiwan, 1996–2003. Emerg
Infect Dis [serial on the Internet]. 2007 Sep. Available
from http://wwwnc.cdc.gov/eid/article/13/9/06-1020_article.htm
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
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
OpenStat
NMDA-induced Seizure Intensity is Enhanced in COX-2 Deficient Mice
Christopher D. Toscano, Philip J. Kingsley, Lawrence J. Marnett, and
Francesca Bosetti
Neurotoxicology. 2008 November; 29(6): 1114–1120.
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2587528/
Future Salary and US Residency Fill Rate Revisited
Mark Ebell. Research letter in JAMA, September 10, 2008—Vol
300, No. 10, p1131-1132
http://jama.ama-assn.org/content/300/10/1131.full.pdf+html
Pulido Ximena Carolina, Pérez Gerardo, Vallejo Gustavo
Adolfo.
Preliminary characterization of a Rhodnius prolixus hemolymph
trypanolytic protein, this being a determinant of Trypanosoma
rangeli KP1(+) and KP1(-) subpopulations' vectorial ability. Mem.
Inst. Oswaldo Cruz [serial on the Internet]. 2008 Mar
[cited 2009 May 28] ; 103(2): 172-179.
Available from: http://www.scielo.br/scielo.php?script=sci_arttext&pid=S0074-02762008000200008&lng=en
. doi: 10.1590/S0074-02762008000200008.
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.ncbi.nlm.nih.gov/pmc/articles/PMC1839133/
Shiga Toxin-Mediated Hemolytic Uremic Syndrome: Time to Change the
Diagnostic Paradigm?
Martina Bielaszewska, Robin Köck, Alexander W. Friedrich,
Christof von Eiff, Lothar B. Zimmerhackl, Helge Karch, and Alexander
Mellmann
PLoS ONE. 2007; 2(10): e1024
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC1995754/
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.ncbi.nlm.nih.gov/pmc/articles/PMC538959/
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.ncbi.nlm.nih.gov/pmc/articles/PMC1393231/
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.
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/content/104/13/4020.full
A journal of the American Society of Hematology. Uses OpenStat.
Reviews which include
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/newsletters/
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.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/
Enzyme Activity Fingerprinting with Substrate Cocktails
Jean-Philippe Goddard and Jean-Louis Reymond
J. AM. CHEM. SOC. 2004, 126, 11116-11117
http://www.dcb-server.unibe.ch/groups/reymond/publications/95.pdf
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.damonlogan.com/Books/seo/Data%20Mining%20Applications%20for%20Empowering%20Knowledge%20Societies~tqw~_darksiderg.pdf#page=187
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
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*****************
EPIDATA
*****************
free00
clit 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 clit
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
*****************
OpenStat
*****************
Correlations
IMR2000
GDPCAP
C-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
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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)
*****************
OpenSTAT
*****************
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** block regression **
Dependent variable: VAR5
Variable
Beta
B Std.Err.
t
Prob.>t VIF TOL
IMR
0.335 0.017
0.005 3.269
0.001 2.665 0.375
GDPCAP -0.287
-0.053 0.014
-3.902 0.000
1.371 0.729
Literacy -0.052
-0.005 0.009
-0.535 0.594
2.354 0.425
Intercept
0.000 3.594
0.959 3.749 0.000
SOURCE
DF
SS
MS
F Prob.>F
Regression 3 228.492
76.164 28.360 0.0000
Residual 168
451.176 2.686
Total 171 679.667
R2 = 0.3362, F = 28.36, D.F. = 3 168, Prob>F
= 0.0000
Adjusted R2 = 0.3243
Standard Error of Estimate = 1.64
F = 28.360 with probability = 0.000
Block 1 met entry requirements
Correlations
Product-Moment Correlations
Matrix
Variables IMR2000
GDPCAP
C-lit free
IMR2000
1.000
-0.529
-0.757 0.520
GDPCAP
-0.529
1.000
0.420 -0.482
C-lit
-0.757
0.420
1.000 -0.421
free
0.520
-0.482
-0.421 1.000
** BEST FIT REGRESSION **
Variables entered in
step 2
1 IMR2000
2 GDPCAP
Squared Multiple Correlation = 0.3298
Dependent variable = free
ANOVA for Regression Effects :
SOURCE
df
SS
MS
F
Prob
Regression 2
221.8869
110.9434
41.3269 0.0000
Residual 168
451.0020 2.6845
Total
170 672.8889
Variables in the equation
VARIABLE
b
s.e.
b
Beta
t prob. t
IMR2000 0.01860 0.0038
0.3683 4.949 0.0000
GDPCAP -0.05640 0.0146 -0.2870
-3.857 0.0002
(Intercept)
3.13079
Increase in squared R for this step = 0.059349
F = 14.8761 with D.F. 1 and 168 with Probability = 0.0002
----------------------------------------------------------
Last variable added failed entry test. Job ended.
----------------------------------------------------------
OpenStat BACKWARD STEPWISE
Backward Stepwise Multiple Regression by Bill Miller
----------------- STEP 3 ------------------
Determinant of correlation matrix = 0.7296
SOURCE
DF
SS
MS
F Prob.>F
Regression 1 181.951
181.951 62.635 0.000
Residual 169
490.937 2.905
Total 170 672.889
Dependent Variable: free
R
R2
F Prob.>F DF1 DF2
0.520 0.270
62.635 0.000 1 169
Adjusted R Squared = 0.266
Std. Error of Estimate = 1.704
Variable
Beta
B Std.Error
t
Prob.>t VIF TOL
IMR2000
0.520 0.026
0.003 7.914
0.000 1.000 1.000
Constant = 2.426
Partial Correlations
Variables IMR2000
0.520
-----------------------------------------
OpenStat
FORWARD STEPWISE
-----------------------------------------
-------------FINAL
STEP-----------
SOURCE
DF
SS
MS
F Prob.>F
Regression 2 221.887
110.943 41.327 0.000
Residual 168
451.002 2.685
Total 170 672.889
Dependent Variable: free
R
R2
F Prob.>F DF1 DF2
0.574 0.330
41.327 0.000 2 168
Adjusted R Squared = 0.322
Std. Error of Estimate = 1.638
Variable
Beta
B Std.Error
t
Prob.>t VIF TOL
IMR2000
0.368 0.019
0.004 4.949
0.000 1.388 0.721
GDPCAP
-0.287 -0.056
0.015 -3.857
0.000 1.388 0.721
Constant = 3.131
*****************
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
*****************
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 |
|
*****************
PSPP
*****************
Currently (Nov 2009) PSPP has
problems with getting output to a file. You can't cut and
paste or save the output anywhere. It is supposed to save to
a file called psppire.txt but I can't find that file
anywhere on my computer. I did a print screen, saved it to paint,
and then saved as jpg, and then inserted to this page. Awkward.
*****************
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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to return to the free software page
last updated 10/16/11
last verified 10/2/10
try packages using data sets here
http://pages.stern.nyu.edu/~jsimonof/classes/1305/pdf/excelreg.pdf