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:
- All programs give
exactly the same correlation coefficients.
- 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 LazStats
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.
- 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.
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,
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
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.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
Return
to top
*****************
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#
#========#===================#=======#========#====#======#
Return
to top
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
*****************
Return
to top
** 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)
*****************
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 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
**************
Return
to top
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
*****************
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 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
*****************
Return
to top
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
Return
to top
Click here
to return to the free software page
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