+@c PSPP - a program for statistical analysis.
+@c Copyright (C) 2017 Free Software Foundation, Inc.
+@c Permission is granted to copy, distribute and/or modify this document
+@c under the terms of the GNU Free Documentation License, Version 1.3
+@c or any later version published by the Free Software Foundation;
+@c with no Invariant Sections, no Front-Cover Texts, and no Back-Cover Texts.
+@c A copy of the license is included in the section entitled "GNU
+@c Free Documentation License".
+@c
@alias prompt = sansserif
@include tut.texi
* Reading data from a pre-prepared PSPP file::
* Saving data to a PSPP file.::
* Reading data from other sources::
+* Exiting PSPP::
@end menu
@node Defining Variables
variable and that its maximum length is 12 bytes.
The second variable's name is specified by the text @samp{height}.
Since no format is given, this variable has the default format.
+Normally the default format expects numeric data, which should be
+entered in the locale of the operating system.
+Thus, the example is correct for English locales and other
+locales which use a period (@samp{.}) as the decimal separator.
+However if you are using a system with a locale which uses the comma (@samp{,})
+as the decimal separator, then you should in the subsequent lines substitute
+@samp{.} with @samp{,}.
+Alternatively, you could explicitly tell @pspp{} that the @var{height}
+variable is to be read using a period as its decimal separator by appending the
+text @samp{DOT8.3} after the word @samp{height}.
For more information on data formats, @pxref{Input and Output Formats}.
It should show the following output:
@example
@group
-Case# forename height
------ ------------ --------
- 1 Ahmed 188.00
- 2 Bertram 167.00
- 3 Catherine 134.23
- 4 David 109.10
+ Data List
++-----------+---------+------+
+|Case Number| forename|height|
++-----------+---------+------+
+|1 |Ahmed |188.00|
+|2 |Bertram |167.00|
+|3 |Catherine|134.23|
+|4 |David |109.10|
++-----------+---------+------+
@end group
@end example
@noindent
In these instances you should
use the @cmd{GET DATA} command (@pxref{GET DATA}).
+@node Exiting PSPP
+@subsection Exiting PSPP
+
+Use the @cmd{FINISH} command to exit PSPP:
+@example
+@prompt{PSPP>} finish.
+@end example
@node Data Screening and Transformation
@section Data Screening and Transformation
Output:
@example
-DESCRIPTIVES. Valid cases = 40; cases with missing value(s) = 0.
-+--------#--+-------+-------+-------+-------+
-|Variable# N| Mean |Std Dev|Minimum|Maximum|
-#========#==#=======#=======#=======#=======#
-|sex #40| .45| .50| .00| 1.00|
-|height #40|1677.12| 262.87| 179.00|1903.00|
-|weight #40| 72.12| 26.70| -55.60| 92.07|
-+--------#--+-------+-------+-------+-------+
+ Descriptive Statistics
++---------------------+--+-------+-------+-------+-------+
+| | N| Mean |Std Dev|Minimum|Maximum|
++---------------------+--+-------+-------+-------+-------+
+|Sex of subject |40| .45| .50|Male |Female |
+|Weight in kilograms |40| 72.12| 26.70| -55.6| 92.1|
+|Height in millimeters|40|1677.12| 262.87| 179| 1903|
+|Valid N (listwise) |40| | | | |
+|Missing N (listwise) | 0| | | | |
++---------------------+--+-------+-------+-------+-------+
@end example
@end cartouche
@caption{Using the @cmd{DESCRIPTIVES} command to display simple
We can examine the data in more detail with the @cmd{EXAMINE}
command (@pxref{EXAMINE}):
-In @ref{examine} you can see that the lowest value of @var{height} is
+In @ref{ex1} you can see that the lowest value of @var{height} is
179 (which we suspect to be erroneous), but the second lowest is 1598
which
we know from the @cmd{DESCRIPTIVES} command
This suggests that the two extreme values are outliers and probably
represent data entry errors.
-@float Example, examine
+@float Example, ex1
@cartouche
[@dots{} continue from @ref{descriptives}]
@example
Output:
@example
-#===============================#===========#=======#
-# #Case Number| Value #
-#===============================#===========#=======#
-#Height in millimetres Highest 1# 14|1903.00#
-# 2# 15|1884.00#
-# 3# 12|1801.65#
-# ----------#-----------+-------#
-# Lowest 1# 30| 179.00#
-# 2# 31|1598.00#
-# 3# 28|1601.00#
-# ----------#-----------+-------#
-#Weight in kilograms Highest 1# 13| 92.07#
-# 2# 5| 92.07#
-# 3# 17| 91.74#
-# ----------#-----------+-------#
-# Lowest 1# 38| -55.60#
-# 2# 39| 54.48#
-# 3# 33| 55.45#
-#===============================#===========#=======#
+ Extreme Values
++-------------------------------+-----------+-----+
+| |Case Number|Value|
++-------------------------------+-----------+-----+
+|Height in millimeters Highest 1| 14| 1903|
+| 2| 15| 1884|
+| 3| 12| 1802|
+| Lowest 1| 30| 179|
+| 2| 31| 1598|
+| 3| 28| 1601|
++-------------------------------+-----------+-----+
+|Weight in kilograms Highest 1| 13| 92.1|
+| 2| 5| 92.1|
+| 3| 17| 91.7|
+| Lowest 1| 38|-55.6|
+| 2| 39| 54.5|
+| 3| 33| 55.4|
++-------------------------------+-----------+-----+
@end example
@end cartouche
@caption{Using the @cmd{EXAMINE} command to see the extremities of the data
For detailed information about the @cmd{RECODE} command @pxref{RECODE}.
If you now re-run the @cmd{DESCRIPTIVES} or @cmd{EXAMINE} commands in
-@ref{descriptives} and @ref{examine} you
+@ref{descriptives} and @ref{ex1} you
will see a data summary with more plausible parameters.
You will also notice that the data summaries indicate the two missing values.
reliability.
This gives the statistician some confidence that the questionnaires have been
completed thoughtfully.
-If you examine the labels of variables @var{v1}, @var{v3} and @var{v5},
+If you examine the labels of variables @var{v1}, @var{v3} and @var{v4},
you will notice that they ask very similar questions.
One would therefore expect the values of these variables (after recoding)
to closely follow one another, and we can test that with the @cmd{RELIABILITY}
command (@pxref{RELIABILITY}).
@ref{reliability} shows a @pspp{} session where the user (after recoding
negatively scaled variables) requests reliability statistics for
-@var{v1}, @var{v3} and @var{v5}.
+@var{v1}, @var{v3} and @var{v4}.
@float Example, reliability
@cartouche
@prompt{PSPP>} * recode negatively worded questions.
@prompt{PSPP>} compute v3 = 6 - v3.
@prompt{PSPP>} compute v5 = 6 - v5.
-@prompt{PSPP>} reliability v1, v3, v5.
+@prompt{PSPP>} reliability v1, v3, v4.
@end example
Output (dictionary information omitted for clarity):
@example
-1.1 RELIABILITY. Case Processing Summary
-#==============#==#======#
-# # N| % #
-#==============#==#======#
-#Cases Valid #17|100.00#
-# Excluded# 0| .00#
-# Total #17|100.00#
-#==============#==#======#
-
-1.2 RELIABILITY. Reliability Statistics
-#================#==========#
-#Cronbach's Alpha#N of Items#
-#================#==========#
-# .86# 3#
-#================#==========#
+Scale: ANY
+
+Case Processing Summary
++--------+--+-------+
+|Cases | N|Percent|
++--------+--+-------+
+|Valid |17| 100.0%|
+|Excluded| 0| .0%|
+|Total |17| 100.0%|
++--------+--+-------+
+
+ Reliability Statistics
++----------------+----------+
+|Cronbach's Alpha|N of Items|
++----------------+----------+
+| .81| 3|
++----------------+----------+
@end example
@end cartouche
@caption{Recoding negatively scaled variables, and testing for
reliability with the @cmd{RELIABILITY} command. The Cronbach Alpha
coefficient suggests a high degree of reliability among variables
-@var{v1}, @var{v2} and @var{v5}.}
+@var{v1}, @var{v3} and @var{v4}.}
@end float
As a rule of thumb, many statisticians consider a value of Cronbach's Alpha of
0.7 or higher to indicate reliable data.
-Here, the value is 0.86 so the data and the recoding that we performed
+Here, the value is 0.81 so the data and the recoding that we performed
are vindicated.
Output:
@example
-1.2 EXAMINE. Descriptives
-#====================================================#=========#==========#
-# #Statistic|Std. Error#
-#====================================================#=========#==========#
-#mtbf Mean # 8.32 | 1.62 #
-# 95% Confidence Interval for Mean Lower Bound# 4.85 | #
-# Upper Bound# 11.79 | #
-# 5% Trimmed Mean # 7.69 | #
-# Median # 8.12 | #
-# Variance # 39.21 | #
-# Std. Deviation # 6.26 | #
-# Minimum # 1.63 | #
-# Maximum # 26.47 | #
-# Range # 24.84 | #
-# Interquartile Range # 5.83 | #
-# Skewness # 1.85 | .58 #
-# Kurtosis # 4.49 | 1.12 #
-#====================================================#=========#==========#
-
-2.2 EXAMINE. Descriptives
-#====================================================#=========#==========#
-# #Statistic|Std. Error#
-#====================================================#=========#==========#
-#mtbf_ln Mean # 1.88 | .19 #
-# 95% Confidence Interval for Mean Lower Bound# 1.47 | #
-# Upper Bound# 2.29 | #
-# 5% Trimmed Mean # 1.88 | #
-# Median # 2.09 | #
-# Variance # .54 | #
-# Std. Deviation # .74 | #
-# Minimum # .49 | #
-# Maximum # 3.28 | #
-# Range # 2.79 | #
-# Interquartile Range # .92 | #
-# Skewness # -.16 | .58 #
-# Kurtosis # -.09 | 1.12 #
-#====================================================#=========#==========#
+ Case Processing Summary
++-----------------------------------+-------------------------------+
+| | Cases |
+| +----------+---------+----------+
+| | Valid | Missing | Total |
+| | N|Percent|N|Percent| N|Percent|
++-----------------------------------+--+-------+-+-------+--+-------+
+|Mean time between failures (months)|15| 100.0%|0| .0%|15| 100.0%|
++-----------------------------------+--+-------+-+-------+--+-------+
+
+ Descriptives
++----------------------------------------------------------+---------+--------+
+| | | Std. |
+| |Statistic| Error |
++----------------------------------------------------------+---------+--------+
+|Mean time between Mean | 8.32| 1.62|
+|failures (months) 95% Confidence Interval Lower | 4.85| |
+| for Mean Bound | | |
+| Upper | 11.79| |
+| Bound | | |
+| 5% Trimmed Mean | 7.69| |
+| Median | 8.12| |
+| Variance | 39.21| |
+| Std. Deviation | 6.26| |
+| Minimum | 1.63| |
+| Maximum | 26.47| |
+| Range | 24.84| |
+| Interquartile Range | 5.83| |
+| Skewness | 1.85| .58|
+| Kurtosis | 4.49| 1.12|
++----------------------------------------------------------+---------+--------+
+
+ Case Processing Summary
++-------+-------------------------------+
+| | Cases |
+| +----------+---------+----------+
+| | Valid | Missing | Total |
+| | N|Percent|N|Percent| N|Percent|
++-------+--+-------+-+-------+--+-------+
+|mtbf_ln|15| 100.0%|0| .0%|15| 100.0%|
++-------+--+-------+-+-------+--+-------+
+
+ Descriptives
++----------------------------------------------------+---------+----------+
+| |Statistic|Std. Error|
++----------------------------------------------------+---------+----------+
+|mtbf_ln Mean | 1.88| .19|
+| 95% Confidence Interval for Mean Lower Bound| 1.47| |
+| Upper Bound| 2.29| |
+| 5% Trimmed Mean | 1.88| |
+| Median | 2.09| |
+| Variance | .54| |
+| Std. Deviation | .74| |
+| Minimum | .49| |
+| Maximum | 3.28| |
+| Range | 2.79| |
+| Interquartile Range | .92| |
+| Skewness | -.16| .58|
+| Kurtosis | -.09| 1.12|
++----------------------------------------------------+---------+----------+
@end example
@end cartouche
@caption{Testing for normality using the @cmd{EXAMINE} command and applying
For the @var{height} variable, the output shows the significance of the
Levene test to be 0.33 which means there is a
33% probability that the
-Levene test produces this outcome when the variances are unequal.
-Such a probability is too high
-to assume that the variances are equal so the row
-for unequal variances should be used.
+Levene test produces this outcome when the variances are equal.
+Had the significance been less than 0.05, then it would have been unsafe to assume that
+the variances were equal.
+However, because the value is higher than 0.05 the homogeneity of variances assumption
+is safe and the ``Equal Variances'' row (the more powerful test) can be used.
Examining this row, the two tailed significance for the @var{height} t-test
is less than 0.05, so it is safe to reject the null hypothesis and conclude
that the mean heights of males and females are unequal.
For the @var{temperature} variable, the significance of the Levene test
-is 0.58 so again, it is unsafe to use the row for equal variances.
-The unequal variances row indicates that the two tailed significance for
-@var{temperature} is 0.19. Since this is greater than 0.05 we must reject
+is 0.58 so again, it is safe to use the row for equal variances.
+The equal variances row indicates that the two tailed significance for
+@var{temperature} is 0.20. Since this is greater than 0.05 we must reject
the null hypothesis and conclude that there is insufficient evidence to
suggest that the body temperature of male and female persons are different.
@end example
Output:
@example
-1.1 T-TEST. Group Statistics
-#==================#==#=======#==============#========#
-# sex | N| Mean |Std. Deviation|SE. Mean#
-#==================#==#=======#==============#========#
-#height Male |22|1796.49| 49.71| 10.60#
-# Female|17|1610.77| 25.43| 6.17#
-#temperature Male |22| 36.68| 1.95| .42#
-# Female|18| 37.43| 1.61| .38#
-#==================#==#=======#==============#========#
-1.2 T-TEST. Independent Samples Test
-#===========================#=========#=============================== =#
-# # Levene's| t-test for Equality of Means #
-# #----+----+------+-----+------+---------+- -#
-# # | | | | | | #
-# # | | | |Sig. 2| | #
-# # F |Sig.| t | df |tailed|Mean Diff| #
-#===========================#====#====#======#=====#======#=========#= =#
-#height Equal variances# .97| .33| 14.02|37.00| .00| 185.72| ... #
-# Unequal variances# | | 15.15|32.71| .00| 185.72| ... #
-#temperature Equal variances# .31| .58| -1.31|38.00| .20| -.75| ... #
-# Unequal variances# | | -1.33|37.99| .19| -.75| ... #
-#===========================#====#====#======#=====#======#=========#= =#
+ Group Statistics
++-------------------------------------------+--+-------+-------------+--------+
+| | | | Std. | S.E. |
+| Group | N| Mean | Deviation | Mean |
++-------------------------------------------+--+-------+-------------+--------+
+|Height in millimeters Male |22|1796.49| 49.71| 10.60|
+| Female|17|1610.77| 25.43| 6.17|
++-------------------------------------------+--+-------+-------------+--------+
+|Internal body temperature in degrees Male |22| 36.68| 1.95| .42|
+|Celcius Female|18| 37.43| 1.61| .38|
++-------------------------------------------+--+-------+-------------+--------+
+
+ Independent Samples Test
++---------------------+-----------------------------------------------------
+| | Levene's
+| | Test for
+| | Equality
+| | of
+| | Variances T-Test for Equality of Means
+| +----+-----+-----+-----+-------+----------+----------+
+| | | | | | | | |
+| | | | | | | | |
+| | | | | | | | |
+| | | | | | | | |
+| | | | | | Sig. | | |
+| | | | | | (2- | Mean |Std. Error|
+| | F | Sig.| t | df |tailed)|Difference|Difference|
++---------------------+----+-----+-----+-----+-------+----------+----------+
+|Height in Equal | .97| .331|14.02|37.00| .000| 185.72| 13.24|
+|millimeters variances| | | | | | | |
+| assumed | | | | | | | |
+| Equal | | |15.15|32.71| .000| 185.72| 12.26|
+| variances| | | | | | | |
+| not | | | | | | | |
+| assumed | | | | | | | |
++---------------------+----+-----+-----+-----+-------+----------+----------+
+|Internal Equal | .31| .581|-1.31|38.00| .198| -.75| .57|
+|body variances| | | | | | | |
+|temperature assumed | | | | | | | |
+|in degrees Equal | | |-1.33|37.99| .190| -.75| .56|
+|Celcius variances| | | | | | | |
+| not | | | | | | | |
+| assumed | | | | | | | |
++---------------------+----+-----+-----+-----+-------+----------+----------+
+
++---------------------+-------------+
+| | |
+| | |
+| | |
+| | |
+| | |
+| +-------------+
+| | 95% |
+| | Confidence |
+| | Interval of |
+| | the |
+| | Difference |
+| +------+------+
+| | Lower| Upper|
++---------------------+------+------+
+|Height in Equal |158.88|212.55|
+|millimeters variances| | |
+| assumed | | |
+| Equal |160.76|210.67|
+| variances| | |
+| not | | |
+| assumed | | |
++---------------------+------+------+
+|Internal Equal | -1.91| .41|
+|body variances| | |
+|temperature assumed | | |
+|in degrees Equal | -1.89| .39|
+|Celcius variances| | |
+| not | | |
+| assumed | | |
++---------------------+------+------+
@end example
@end cartouche
@caption{The @cmd{T-TEST} command tests for differences of means.
@prompt{PSPP>} regression /variables = mtbf duty_cycle /dependent = mttr.
@prompt{PSPP>} regression /variables = mtbf /dependent = mttr.
@end example
-Output:
+Output (excerpts):
@example
-1.3(1) REGRESSION. Coefficients
-#=============================================#====#==========#====#=====#
-# # B |Std. Error|Beta| t #
-#========#====================================#====#==========#====#=====#
-# |(Constant) #9.81| 1.50| .00| 6.54#
-# |Mean time between failures (months) #3.10| .10| .99|32.43#
-# |Ratio of working to non-working time#1.09| 1.78| .02| .61#
-# | # | | | #
-#========#====================================#====#==========#====#=====#
-
-1.3(2) REGRESSION. Coefficients
-#=============================================#============#
-# #Significance#
-#========#====================================#============#
-# |(Constant) # .10#
-# |Mean time between failures (months) # .00#
-# |Ratio of working to non-working time# .55#
-# | # #
-#========#====================================#============#
-2.3(1) REGRESSION. Coefficients
-#============================================#=====#==========#====#=====#
-# # B |Std. Error|Beta| t #
-#========#===================================#=====#==========#====#=====#
-# |(Constant) #10.50| .96| .00|10.96#
-# |Mean time between failures (months)# 3.11| .09| .99|33.39#
-# | # | | | #
-#========#===================================#=====#==========#====#=====#
-
-2.3(2) REGRESSION. Coefficients
-#============================================#============#
-# #Significance#
-#========#===================================#============#
-# |(Constant) # .06#
-# |Mean time between failures (months)# .00#
-# | # #
-#========#===================================#============#
+ Coefficients (Mean time to repair (hours) )
++------------------------+-----------------------------------------+-----+----+
+| | Unstandardized Standardized | | |
+| | Coefficients Coefficients | | |
+| +---------+-----------+-------------------+ | |
+| | B | Std. Error| Beta | t |Sig.|
++------------------------+---------+-----------+-------------------+-----+----+
+|(Constant) | 9.81| 1.50| .00| 6.54|.000|
+|Mean time between | 3.10| .10| .99|32.43|.000|
+|failures (months) | | | | | |
+|Ratio of working to non-| 1.09| 1.78| .02| .61|.552|
+|working time | | | | | |
++------------------------+---------+-----------+-------------------+-----+----+
+
+ Coefficients (Mean time to repair (hours) )
++-----------------------+------------------------------------------+-----+----+
+| | Unstandardized Standardized | | |
+| | Coefficients Coefficients | | |
+| +---------+------------+-------------------+ | |
+| | B | Std. Error | Beta | t |Sig.|
++-----------------------+---------+------------+-------------------+-----+----+
+|(Constant) | 10.50| .96| .00|10.96|.000|
+|Mean time between | 3.11| .09| .99|33.39|.000|
+|failures (months) | | | | | |
++-----------------------+---------+------------+-------------------+-----+----+
@end example
@end cartouche
@caption{Linear regression analysis to find a predictor for
projects / pspp / blobdiff