X-Git-Url: https://pintos-os.org/cgi-bin/gitweb.cgi?a=blobdiff_plain;f=doc%2Ftutorial.texi;h=c6928c810c187def008b7a9456a62a2f0865fa91;hb=25bc101890a62f780da3a7c6af71ecc3ce09fdb1;hp=a14f98e9f80baeda2dacdc7393833987da73dc18;hpb=9940422df46fc188d960694aade7b83d8306a78a;p=pspp diff --git a/doc/tutorial.texi b/doc/tutorial.texi index a14f98e9f8..c6928c810c 100644 --- a/doc/tutorial.texi +++ b/doc/tutorial.texi @@ -1,3 +1,12 @@ +@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 @@ -85,6 +94,7 @@ The following sections explain how to define a dataset. * 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 @@ -187,12 +197,15 @@ shown along with the data. 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 @@ -286,6 +299,13 @@ separated text, from spreadsheets, databases or other sources. 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 @@ -333,14 +353,16 @@ data and identify the erroneous values. 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 @@ -359,7 +381,7 @@ seemingly bizarre height for an adult person. 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 @@ -369,7 +391,7 @@ negative but a plausible value for the second lowest value. 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 @@ -378,25 +400,24 @@ represent data entry errors. 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 @@ -431,7 +452,7 @@ From now on, they will be ignored in analysis. 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. @@ -484,14 +505,14 @@ A sensible check to perform on survey data is the calculation of 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 @@ -501,37 +522,39 @@ negatively scaled variables) requests reliability statistics for @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. @@ -589,43 +612,66 @@ an appropriate non-parametric test instead of a linear one. 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 @@ -735,28 +781,82 @@ 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. @@ -799,44 +899,33 @@ identifies the potential linear relationship. @xref{REGRESSION}. @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