 1. Exploratory Data Analysis
1.4. EDA Case Studies

## Dataplot Commands for EDA Techniques

This page documents the Dataplot commands that can be used for the graphical and analytical techniques discussed in this chapter. This is only meant to guide you to the appropriate commands. The complete documentation for these commands is available in the Dataplot Reference Manual.

Dataplot Commands for 1-Factor ANOVA The Dataplot command for a one way analysis of variance is
ANOVA Y X
where Y is a response variable and X is a group identifier variable.

Dataplot is currently limited to the balanced case (i.e., each level has the same number of observations) and it does not compute interaction effect estimates.

Dataplot Commands for Multi-Factor ANOVA The Dataplot commands for generating multi-factor analysis of variance are:
ANOVA Y X1
ANOVA Y X1 X2
ANOVA Y X1 X2 X3
ANOVA Y X1 X2 X3 X4
ANOVA Y X1 X2 X3 X4 X5
where Y is the response variable and X1, X2, X3, X4, and X5 are factor variables. Dataplot allows up to 10 factor variables.

Dataplot is currently limited to the balanced case (i.e., each level has the same number of observations) and it does not compute interaction effect estimates.

Dataplot Commands for the Anderson-Darling Test The Dataplot commands for the Anderson-Darling test are
ANDERSON DARLING NORMAL TEST Y
ANDERSON DARLING LOGNORMAL TEST Y
ANDERSON DARLING EXPONENTIAL TEST Y
ANDERSON DARLING WEIBULL TEST Y
ANDERSON DARLING EXTREME VALUE TYPE I TEST Y
where Y is the response variable.

Dataplot Commands for Autocorrelation To generate the lag 1 autocorrelation value in Dataplot, enter
LET A = AUTOCORRELATION Y
where Y is the response variable.

In Dataplot, the easiest way to generate the autocorrelations for lags greater than 1 is:

AUTOCORRELATION PLOT Y
LET AC = YPLOT
LET LAG = XPLOT
RETAIN AC LAG SUBSET TAGPLOT = 1
The AUTOCORRELATION PLOT command generates an autocorrelation plot for lags 0 to N/4. It also generates 95% and 99% confidence limits for the autocorrelations. Dataplot stores the plot coordinates in the internal variables XPLOT, YPLOT, and TAGPLOT. The 2 LET commands and the RETAIN command are used to extract the numerical values of the autocorrelations. The variable LAG identifies the lag while the corresponding row of AC contains the autocorrelation value.

Dataplot Commands for Autocorrelation Plots The command to generate an autocorrelation plot is
AUTOCORRELATION PLOT Y
The appearance of the autocorrelation plot can be controlled by appropriate settings of the LINE, CHARACTER, and SPIKE commands. Dataplot draws the following curves on the autocorrelation plot:
1. The auotocorrelations.
2. A reference line at zero.
3. A reference line at the upper 95% confidence limit.
4. A reference line at the lower 95% confidence limit.
5. A reference line at the upper 99% confidence limit.
6. A reference line at the lower 99% confidence limit.
For example, to draw the autocorrelations as spikes, the zero reference line as a solid line, the 95% lines as dashed lines, and the 99% line as dotted lines, enter the command
LINE BLANK SOLID DASH DASH DOT DOT
CHARACTER BLANK ALL
SPIKE ON OFF OFF OFF OFF OFF
SPIKE BASE 0
By default, the confidence bands are fixed width. This is appropriate for testing for white noise (i.e., randomness). For Box-Jenkins modeling, variable-width confidence bands are more appropriate. Enter the following command for variable-width confidence bands:
SET AUTOCORRELATION BAND BOX-JENKINS
To restore fixed-width confidence bands, enter
SET AUTOCORRELATION BAND WHITE-NOISE

Dataplot Commands for the Bartlett Test The Dataplot command for the Bartlett test is
BARTLETT TEST Y X
where Y is the response variable and X is the group id variable.

The above computes the standard form of Bartlett's test. To compute the Dixon-Massey form of Bartlett's test, the Dataplot command is one of the following (these are synonyms, not distinct commands)

DIXON BARTLETT TEST Y X
DIXON MASSEY BARTLETT TEST Y X
DM BARTLETT TEST Y X

Dataplot Commands for Bihistograms The Dataplot command to generate a bihistogram is
BIHISTOGRAM Y1 Y2
As with the standard histogram, the class width, the lower class limit, and the upper class limit can be controlled with the commands
CLASS WIDTH <value>
CLASS LOWER <value>
CLASS UPPER <value>
In addition, relative bihistograms, cumulative bihistograms, and relative cumulative bihistograms can be generated with the commands
RELATIVE BIHISTOGRAM Y1 Y2
CUMULATIVE BIHISTOGRAM Y1 Y2
RELATIVE CUMULATIVE BIHISTOGRAM Y1 Y2

Dataplot Commands for the Binomial Probability Functions Dataplot can compute the probability functions for the binomial distribution with the following commands.
 cdf LET Y = BINCDF(X,P,N) pdf LET Y = BINPDF(X,P,N) ppf LET Y = BINPPF(F,P,N) random numbers LET N = value LET P = value LET Y = BINOMIAL RANDOM NUMBERS FOR I = 1 1 1000 probability plot LET N = value LET P = value BINOMIAL PROBABILITY PLOT Y
where X can be a number, a parameter, or a variable. P and N are the shape parameters and are required. They can be a number, a parameter, or a variable. They are typically a number or a parameter.

These functions can be used in the Dataplot PLOT and FIT commands as well. For example,

PLOT BINPDF(X,0.5,100) FOR X = 0  1  100

Dataplot Commands for the Block Plot The Dataplot command for the block plot is
BLOCK PLOT    Y    X1 X2 X3 etc.    XP
where
• Y is the response variable,
• X1, X2, X3, etc. are the one or more nuisance (= secondary) factors, and
• XP is the primary factor of interest.
The following commands typically precede the block plot.
CHARACTER 1 2
LINE BLANK BLANK
These commands set the plot character for the primary factor. Although 1 and 2 are useful indicators, the choice of plot character is at the discretion of the user.

Dataplot Commands for the Bootstrap Plot The Dataplot command for the bootstrap plot is
BOOTSTRAP <STAT> PLOT Y

where <STAT> is one of the following:
MEAN
MIDMEAN
MIDRANGE
MEDIAN
TRIMMED MEAN
WINSORIZED MEAN
GEOMETRIC MEAN
HARMONIC MEAN

SUM
PRODUCT
MINIMUM
MAXIMUM

STANDARD DEVIATION
VARIANCE
STANDARD DEVIATION OF MEAN
VARIANCE OF MEAN
RELATIVE STANDARD DEVIATION
RELATIVE VARIANCE
AVERAGE ABSOLUTE DEVIATION
MEDIAN ABSOLUTE DEVIATION
LOWER QUARTILE
LOWER HINGE
UPPER QUARTILE
UPPER HINGE

FIRST DECILE
SECOND DECILE
THIRD DECILE
FOURTH DECILE
FIFTH DECILE
SIXTH DECILE
SEVENTH DECILE
EIGHTH DECILE
NINTH DECILE
PERCENTILE

SKEWNESS
KURTOSIS

AUTOCORRELATION
AUTOCOVARIANCE
SINE FREQUENCY
COSINE FREQUENCY

TAGUCHI SN0
TAGUCHI SN+
TAGUCHI SN-
TAGUCHI SN00

The BOOTSTRAP PLOT command is almost always followed by a histogram or some other distributional plot.

Dataplot automatically stores the following internal parameters after a BOOTSTRAP PLOT command:

BMEAN - mean of the plotted bootstrap values
BSD - standard deviation of the plotted bootstrap values
B001 - the 0.1 percentile of the plotted bootstrap values
B005 - the 0.5 percentile of the plotted bootstrap values
B01 - the 1.0 percentile of the plotted bootstrap values
B025 - the 2.5 percentile of the plotted bootstrap values
B05 - the 5.0 percentile of the plotted bootstrap values
B10 - the 10 percentile of the plotted bootstrap values
B20 - the 20 percentile of the plotted bootstrap values
B80 - the 80 percentile of the plotted bootstrap values
B90 - the 90 percentile of the plotted bootstrap values
B95 - the 95 percentile of the plotted bootstrap values
B975 - the 97.5 percentile of the plotted bootstrap values
B99 - the 99 percentile of the plotted bootstrap values
B995 - the 99.5 percentile of the plotted bootstrap values
B999 - the 99.9 percentile of the plotted bootstrap values
These internal parameters are useful for generating confidence intervals and can be printed (PRINT BMEAN) or used as any user-defined parameter could (e.g., LET UCL = B95).

To specify the number of bootstrap subsamples to use, enter the command

BOOTSTRAP SAMPLE <N>
where <N> is the number of samples you want. The default is 500 (it may be 100 in older implementations).

Dataplot can also generate bootstrap estimates for statistics that are not directly supported. The following example shows a bootstrap calculation for the mean of 500 normal random numbers. Although we can do this directly in Dataplot, this demonstrates the steps necessary for an unsupported statistic. The subsamples are generated with a loop. The BOOTSTRAP INDEX and BOOTSTRAP SAMPLE commands generate a single subsample which is stored in Y2. The desired statistic is then calculated for Y2 and the result stored in an array. After the loop, the array XMEAN contains the 100 mean values.

LET Y = NORMAL RANDOM NUMBERS FOR I = 1 1 500
LET N = SIZE Y
LOOP FOR K = 1 1 500
LET IND = BOOTSTRAP INDEX FOR I = 1 1 N
LET Y2 = BOOTSTRAP SAMPLE Y IND
LET A = MEAN Y2
LET XMEAN(K) = A
END OF LOOP
HISTOGRAM XMEAN

Dataplot Command for the Box-Cox Linearity Plot The Dataplot command to generate a Box-Cox linearity plot is
BOX-COX LINEARITY PLOT Y X
where Y and X are the response variables.

Dataplot Command for the Box-Cox Normality Plot The Dataplot command to generate a Box-Cox normality plot is
BOX-COX NORMALITY PLOT Y
where Y is the response variable.

Dataplot Commands for the Boxplot The Dataplot command to generate a boxplot is
BOX PLOT Y X
The BOX PLOT command is usually preceded by the commands
CHARACTER BOX PLOT
LINE BOX PLOT
These commands set the default line and character settings for the box plot. You can use the CHARACTER and LINE commands to choose your own line and character settings if you prefer.

To show the outliers as circles, enter the command

FENCES ON

Dataplot Commands for the Cauchy Probability Functions Dataplot can compute the probability functions for the Cauchy distribution with the following commands.
 cdf LET Y = CAUCDF(X,A,B) pdf LET Y = CAUPDF(X,A,B) ppf LET Y = CAUPPF(X,A,B) hazard LET Y = CAUHAZ(X,A,B) cumulative hazard LET Y = CAUCHAZ(X,A,B) survival LET Y = 1 - CAUCDF(X,A,B) inverse survival LET Y = CAUPPF(1-X,A,B) random numbers LET Y = CAUCHY RANDOM NUMBERS FOR I = 1 1 1000 probability plot CAUCHY PROBABILITY PLOT Y
where X can be a number, a parameter, or a variable. A and B are the location and scale parameters and they are optional (a location of 0 and scale of 1 are used if they are omitted). If given, A and B can be a number, a parameter, or a variable. However, they are typically either a number or a parameter.

These functions can be used in the Dataplot PLOT and FIT commands as well. For example,

PLOT CAUPDF(X) FOR X = -5  0.01  5

Dataplot Commands for the Chi-Square Probability Functions Dataplot can compute the probability functions for the chi-square distribution with the following commands.
 cdf LET Y = CHSCDF(X,NU,NU2,A,B) pdf LET Y = CHSPDF(X,NU,A,B) ppf LET Y = CHSPPF(X,NU,A,B) random numbers LET NU = value LET Y = CHI-SQUARE RANDOM NUMBERS FOR I = 1 1 1000 probability plot LET NU = value CHI-SQUARE PROBABILITY PLOT Y ppcc plot LET NU = value CHI-SQUARE PPCC PLOT Y
where X can be a number, a parameter, or a variable. NU is the shape parameter (number of degrees of freedom). NU can be a number, a parameter, or a variable. However, it is typically either a number or a parameter. A and B are the location and scale parameters and they are optional (a location of 0 and scale of 1 are used if they are omitted). If given, A and B can be a number, a parameter, or a variable. However, they are typically either a number or a parameter.

These functions can be used in the Dataplot PLOT and FIT commands as well. For example,

PLOT CHSPDF(X,5) FOR X = 0  0.01  5

Dataplot Commands for the Chi-Square Goodness of Fit Test The Dataplot commands for the chi-square goodness of fit test are
<dist> CHI-SQUARE GOODNESS OF FIT TEST Y
<dist> CHI-SQUARE GOODNESS OF FIT TEST Y X
<dist> CHI-SQUARE GOODNESS OF FIT TEST Y XL XU
where <dist> is one of 70+ built-in distributions. Dataplot supports the chi-square goodness-of-fit test for all distributions that support the cumulative distribution function. To see a list of supported distributions, enter the command LIST DISTRIBUTIONS. Some specific examples are
NORMAL CHI-SQUARE GOODNESS OF FIT TEST Y
LOGISTIC CHI-SQUARE GOODNESS OF FIT TEST Y
DOUBLE EXPONENTIAL CHI-SQUARE GOODNESS OF FIT TEST Y
You can specify the location and scale parameters (for any of the supported distributions) by entering
LET CHSLOC = value
LET CHSSCAL = value
You may need to enter the values for 1 or more shape parameters for distributions that require them. For example, to specify the shape parameter gamma for the gamma distribution, enter the commands
LET GAMMA = value
GAMMA CHI-SQUARE GOODNESS OF FIT TEST Y
Dataplot also allows you to control the class width, the lower limit (i.e., start of the first bin), and the upper limit (i.e., the end value for the last bin). These commands are
CLASS WIDTH value
CLASS LOWER value
CLASS UPPER value
In most cases, the default Dataplot class intervals will be adequate.

NORMAL CHI-SQUARE GOODNESS OF FIT TEST Y X
NORMAL CHI-SQUARE GOODNESS OF FIT TEST Y XL XU
In both commands above, Y is the frequency variable. If one X variable is given, Dataplot assumes that it is the bin mid point and that bins have equal width. If two X variables are given, Dataplot assumes that these are the bin end points and that the bin widths are not necessarily of equal width. Unequal bin widths are typically used to combine classes with small frequencies since the chi-square approximation for the test may not be accurate if there are frequency classes with less than five observations.

Dataplot Command for the Chi-Square Test for the Standard Deviation The Dataplot command for the chi-square test for the standard deviation is
CHI-SQUARE TEST Y A
where Y is the response variable and A is the value being tested.

Dataplot Command for Complex Demodulation Amplitude Plot The Dataplot command for a complex demodulation amplitude plot is
COMPLEX DEMODULATION AMPLITUDE PLOT Y
where Y is the response variable.

Dataplot Commands for Complex Demodulation Phase Plot The Dataplot commands for a complex demodulation phase plot are
DEMODULATION FREQUENCY <VALUE>
COMPLEX DEMODULATION PHASE PLOT Y
where Y is the response variable. The DEMODULATION FREQUENCY is used to specify the desired frequency for the COMPLEX DEMODULATION PLOT. The value of the demodulation frequency is usually obtained from a spectral plot.

Dataplot Commands for Conditioning Plot The Dataplot command to generate a conditioning plot is
• CONDITION PLOT Y X COND
Y is the response variable, X is the independent variable, and COND is the conditioning variable. Dataplot expects COND to contain a discrete number of distinct values. Dataplot provides a number of commands for creating a discrete variable from a continuous variable. For example, suppose X2 is a continuous variable that we want to split into 4 regions. We could enter the following sequence of commands to create a discrete variable from X2.
LET COND = X2
LET COND = 1 SUBSET X2 = 0 TO 99.99
LET COND = 2 SUBSET X2 = 100 TO 199.99
LET COND = 3 SUBSET X2 = 200 TO 299.99
LET COND = 4 SUBSET X2 = 300 TO 400
The SUBSET feature can be used as above to create whatever ranges we want. A simpler, more automatic way is to use the CODE command in Dataplot. For example,
LET COND = CODE4 X2
splits the data into quartiles and assigns a value of 1 to 4 to COND based on what quartile the corresponding value of X2 is in.

The appearance of the plot can be controlled by appropriate settings of the CHARACTER and LINE commands and their various attribute setting commands.

In addition, Dataplot provides a number of SET commands to control the appearance of the conditioning plot. In Dataplot, enter HELP CONDITION PLOT for details.

Dataplot Commands for Confidence Limits and One Sample t-test The following commands can be used in Dataplot to generate a confidence interval for the mean or to generate a one sample t-test, respectively.
CONFIDENCE LIMITS Y
T TEST Y U0
where Y is the response variable and U0 is a parameter or scalar value that defines the hypothesized value.

Dataplot Commands for Contour Plots The Dataplot command for generating a contour plot is
CONTOUR PLOT Z X Y Z0
The variables X and Y define the grid, the Z variable is the response variable, and Z0 defines the desired contour levels. Currently, Dataplot only supports contour plots over regular grids. Dataplot does provide 2D interpolation capabilities to form regular grids from irregular data. Dataplot also does not support labels for the contour lines or solid fills between contour lines.

Dataplot Commands for Control Charts The Dataplot commands for generating control charts are
XBAR CONTROL CHART Y X
R CONTROL CHART Y X
S CONTROL CHART Y X
C CONTROL CHART Y X
U CONTROL CHART Y X
P CONTROL CHART Y X
NP CONTROL CHART Y X
CUSUM CONTROL CHART Y X
EWMA CONTROL CHART Y X
MOVING AVERAGE CONTROL CHART Y
MOVING AVERAGE CONTROL CHART Y X
MOVING RANGE CONTROL CHART Y
MOVING RANGE CONTROL CHART Y X
MOVING SD CONTROL CHART Y
MOVING SD CONTROL CHART Y X
where Y is the response variable and X is the group identifier variable.

Dataplot computes the control limits. In some cases, you may have pre determined values to put in as control limits (e.g., based on historical data). Dataplot allows you to specify these limits by entering the following commands before the control chart command.

LET TARGET = <value>
LET LSL = <value>
LET USL = <value>
These allow you to specify the target, lower specification, and upper specification limit respectively.

The appearance of the plot can be controlled by appropriate settings of the LINE and CHARACTER commands. Specifically, there are seven settings:

1. the response curve
2. the reference line at the Dataplot determined target value
3. the reference line at the Dataplot determined upper specification limit
4. the reference line at the Dataplot determined lower specification limit
5. the reference line at the user-specified target value
6. the reference line at the user-specified upper specification limit
7. the reference line at the user-specified lower specification limit

Dataplot Commands for DEX Contour Plots The Dataplot command for generating a linear dex contour plot is
DEX CONTOUR PLOT Y X1 X2 Y0
The variables X1 and X2 are the two factor variables, Y is the response variable, and Y0 defines the desired contour levels.

Dataplot does not have a built-in quadratic dex contour plot. However, the macro DEXCONTQ.DP will generate a quadratic dex contour plot. Enter LIST DEXCONTQ.DP for more information.

Dataplot Commands for DEX Interaction Effects Plots The Dataplot command to generate a dex mean interaction effects plot is
DEX MEAN INTERACTION EFFECTS PLOT Y X1 X2 X3 X4 X5
where Y is the response variable and X1, X2, X3, X4, and X5 are the factor variables. The number of factor variables can vary, and is at least one.

Dataplot supports the following additional plots for other location statistics

DEX MEDIAN INTERACTION EFFECTS PLOT Y X1 X2 X3 X4 X5
DEX MIDMEAN INTERACTION EFFECTS PLOT Y X1 X2 X3 X4 X5
DEX TRIMMED MEAN INTERACTION EFFECTS PLOT Y X1 X2 X3 X4 X5
DEX WINSORIZED MEAN INTERACTION EFFECTS PLOT Y X1 X2 X3 X4 X5
If you want the raw data plotted rather than a statistic, enter
DEX INTERACTION EFFECTS PLOT Y X1 X2 X3 X4 X5
The LINE and CHARACTER commands can be used to control the appearance of the plot. For example, a typical sequence of commands might be
LINE SOLID SOLID
CHARACTER CIRCLE BLANK
CHARACTER FILL ON
This draws the connecting line between the levels of a factor and the overall mean reference line as solid lines. In addition, the level means are drawn with a solid fill circle.

This command is a variant of the SCATTER PLOT MATRIX command. There are a number of options to control the appearance of these plots. In Dataplot, you can enter HELP SCATTER PLOT MATRIX for details.

Dataplot Commands for DEX Mean Plots The Dataplot command to generate a dex mean plot is
DEX MEAN PLOT Y X1 X2 X3 X4 X5
where Y is the response variable and X1, X2, X3, X4, and X5 are the factor variables. The number of factor variables can vary, and is at least one.

Dataplot supports the following additional plots for other location statistics

DEX MEDIAN PLOT Y X1 X2 X3 X4 X5
DEX MIDMEAN PLOT Y X1 X2 X3 X4 X5
DEX TRIMMED MEAN PLOT Y X1 X2 X3 X4 X5
DEX WINSORIZED MEAN PLOT Y X1 X2 X3 X4 X5
The LINE and CHARACTER commands can be used to control the appearance of the plot. For example, a typical sequence of commands might be
LINE SOLID SOLID
CHARACTER CIRCLE BLANK
CHARACTER FILL ON
This draws the connecting line between the levels of a factor and the overall mean reference line as solid lines. In addition, the level means are drawn with a solid fill circle.

It is often desirable to provide alphabetic labels for the factors. For example, if there are 2 factors, time and temperature, the following commands could be used to define alphabetic labels:

XLIMITS 1 2
XTIC OFFSET 0.5 0.5
MAJOR XTIC MARK NUMBER 2
MINOR XTIC MARK NUMBER 0
XTIC MARK LABEL FORMAT ALPHA
XTIC MARK LABEL CONTENT TIME TEMPERATURE

Dataplot Commands for a DEX Scatter Plot The Dataplot command for generating a dex scatter plot is
DEX SCATTER PLOT Y X1 X2 X3 X4 X5
where Y is the response variable and X1, X2, X3, X4, and X5 are the factor variables. The number of factor variables can vary, and is at least one.

The DEX SCATTER PLOT is typically preceded by the commands

CHARACTER X BLANK
LINE BLANK SOLID
However, you can set the plot character and line settings to whatever seems appropriate.

It is often desirable to provide alphabetic labels for the factors. For example, if there are 2 factors, time and temperature, the following commands could be used to define alphabetic labels:

XLIMITS 1 2
XTIC OFFSET 0.5 0.5
MAJOR XTIC MARK NUMBER 2
MINOR XTIC MARK NUMBER 0
XTIC MARK LABEL FORMAT ALPHA
XTIC MARK LABEL CONTENT TIME TEMPERATURE

Dataplot Commands for a DEX Standard Deviation Plot The Dataplot command to generate a dex standard deviation plot is
DEX STANDARD DEVIATION PLOT Y X1 X2 X3 X4 X5
where Y is the response variable and X1, X2, X3, X4, and X5 are the factor variables. The number of factor variables can vary, and is at least one.

Dataplot supports the following additional plots for other scale statistics.

DEX VARIANCE PLOT Y X1 X2 X3 X4 X5
DEX MEDIAN ABSOLUTE VALUE PLOT Y X1 X2 X3 X4 X5
DEX AVERAGE ABSOLUTE VALUE PLOT Y X1 X2 X3 X4 X5
DEX RANGE VALUE PLOT Y X1 X2 X3 X4 X5
DEX MIDRANGE VALUE PLOT Y X1 X2 X3 X4 X5
DEX MINIMUM PLOT Y X1 X2 X3 X4 X5
DEX MAXIMUM PLOT Y X1 X2 X3 X4 X5
The LINE and CHARACTER commands can be used to control the appearance of the plot. For example, a typical sequence of commands might be
LINE SOLID SOLID
CHARACTER CIRCLE BLANK
CHARACTER FILL ON
This draws the connecting line between the levels of a factor and the overall mean reference line as solid lines. In addition, the level means are drawn with a solid fill circle.

It is often desirable to provide alphabetic labels for the factors. For example, if there are 2 factors, time and temperature, the following commands could be used to define alphabetic labels:

XLIMITS 1 2
XTIC OFFSET 0.5 0.5
MAJOR XTIC MARK NUMBER 2
MINOR XTIC MARK NUMBER 0
XTIC MARK LABEL FORMAT ALPHA
XTIC MARK LABEL CONTENT TIME TEMPERATURE

Dataplot Commands for the Double Exponential Probability Functions Dataplot can compute the probability functions for the double exponential distribution with the following commands.
 cdf LET Y = DEXCDF(X,A,B) pdf LET Y = DEXPDF(X,A,B) ppf LET Y = DEXPPF(X,A,B) hazard LET Y = DEXHAZ(X,A,B)/(1 - DEXCDF(X,A,B)) cumulative hazard LET Y = -LOG(1 - DEXCHAZ(X,A,B)) survival LET Y = 1 - DEXCDF(X,A,B) inverse survival LET Y = DEXPPF(1-X,A,B) random numbers LET Y = DOUBLE EXPONENTIAL RANDOM NUMBERS FOR I = 1 1 1000 probability plot DOUBLE EXPONENTIAL PROBABILITY PLOT Y maximum likelihood LET MU = MEDIAN Y LET BETA = MEDIAN ABSOLUTE DEVIATION Y
where X can be a number, a parameter, or a variable. A and B are the location and scale parameters and they are optional (a location of 0 and scale of 1 are used if they are omitted). If given, A and B can be a number, a parameter, or a variable. However, they are typically either a number or a parameter.

These functions can be used in the Dataplot PLOT and FIT commands as well. For example,

PLOT DEXPDF(X) FOR X = -5  0.01  5

Dataplot Command for Confidence Interval for the Difference Between Two Proportions The Dataplot command for a confidence interval for the difference of two proportions is
DIFFERENCE OF PROPORTIONS CONFIDENCE INTERVAL Y1 Y2
where Y1 contains the data for sample 1 and Y2 contains the data for sample 2. For large samples, Dataplot uses the binomial computation, not the normal approximation.

The following command sets the lower and upper bounds that define a success in the response variable

ANOP LIMITS <lower bound> <upper bound>

Dataplot Command for Duane Plot The Dataplot command for a Duane plot is
DUANE PLOT Y
where Y is a response variable containing failure times.

Dataplot Command for Starting Values for Rational Function Models Starting values for a rational function model can be obtained by fitting an exact rational function to a subset of the original data. The number of points in the subset should equal the number of parameters to be estimated in the rational function model. The EXACT RATIONAL FIT can be used to fit this subset model and thus to provide starting values for the rational function model. For example, to fit a quadratic/quadratic rational function model to data in X and Y, you might do something like the following.

LET X2 = DATA 12 17 22 34 56
LET Y2 = DATA 7 9 6 19 23
EXACT 2/2 FIT Y2 X2 Y X
FIT Y = (A0 + A1*X + A2*X**2)/(1 + B1*X + B2*X**2)
The DATA command is used to define the subset variables and EXACT 2/2 FIT is used to fit the exact rational function. The "2/2" identifies the degree of the numerator as 2 and the degree of the denominator as 2. It provides values for A0, A1, A2, B1, and B2, which are used to fit the rational function model for the full data set.

Dataplot Commands for the Exponential Probability Functions Dataplot can compute the probability functions for the exponential distribution with the following commands.
 cdf LET Y = EXPCDF(X,A,B) pdf LET Y = EXPPDF(X,A,B) ppf LET Y = EXPPPF(X,A,B) hazard LET Y = EXPHAZ(X,A,B) cumulative hazard LET Y = EXPCHAZ(X,A,B) survival LET Y = 1 - EXPCDF(X,A,B) inverse survival LET Y = EXPPPF(1-X,A,B) random numbers LET Y = EXPONENTIAL RANDOM NUMBERS FOR I = 1 1 1000 probability plot EXPONENTIAL PROBABILITY PLOT Y parameter estimation If your data are not censored, enter the commands SET CENSORING TYPE NONE EXPONENTIAL MLE Y If your data have type 1 censoring at fixed time t0, enter the commands LET TEND = censoring time SET CENSORING TYPE 1 EXPONENTIAL MLE Y X If your data have type 2 censoring, enter the commands SET CENSORING TYPE 2 EXPONENTIAL MLE Y X Y is the response variable and X is the censoring variable where a value of 1 indicates a failure time and a value of 0 indicates a censoring time. In addition to the point estimates, confidence intervals for the parameters are generated.
In the above, X can be a number, a parameter, or a variable. A and B are the location and scale parameters and they are optional (a location of 0 and scale of 1 are used if they are omitted). If given, A and B can be a number, a parameter, or a variable. However, they are typically either a number or parameter.

These functions can be used in the Dataplot PLOT and FIT commands as well. For example,

PLOT EXPPDF(X) FOR X = 0  0.01  4

Dataplot Command for Generalized ESD Test The Dataplot command for the generalized ESD (Extreme Studentized Deviate) test is
LET NOUTLIER = <value>
EXTREME STUDENTIZED DEVIATE TEST Y
where Y is the response variable and NOUTLIER specifies the upper bound on the number of outliers to test.

Dataplot Commands for the Extreme Value Type I (Gumbel) Distribution To specify the form of the Gumbel distribution based on the smallest value, enter the command
SET MINMAX 1
To specify the form of the Gumbel distribution based on the largest value, enter the command
SET MINMAX 2
One of these commands must be entered before using the commands below.

Dataplot can compute the probability functions for the extreme value type I distribution with the following commands.

 cdf LET Y = EV1CDF(X,A,B) pdf LET Y = EV1PDF(X,A,B) ppf LET Y = EV1PPF(X,A,B) hazard LET Y = EV1HAZ(X,A,B) cumulative hazard LET Y = EV1CHAZ(X,A,B) survival LET Y = 1 - EV1CDF(X,A,B) inverse survival LET Y = EV1PPF(1-X,A,B) random numbers LET Y = EXTREME VALUE TYPE 1 RANDOM NUMBERS FOR I = 1 1 1000 probability plot EXTREME VALUE TYPE 1 PROBABILITY PLOT Y maximum likelihood EV1 MLE Y This returns a point estimate for the full sample case. It does not provide confidence intervals for the parameters and it does not handle censored data.
In the above, X can be a number, a parameter, or a variable. A and B are the location and scale parameters and they are optional (a location of 0 and scale of 1 are used if they are omitted). If given, A and B can be a number, a parameter, or a variable. However, they are typically either a number or a parameter.

These functions can be used in the Dataplot PLOT and FIT commands as well. For example,

SET MINMAX 1
PLOT EV1PDF(X) FOR X = -4  0.01  4

Dataplot Commands for the F Distribution Probability Functions Dataplot can compute the probability functions for the F distribution with the following commands.
 cdf LET Y = FCDF(X,NU1,NU2,A,B) pdf LET Y = FPDF(X,NU1,NU2,A,B) ppf LET Y = FPPF(X,NU1,NU2,A,B) random numbers LET NU1 = value LET NU2 = value LET Y = F RANDOM NUMBERS FOR I = 1 1 1000 probability plot LET NU1 = value LET NU2 = value F PROBABILITY PLOT Y
where X can be a number, a parameter, or a variable. NU1 and NU2 are the shape parameters (= number of degrees of freedom). NU1 and NU2 can be a number, a parameter, or a variable. However, they are typically either a number or a parameter. A and B are the location and scale parameters and they are optional (a location of 0 and scale of 1 are used if they are omitted). If given, A and B can be a number, a parameter, or a variable. However, they are typically either a number or a parameter.

These functions can be used in the Dataplot PLOT and FIT commands as well. For example,

PLOT FPDF(X,10,10) FOR X = 0  0.01  5

Dataplot Command for F Test for Equality of Two Standard Deviations The Datpalot command for the F test for the equality of two standard deviations is
F TEST Y1 Y2
where Y1 is the data for sample one and Y2 is the data for sample two.

Dataplot Commands for the Histogram The Dataplot command to generate a histogram is
HISTOGRAM Y
where Y is the response variable. The different variants of the histogram can be generated with the commands
RELATIVE HISTOGRAM Y
CUMULATIVE HISTOGRAM Y
RELATIVE CUMULATIVE HISTOGRAM Y
The class width, the start of the first class, and the end of the last class can be specified with the commands
CLASS WIDTH <value>
CLASS LOWER <value>
CLASS UPPER <value>
By default, Dataplot uses a class width of 0.3*SD where SD is the standard deviation of the data. The lower class limit is the sample mean minus 6 times the sample standard deviation. Similarly, the upper class limit is the sample mean plus 6 times the sample standard deviation.

By default, Dataplot uses the probability normalization for relative histograms. If you want the relative counts to sum to one instead, enter the command

SET RELATIVE HISTOGRAM PERCENT
To reset the probability interpretation, enter
SET RELATIVE HISTOGRAM AREA

Dataplot Commands for a Lag Plot The Dataplot command to generate a lag plot is
LAG PLOT Y
The appearance of the lag plot can be controlled with appropriate settings for the LINE and CHARACTER commands. Typical settings for these commands would be
LINE BLANK
CHARACTER X
To generate a linear fit of the points on the lag plot when an autoregressive fit is suggested, enter the following commands
LAG PLOT Y
LINEAR FIT YPLOT XPLOT
The variables YPLOT and XPLOT are internal variables that store the coordinates of the most recent plot.

Dataplot Commands for the Fatigue Life Probability Functions Dataplot can compute the probability functions for the fatigue life distribution with the following commands.
 cdf LET Y = FLCDF(X,GAMMA,A,B) pdf LET Y = FLPDF(X,GAMMA,A,B) ppf LET Y = FLPPF(X,GAMMA,A,B) hazard LET Y = FLHAZ(X,GAMMA,A,B) cumulative hazard LET Y = FLCHAZ(X,GAMMA,A,B) survival LET Y = 1 - FLCDF(X,GAMMA,A,B) inverse survival LET Y = FLPPF(1-X,GAMMA,A,B) random numbers LET GAMMA = value LET Y = FATIGUE LIFE RANDOM NUMBERS FOR I = 1 1 1000 probability plot LET GAMMA = value FATIGUE LIFE PROBABILITY PLOT Y ppcc plot LET GAMMA = value FATIGUE LIFE PPCC PLOT Y
where X can be a number, a parameter, or a variable. FLMA is the shape parameter and is required. It can be a number, a parameter, or a variable. It is typically a number or a parameter. A and B are the location and scale parameters and they are optional (a location of 0 and scale of 1 are used if they are omitted). If given, A and B can be a number, a parameter, or a variable. However, they are typically either a number or a parameter.

These functions can be used in the Dataplot PLOT and FIT commands as well. For example,

PLOT FLPDF(X,2) FOR X = 0.01  0.01  10

Dataplot Command for Fitting Dataplot can generate both linear and nonlinear fit commands.

For example, to generate a linear fit of Y versus X1, X2, and X3, the command is:

FIT Y X1 X2 X3
To generate quadratic and cubic fits of Y versus X, the commands are:
CUBIC FIT Y X
Nonlinear fits are generated by entering an equation. For example,
FIT Y = A*(EXP(-B*X/10) - EXP(-X/10))
FIT Y = C/(1+C*A*X**B)
FIT Y = A - B*X - ATAN(C/(X-D))/3.14159
In the above equations, there are variables (X and Y), parameters (A, B, C, and D), and constants (10 and 3.14159). The FIT command estimates values for the parameters. If you have a parameter that you do not want estimated, enter it as a constant or with the "^" (e.g., FIT Y = ^C/(1+^C*A*X**B). The "^" substitutes the value of a parameter into a command.

You can also define a function and then fit the function. For example,

LET FUNCTION F = C/(1+C*A*X**B)
FIT Y = F

Dataplot Commands for the Gamma Probability Functions Dataplot can compute the probability functions for the gamma distribution with the following commands.
 cdf LET Y = GAMCDF(X,GAMMA,A,B) pdf LET Y = GAMPDF(X,GAMMA,A,B) ppf LET Y = GAMPPF(X,GAMMA,A,B) hazard LET Y = GAMHAZ(X,GAMMA,A,B) cumulative hazard LET Y = GAMCHAZ(X,GAMMA,A,B) survival LET Y = 1 - GAMCDF(X,GAMMA,A,B) inverse survival LET Y = GAMPPF(1-X,GAMMA,A,B) random numbers LET GAMMA = value LET Y = Gamma RANDOM NUMBERS FOR I = 1 1 1000 probability plot LET GAMMA = value Gamma PROBABILITY PLOT Y ppcc plot LET GAMMA = value Gamma PPCC PLOT Y maximum likelihood GAMMA MLE Y This returns a point estimate for the full-sample case. It does not provide confidence intervals for the parameters and it does not handle censored data.
where X can be a number, a parameter, or a variable. GAMMA is the shape parameter and is required. It can be a number, a parameter, or a variable. It is typically a number or a parameter. A and B are the location and scale parameters and they are optional (a location of 0 and scale of 1 are used if they are omitted). If given, A and B can be a number, a parameter, or a variable. However, they are typically either a number or a parameter.

These functions can be used in the Dataplot PLOT and FIT commands as well. For example,

PLOT GAMPDF(X,2) FOR X = 0.01  0.01  10

Dataplot Command for Grubbs' Test The Dataplot command for Grubbs' test is
GRUBBS <MINIMUM/MAXIMUM> TEST Y
where Y is the response variable. Dataplot identifies one outlier at a time. The MINIMUM or MAXIMUM keyword is optional. If omitted, the most extreme value will be checked (regardless of whether it is in the minimum or maximum direction).

Dataplot Commands for Hazard Plots The Dataplot commands for hazard plots are
EXPONENTIAL HAZARD PLOT Y X
NORMAL HAZARD PLOT Y X
LOGNORMAL HAZARD PLOT Y X
WEIBULL HAZARD PLOT Y X
where Y is a response variable containing failure times and X is a censoring variable (0 means failure time, 1 means censoring time).

Dataplot Command for Kruskal-Wallis Test The Dataplot command for a Kruskal-Wallis test is
KRUSKAL WALLIS TEST Y X
where Y is the response variable and X is the group identifier variable.

Dataplot Commands for the Kolmogorov Smirnov Goodness-of-Fit Test The Dataplot command for the Kolmogorov-Smirnov goodness-of-fit test is
<dist> KOLMOGOROV-SMIRNOV GOODNESS OF FIT TEST Y
where <dist> is one of 60+ built-in distributions. The K-S goodness of fit test is supported for all Dataplot internal continuous distributions that support the CDF (cumulative distribution function). The command LIST DISTRIBUTIONS shows the currently supported distributions in Dataplot. Some specific examples are
NORMAL KOLM-SMIR GOODNESS OF FIT Y
LOGISTIC KOLM-SMIR GOODNESS OF FIT Y
DOUBLE EXPONENTIAL KOLM-SMIR GOODNESS OF FIT Y
You can specify the location and scale parameters by entering
LET KSLOC = value
LET KSSCALE = value
You may need to enter the values for 1 or more shape parameters for distributions that require them. For example, to specify the shape parameter gamma for the gamma distribution, enter the commands
LET GAMMA = value
GAMMA KOLMOGOROV-SMIRNOV GOODNESS OF FIT TEST Y
Be aware that you should not use the same data to estimate these distributional parameters as you use to calculate the K-S test as the critical values of the K-S test assume the distribution is fully specified.

The empirical cdf function can be plotted with the following command

EMPIRICAL CDF PLOT Y

Dataplot Commands for Least Squares Estimation of Distributional Parameters The following example shows how to use Dataplot to obtain least squares estimates for data generated from a Weibull distribution.
. Generate some Weibull data
SET MINMAX MIN
LET GAMMA = 5
LET Y = WEIBULL RAND NUMB FOR I = 1 1 1000
. Bin the data
SET RELATIVE HISTOGRAM AREA
RELATIVE HISTOGRAM Y
LET ZY = YPLOT
LET ZX = XPLOT
RETAIN ZY ZX SUBSET YPLOT > 0
. Specify some starting values
LET SHAPE = 3
LET LOC = MINIMUM Y
LET SCALE = 1
. Now perform the least squares fit
FIT ZY = WEIPDF(ZX,SHAPE,LOC,SCALE)
The RELATIVE HISTOGRAM generates a relative histogram. The command SET RELATIVE HISTOGRAM specifies that the relative histogram is created so that the area under the histogram is 1 (i.e., the integral is 1) rather than the sum of the bars equaling 1. This effectively makes the relative histogram an estimator of the underlying density function. Dataplot saves the coordinates of the histogram in the internal variables XPLOT and YPLOT. The SUBSET command eliminates zero frequency classes. The FIT command then performs the least squares fit.

The same general approach can be used to compute least squares estimates for any distribution for which Dataplot has a pdf function. The primary difficulty with the least squares fitting is that it can be quite sensitive to starting values. For distributions with no shape parameters, the probability plot can be used to determine starting values for the location and scale parameters. For distributions with a single shape parameter, the ppcc plot can be used to determine a starting value for the shape parameter and the probability plot used to determine starting values for the location and scale parameters.

The approach above can be used in any statistical software package that provides non-linear least squares fitting and a method for defining the probability density function (either built-in or user definable).

Dataplot Command for Levene's Test The Dataplot command for the Levene test is
LEVENE TEST Y X
where Y is the response variable and X is the group id variable.

Dataplot Command for the Linear Correlation Plot The Dataplot command to generate a linear correlation plot is
LINEAR CORRELATION PLOT Y X TAG
where Y is the response variable, X is the independent variable, and TAG is the group id variable.

The appearance of the plot can be controlled with appropriate settings for the LINE and CHARACTER commands. Typical settings would be

CHARACTER X BLANK
LINE BLANK SOLID

Dataplot Command for the Linear Intercept Plot The Dataplot command to generate a linear intercept plot is
LINEAR INTERCEPT PLOT Y X TAG
where Y is the response variable, X is the independent variable, and TAG is the group id variable.

The appearance of the plot can be controlled with appropriate settings for the LINE and CHARACTER commands. Typical settings would be

CHARACTER X BLANK
LINE BLANK SOLID

Dataplot Command for the Linear Slope Plot The Dataplot command to generate a linear slope plot is
LINEAR SLOPE PLOT Y X TAG
where Y is the response variable, X is the independent variable, and TAG is the group id variable.

The appearance of the plot can be controlled with appropriate settings for the LINE and CHARACTER commands. Typical settings would be

CHARACTER X BLANK
LINE BLANK SOLID

Dataplot Command for the Linear Residual Standard Deviation Plot The Dataplot command to generate a linear residual standard deviation plot is
LINEAR RESSD PLOT Y X TAG
where Y is the response variable, X is the independent variable, and TAG is the group id variable.

The appearance of the plot can be controlled with appropriate settings for the LINE and CHARACTER commands. Typical settings would be

CHARACTER X BLANK
LINE BLANK SOLID

Dataplot Commands for Measures of Location Various measures of location can be computed in Dataplot as follows:
LET A = MEAN Y
LET A = MEDIAN Y
LET A = MIDMEAN Y

LET P1 = 10
LET P2 = 10
LET A = TRIMMED MEAN Y

LET P1 = 10
LET P2 = 10
LET A = WINSORIZED Y
In the above, P1 and P2 are used to set the percentage of values that are trimmed or Winsorized. Use P1 to set the percentage for the lower tail and P2 the percentage for the upper tail.

Dataplot Commands for the Lognormal Probability Functions Dataplot can compute the probability functions for the lognormal distribution with the following commands.
 cdf LET Y = LGNCDF(X,SD,A,B) pdf LET Y = LGNPDF(X,SD,A,B) ppf LET Y = LGNPPF(X,SD,A,B) hazard LET Y = LGNHAZ(X,SD,A,B) cumulative hazard LET Y = LGNCHAZ(X,SD,A,B) survival LET Y = 1 - LGNCDF(X,SD,A,B) inverse survival LET Y = LGNPPF(1-X,SD,A,B) random numbers LET SD = value LET Y = LOGNORMAL RANDOM NUMBERS FOR I = 1 1 1000 probability plot LET SD = value LOGNORMAL PROBABILITY PLOT Y ppcc plot LET SD = value LOGNORMAL PPCC PLOT Y parameter estimation LOGNORMAL MLE Y This returns point estimates for the shape and scale parameters. It does not handle censored data and it does not generate confidence intervals for the parameters.
where X can be a number, a parameter, or a variable. SD is the shape parameter and is optional. It can be a number, a parameter, or a variable. It is typically a number or a parameter. A and B are the location and scale parameters and they are optional (a location of 0 and scale of 1 are used if they are omitted). If given, A and B can be a number, a parameter, or a variable. However, they are typically either a number or a parameter.

These functions can be used in the Dataplot PLOT and FIT commands as well. For example,

PLOT LGNPDF(X,5) FOR X = 0.01  0.01  5

Dataplot Commands for Maximum Likelihood Estimation for Distributions Dataplot performs maximum likelihood estimation for a few specific distributions as documented in the table below. Unless specified otherwise, censored data are not supported and only point estimates are generated (i.e., no confidence intervals for the parameters). For censored data, create an id variable that is equal to 1 for a failure time and equal to 0 for a censoring time. Type I censoring is censoring at a fixed time t0. Type II censoring is censoring after a pre-determined number of units have failed.

 Normal NORMAL MAXIMUM LIKELIHOOD Y Exponential EXPONENTIAL MAXIMUM LIKELIHOOD Y Confidence intervals are generated for the parameters and both type I and type II censoring are supported. For type I censoring, enter the following commands SET CENSORING TYPE 1 LET TEND = censoring time EXPONENTIAL MAXIMUM LIKELIHOOD Y ID For type II censoring, enter the following commands SET CENSORING TYPE 2 EXPONENTIAL MAXIMUM LIKELIHOOD Y ID Weibull WEIBULL MAXIMUM LIKELIHOOD Y Confidence intervals are generated for the parameters and both type I and type II censoring are supported. For type I censoring, enter the following commands SET CENSORING TYPE 1 LET TEND = censoring time WEIBULL MAXIMUM LIKELIHOOD Y ID For type II censoring, enter the following commands SET CENSORING TYPE 2 WEIBULL MAXIMUM LIKELIHOOD Y ID Lognormal LOGNORMAL MAXIMUM LIKELIHOOD Y Double Exponential DOUBLE EXPONENTIAL MAXIMUM LIKELIHOOD Y Pareto PARETO MAXIMUM LIKELIHOOD Y Gamma GAMMA MAXIMUM LIKELIHOOD Y Inverse Gaussian INVERSE GAUSSIAN MAXIMUM LIKELIHOOD Y Gumbel GUMBEL MAXIMUM LIKELIHOOD Y Binomial BINOMIAL MAXIMUM LIKELIHOOD Y Poisson POISSON MAXIMUM LIKELIHOOD Y

Dataplot Command for the Mean Plot The Dataplot command to generate a mean plot is
MEAN PLOT Y X
where Y is a response variable and X is a group id variable.

Dataplot supports this command for a number of other common location statistics. For example, MEDIAN PLOT Y X and MID-RANGE PLOT Y X compute the median and mid-range instead of the mean for each group.

Dataplot Commands for Normal Probability Functions Dataplot can compute the various probability functions for the normal distribution with the following commands.
 cdf LET Y = NORCDF(X,A,B) pdf LET Y = NORPDF(X,A,B) ppf LET Y = NORPPF(X,A,B) hazard LET Y = NORHAZ(X,A,B) cumulative hazard LET Y = NORCHAZ(X,A,B) survival LET Y = 1 - NORCDF(X,A,B) inverse survival LET Y = NORPPF(1-X,A,B) random numbers LET Y = NORMAL RANDOM NUMBERS FOR I = 1 1 1000 probability plot NORMAL PROBABILITY PLOT Y parameter estimates LET YMEAN = MEAN Y LET YSD = STANDARD DEVIATION Y
where X can be a number, a parameter, or a variable. A and B are the location and scale parameters and they are optional (a location of 0 and scale of 1 are used if they are omitted). If given, A and B can be a number, a parameter, or a variable. However, they are typically either a number or parameter.

These functions can be used in the Dataplot PLOT and FIT commands as well. For example,

PLOT NORPDF(X) FOR X = -4  0.01  4

Dataplot Commands for a Normal Probability Plot The Dataplot command to generate a normal probability plot is
NORMAL PROBABILITY PLOT Y
where Y is the response variable.

If your data are already grouped (i.e., Y contains counts for the groups identified by X), the Dataplot command is

NORMAL PROBABILITY PLOT Y X
Dataplot returns the following internal parameters when it generates a probability plot.
• PPCC - the correlation coefficient of the fitted line on the probability plot. This is a measure of how well the straight line fits the probability plot.
• PPA0 - the intercept term for the fitted line on the probability plot. This is an estimate of the location parameter.
• PPA1 - the slope term for the fitted line on the probability plot. This is an estimate of the scale parameter.
• SDPPA0 - the standard deviation of the intercept term for the fitted line on the probability plot.
• SDPPA1 - the standard deviation of the slope term for the fitted line on the probability plot.
• PPRESSD - the residual standard deviation of the fitted line on the probability plot. This is a measure of the adequacy of the fitted line.
• PPRESDF - the residual degrees of freedom of the fitted line on the probability plot.

Dataplot Commands for the Generation of Normal Random Numbers The Dataplot commands to generate 1,000 normal random numbers with a location of 50 and a scale of 20 are
LET LOC = 50
LET SCALE = 20
LET Y = NORM RAND NUMBERS FOR I = 1 1 1000
LET Y = LOC + SCALE*Y
Programs that automatically generate random numbers are typically controlled by a seed, which is usually an integer value. The importance of the seed is that it allows the random numbers to be replicated. That is, giving the program the same seed should generate the same sequence of random numbers. If the ability to replicate the set of random numbers is not important, you can give any valid value for the seed.

In Dataplot, the seed is an odd integer with a minimum (and default) value of 305. Seeds less than 305 generate the same sequence as 305 and even numbers generate the same sequence as the preceding odd number. To change the seed value to 401 in Dataplot, enter the command:

SEED 401

Dataplot Commands for Partial Autocorrelation Plots The command to generate a partial autocorrelation plot is
PARTIAL AUTOCORRELATION PLOT Y
The appearance of the partial autocorrelation plot can be controlled by appropriate settings of the LINE, CHARACTER, and SPIKE commands. Dataplot draws the following curves on the autocorrelation plot:
1. The autocorrelations.
2. A reference line at zero.
3. A reference line at the upper 95% confidence limit.
4. A reference line at the lower 95% confidence limit.
5. A reference line at the upper 99% confidence limit.
6. A reference line at the lower 99% confidence limit.
For example, to draw the partial autocorrelations as spikes, the zero reference line as a solid line, the 95% lines as dashed lines, and the 99% line as dotted lines, enter the command
LINE BLANK SOLID DASH DASH DOT DOT
CHARACTER BLANK ALL
SPIKE ON OFF OFF OFF OFF OFF
SPIKE BASE 0

Dataplot Commands for the Poisson Probability Functions Dataplot can compute the probability functions for the Poisson distribution with the following commands.
 cdf LET Y = POICDF(X,LAMBDA) pdf LET Y = POIPDF(X,LAMBDA) ppf LET Y = POIPPF(X,LAMBDA) random numbers LET LAMBDA = value LET Y = POISSON RANDOM NUMBERS FOR I = 1 1 1000 probability plot LET LAMBDA = value POISSON PROBABILITY PLOT Y ppcc plot POISSON PPCC PLOT Y
where X can be a number, a parameter, or a variable. LAMBDA is the shape parameter and is required. It can be a number, a parameter, or a variable. It is typically a number or a parameter.

These functions can be used in the Dataplot PLOT and FIT commands as well. For example,

PLOT POIPDF(X,15) FOR X = 0  1  50

Dataplot Commands for the Power Lognormal Distribution Dataplot can compute the probability functions for the power lognormal distribution with the following commands.
 cdf LET Y = PLNCDF(X,P,SD,MU) pdf LET Y = PLNPDF(X,P,SD,MU) ppf LET Y = PLNPPF(X,P,SD,MU) hazard LET Y = PLNHAZ(X,P,SD,MU) cumulative hazard LET Y = PLNCHAZ(X,P,SD,MU) survival LET Y = 1 - PLNCDF(X,P,SD,MU) inverse survival LET Y = PLNPPF(1-X,P,SD,MU) probability plot LET P = value LET SD = value (defaults to 1) POWER LOGNORMAL PROBABILITY PLOT Y ppcc plot LET SD = value POWER LOGNORMAL PPCC PLOT Y
In the above, X can be a number, a parameter, or a variable. SD and MU are the scale and location parameters, respectively, and they are optional (a location of 0 and scale of 1 are used if they are omitted). If given, SD and MU can be a number, a parameter, or a variable. However, they are typically either a number or a parameter.

These functions can be used in the Dataplot PLOT and FIT commands as well. For example, the command

PLOT PLNPDF(X,5,1) FOR X = 0.01  0.01  5

Dataplot Commands for the Power Normal Probability Functions Dataplot can compute the probability functions for the power normal distribution with the following commands.
 cdf LET Y = PNRCDF(X,P,SD,MU) pdf LET Y = PNRPDF(X,P,SD,MU) ppf LET Y = PNRPPF(X,P,SD,MU) hazard LET Y = PNRHAZ(X,P,SD,MU) cumulative hazard LET Y = PNRCHAZ(X,P,SD,MU) survival LET Y = 1 - PNRCDF(X,P,SD,MU) inverse survival LET Y = PNRPPF(1-X,P,SD,MU) probability plot LET P = value LET SD = value (defaults to 1) POWER NORMAL PROBABILITY PLOT Y ppcc plot POWER NORMAL PPCC PLOT Y
In the above, X can be a number, a parameter, or a variable. SD and MU are the scale and location parameters, respectively, and they are optional (a location of 0 and scale of 1 are used if they are omitted). If given, SD and MU can be a number, a parameter, or a variable. However, they are typically either a number or a parameter.

These functions can be used in the Dataplot PLOT and FIT commands as well. For example,

PLOT PNRPDF(X,10,1) FOR X = -5  0.01  5

Dataplot Commands for Probability Plots The Dataplot command for a probability plot is
<dist> PROBABILITY PLOT Y

where <dist> is the name of the specific distribution. Dataplot currently supports probability plots for over 70 distributions. For example,

NORMAL PROBABILITY PLOT Y
EXPONENTIAL PROBABILITY PLOT Y
DOUBLE EXPONENTIAL PROBABILITY PLOT Y
CAUCHY PROBABILITY PLOT Y

For some distributions, you may need to specify one or more shape parameters. For example, to specify the shape parameter for the gamma distribution, you might enter the following commands:

LET GAMMA = 2
GAMMA PROBABILITY PLOT Y
Enter the command LIST DISTRIBUTIONS to see a list of distributions for which Dataplot supports probability plots (and to see what parameters need to be specified).

Dataplot returns the following internal parameters when it generates a probability plot.

• PPCC - the correlation coefficient of the fitted line on the probability plot. This is a measure of how well the straight line fits the probability plot.
• PPA0 - the intercept term for the fitted line on the probability plot. This is an estimate of the location parameter.
• PPA1 - the slope term for the fitted line on the probability plot. This is an estimate of the scale parameter.
• SDPPA0 - the standard deviation of the intercept term for the fitted line on the probability plot.
• SDPPA1 - the standard deviation of the slope term for the fitted line on the probability plot.
• PPRESSD - the residual standard deviation of the fitted line on the probability plot. This is a measure of the adequacy of the fitted line.
• PPRESDF - the residual degrees of freedom of the fitted line on the probability plot.

Dataplot Commands for the PPCC Plot The Dataplot command to generate a PPCC plot for unbinned data is:
<dist> PPCC PLOT Y
where <dist> identifies the distributional family and Y is the response variable.

The Dataplot command to generate a PPCC plot for binned data is:

<dist> PPCC PLOT Y X
where <dist> identifies the distributional family, Y is the counts variable, and X is the bin identifier variable.

Dataplot supports the PPCC plot for over 25 distributions. Some of the most common are WEIBULL, TUKEY LAMBDA, GAMMA, PARETO, and INVERSE GAUSSIAN. Enter the command LIST DISTRIBUTIONS for a list of supported distributions.

Dataplot allows you to specify the range of the shape parameter. Dataplot generates 50 probability plots in equally spaced intervals from the smallest value of the shape parameter to the largest value of the shape parameter. For example, to generate a Weibull PPCC plot for values of the shape parameter gamma from 2 to 4, enter the commands:

LET GAMMA1 = 2
LET GAMMA2 = 4
WEIBULL PPCC PLOT Y
The command LIST DISTRIBUTIONS gives the name of the shape parameter for the supported distributions. The "1" and "2" suffixes imply the minimum and maximum value for the shape parameter, respectively.

Whenever Dataplot generates a PPCC plot, it saves the following internal parameters:

• MAXPPCC - the maximum correlation coefficient from the PPCC plot.
• SHAPE - the value of the shape parameter that generated the maximum correlation coefficient.

Dataplot Command for Proportion Defective Confidence Interval The Dataplot command for a confidence interval for the proportion defective is
PROPORTION CONFIDENCE LIMITS Y
where Y is a response variable. Note that for large samples, Dataplot generates the interval based on the exact binomial probability, not the normal approximation.

The following command sets the lower and upper bounds that define a success in the response variable:

ANOP LIMITS <lower bound> <upper bound>

Dataplot Command for Q-Q Plot The Dataplot command to generate a q-q plot is
QUANTILE-QUANTILE PLOT Y1 Y2
The CHARACTER and LINE commands can be used to control the appearance of the q-q plot. For example, to draw the quantile points as circles and the reference line as a solid line, enter the commands
LINE BLANK SOLID
CHARACTER CIRCLE BLANK

Dataplot Commands for the Generation of Random Walk Numbers To generate a random walk with 1,000 points requires the following Dataplot commands:
```LET Y = UNIFORM RANDOM NUMBERS FOR I = 1 1 1000
LET Y2 = Y - 0.5
LET RW = CUMULATIVE SUM Y2
```

Dataplot Commands for Rank Sum Test The Dataplot commands for a rank sum (Wilcoxon rank sum, Mann-Whitney) test are
RANK SUM TEST Y1 Y2
RANK SUM TEST Y1 Y2 A
where Y1 contains the data for sample 1, Y2 contains the data for sample 2, and A is a scalar value (either a number or a parameter). Y1 and Y2 need not have the same number of observations.

The first syntax is used to test the hypothesis that two sample means are equal. The second syntax is used to test that the difference between two means is equal to a specified constant.

Dataplot Commands for the Run Sequence Plot The Dataplot command to generate a run sequence plot is
RUN SEQUENCE PLOT Y
Equivalently, you can enter
PLOT Y
The appearance of the plot can be controlled with appropriate settings of the LINE, CHARACTER, SPIKE, and BAR commands and their associated attribute-setting commands.

Dataplot Command for the Runs Test The Dataplot command for a runs test is
RUNS TEST Y
where Y is a response variable.

Dataplot Commands for Measures of Scale The various scale measures can be computed in Dataplot as follows:
LET A = VARIANCE Y
LET A = STANDARD DEVIATION Y
LET A = AVERAGE ABSOLUTE DEVIATION Y
LET A = MEDIAN ABSOLUTE DEVIATION Y
LET A = RANGE Y

LET A1 = LOWER QUARTILE Y
LET A2 = UPPER QUARTILE Y
LET IQRANGE = A2 - A1

Dataplot Commands for Scatter Plots The Dataplot command to generate a scatter plot is
PLOT Y X
The appearance of the plot can be controlled by appropriate settings of the CHARACTER and LINE commands and their various attribute-setting commands.

Dataplot Commands for Scatterplot Matrix The Dataplot command to generate a scatterplot matrix is
SCATTER PLOT MATRIX X1 X2 ... XK
The appearance of the plot can be controlled by appropriate settings of the CHARACTER and LINE commands and their various attribute-setting commands.

In addition, Dataplot provides a number of SET commands to control the appearance of the scatterplot matrix. The most common commands are:

• SET MATRIX PLOT LOWER DIAGONAL <ON/OFF>
This command controls whether or not the plots below the diagonal are plotted.
• SET MATRIX PLOT TAG <ON/OFF>
If ON, the last variable on the SCATTER PLOT MATRIX command is not plotted directly. Instead, it is used as a group-id variable. You can use the CHARACTER and LINE commands to set the plot attributes for each group.
• SET MATRIX PLOT FRAME <DEFAULT/USER/CONNECTED>
If DEFAULT, the plot frames are connected (that is, it does a FRAME CORNER COORDINATES 0 0 100 100). The axis tic marks and labels are controlled automatically. If CONNECTED, then it is similar to DEFAULT except the current value of FRAME CORNER COORDINATES is used. This is useful for putting a small gap between the plots (e.g., enter FRAME CORNER COORDINATES 3 3 97 97 before generating the scatterplot matrix). If USER, Dataplot does not connect the plot frames. The tic marks and labels are as the user set them.
• SET MATRIX PLOT FIT <NONE/LOWESS/LINEAR/QUADRATIC> This controls whether a lowess fit, a linear fit, a quadratic fit line, or no fit is superimposed on the plot points. If lowess, a rather high value of the lowess fraction is recommended (e.g., LOWESS FRACTION 0.6).
In Dataplot, enter HELP SCATTER PLOT MATRIX for additional options for this plot.

Dataplot Commands for Seasonal Subseries Plot The Dataplot commands to generate a seasonal subseries plot are
LET PERIOD = <value>
LET START = <value>
SEASONAL SUBSERIES PLOT Y
The value of PERIOD defines the length of the seasonal period (e.g., 12 for monthly data) and START identifies which group the series starts with (e.g., if you have monthly data that starts in March, set START to 3).

The appearance of the plot can be controlled by appropriate settings of the CHARACTER and LINE commands and their various attribute-setting commands.

Dataplot Commands for Sign Test The Dataplot commands for a sign test are
SIGN TEST Y1 A
SIGN TEST Y1 Y2
SIGN TEST Y1 Y2 A
where Y1 contains the data for sample 1, Y2 contains the data for sample 2, and A is a scalar value (either a number or a parameter). Y1 and Y2 should have the same number of observations.

The first syntax is used to test the hypothesis that the mean for one sample equals a specified constant. The second syntax is used to test the hypothesis that two sample means are equal. The third syntax is used to test that the difference between two means is equal to a specified constant.

Dataplot Commands for Signed Rank Test The Dataplot commands for a signed rank (or Wilcoxon signed-rank) test are
SIGNED RANK TEST Y1 A
SIGNED RANK TEST Y1 Y2
SIGNED RANK TEST Y1 Y2 A
where Y1 contains the data for sample 1, Y2 contains the data for sample 2, and A is a scalar value (either a number or a parameter). Y1 and Y2 should have the same number of observations.

The first syntax is used to test the hypothesis that the mean for one sample equals a specified constant. The second syntax is used to test the hypothesis that two sample means are equal. The third syntax is used to test that the difference between two means is equal to a specified constant.

Dataplot Commands for Skewness and Kurtosis The Dataplot commands for skewness and kurtosis are
LET A = SKEWNESS Y
LET A = KURTOSIS Y
where Y is the response variable. Dataplot can also generate plots of the skewness and kurtosis for grouped data or one-factor data with the following commands:
SKEWNESS PLOT Y X
KURTOSIS PLOT Y X
where Y is the response variable and X is the group id variable.

Dataplot Command for the Spectral Plot The Dataplot command to generate a spectral plot is
SPECTRAL PLOT Y

Dataplot Command for the Standard Deviation Plot The Dataplot command to generate a standard deviation plot is
STANDARD DEVIATION PLOT Y X
where Y is a response variable and X is a group id variable.

Dataplot supports this command for a number of other common scale statistics. For example, AAD PLOT Y X and MAD PLOT Y X compute the average absolute deviation and median absolute deviation, respectively, instead of the standard deviation for each group.

Dataplot Command for the Star Plot The Dataplot command to generate a star plot is
STAR PLOT X1 TO XP FOR I = 10 1 10
where there are p response variables called X1, X2, ... , XP. Note that this syntax prints one star, specifically the tenth row of the X1, X2, ..., XP variables.

Typically, multiple star plots will be displayed on the same page. For example, to plot the first 25 rows on the same page, enter the following sequence of commands

MULTIPLOT CORNER COORDINATES 0 0 100 100
MULTIPLOT 5 5
LOOP FOR K = 1 1 25
STAR PLOT X1 TO XP FOR I = K 1 K
END OF LOOP

Dataplot Command to Generate a Table of Summary Statistics The Dataplot command to generate a table of summary statistics is
SUMMARY Y
where Y is the response variable.

Dataplot Commands for the t Probability Functions Dataplot can compute the probability functions for the t distribution with the following commands.
 cdf LET Y = TCDF(X,NU,A,B) pdf LET Y = TPDF(X,NU,A,B) ppf LET Y = TPPF(X,NU,A,B) random numbers LET NU = value LET Y = T RANDOM NUMBERS FOR I = 1 1 1000 probability plot LET NU = value T PROBABILITY PLOT Y ppcc plot LET NU = value T PPCC PLOT Y
In the above, X can be a number, a parameter, or a variable. NU is the shape parameter (= number of degrees of freedom). NU can be a number, a parameter, or a variable. However, it is typically either a number or a parameter. A and B are the location and scale parameters, respectively, and they are optional (a location of 0 and scale of 1 are used if they are omitted). If given, A and B can be a number, a parameter, or a variable. However, they are typically either a number or a parameter.

These functions can be used in the Dataplot PLOT and FIT commands as well. For example,

PLOT TPDF(X) FOR X = -4  0.01  4

Dataplot Command for Tietjen-Moore Test The Dataplot command for the Tietjen-Moore test is
LET NOUTLIER = <value>
TIETJEN-MOORE <MINIMUM/MAXIMUM> TEST Y
where Y is the response variable and NOUTLIER specifies the number of outliers to test. The MINIMUM or MAXIMUM keyword is optional. If it is omitted, outliers will be checked in both the minimum and the maximum direction.

Dataplot Command for Tolerance Intervals The Dataplot command for tolerance intervals is
TOLERANCE Y
where Y is the response variable. Both normal and nonparametric tolerance intervals are printed.

Dataplot Command for Two-Sample t-Test The Dataplot command to generate a two-sample t-test is
T TEST Y1 Y2
where Y1 contains the data for sample 1 and Y2 contains the data for sample 2. Y1 and Y2 do not need to have the same number of observations.

Dataplot Commands for the Tukey-Lambda Probability Functions Dataplot can compute the probability functions for the Tukey-Lambda distribution with the following commands.
 cdf LET Y = LAMCDF(X,LAMBDA,A,B) pdf LET Y = LAMPDF(X,LAMBDA,A,B) ppf LET Y = LAMPPF(X,LAMBDA,A,B) random numbers LET LAMBDA = value LET Y = TUKEY-LAMBDA RANDOM NUMBERS FOR I = 1 1 1000 probability plot LET LAMBDA = value TUKEY-LAMBDA PROBABILITY PLOT Y ppcc plot TUKEY-LAMBDA PPCC PLOT Y
In the above, X can be a number, a parameter, or a variable. LAMBDA is the shape parameter and is required. It can be a number, a parameter, or a variable. It is typically a number or a parameter. A and B are the location and scale parameters, respectively, and they are optional (a location of 0 and scale of 1 are used if they are omitted). If given, A and B can be a number, a parameter, or a variable. However, they are typically either a number or a parameter.

These functions can be used in the Dataplot PLOT and FIT commands as well. For example,

PLOT LAMPDF(X,0.14) FOR X = -5  0.01  5

Dataplot Commands for the Uniform Probability Functions Dataplot can compute the probability functions for the uniform distribution with the following commands.
 cdf LET Y = UNICDF(X,A,B) pdf LET Y = UNIPDF(X,A,B) ppf LET Y = UNIPPF(X,A,B) hazard LET Y = UNIHAZ(X,A,B) cumulative hazard LET Y = UNICHAZ(X,A,B) survival LET Y = 1 - UNICDF(X,A,B) inverse survival LET Y = UNIPPF(1-X,A,B) random numbers LET Y = UNIFORM RANDOM NUMBERS FOR I = 1 1 1000 probability plot UNIFORM PROBABILITY PLOT Y parameter estimation The method of moment estimators can be computed with the commands LET YMEAN = MEAN Y LET YSD = STANDARD DEVIATION Y LET A = YMEAN - SQRT(3)*YSD LET B = YMEAN + SQRT(3)*YSD The maximum likelihood estimators can be computed with the commands LET YRANGE = RANGE Y LET YMIDRANG = MID-RANGE Y LET A = YMIDRANG - 0.5*YRANGE LET B = YMIDRANG + 0.5*YRANGE
In the above, X can be a number, a parameter, or a variable. A and B are the lower and upper limits of the uniform distribution and they are optional (A is 0 and B is 1 if they are omitted). The location parameter is A and the scale parameter is (B - A). If given, A and B can be a number, a parameter, or a variable. However, they are typically either a number or a parameter.

These functions can be used in the Dataplot PLOT and FIT commands as well. For example,

PLOT UNIPDF(X) FOR X = 0  0.1  1

Dataplot Commands for the Generation of Uniform Random Numbers The Dataplot commands to generate 1,000 uniform random numbers in the interval (-100,100) are
LET A = -100
LET B = 100
LET Y = UNIFORM RANDOM NUMBERS FOR I = 1 1 1000
LET Y = A + (B-A)*Y
A similar technique can be used for any package that can generate standard uniform random numbers. Simply multiply by the scale value (equals upper limit minus lower limit) and add the location value.

Programs that automatically generate random numbers are typically controlled by a seed, which is usually an integer value. The importance of the seed is that it allows the random numbers to be replicated. That is, giving the program the same seed should generate the same sequence of random numbers. If the ability to replicate the set of random numbers is not important, you can give any valid value for the seed.

In Dataplot, the seed is an odd integer with a minimum (and default) value of 305. Seeds less than 305 generate the same sequence as 305 and even numbers generate the same sequence as the preceeding odd number. To change the seed value to 401 in Dataplot, enter the command:

SEED 401

Dataplot Commands for the Weibull Probability Functions Dataplot can compute the probability functions for the Weibull distribution with the following commands.
 cdf LET Y = WEICDF(X,GAMMA,A,B) pdf LET Y = WEIPDF(X,GAMMA,A,B) ppf LET Y = WEIPPF(X,GAMMA,A,B) hazard LET Y = WEIHAZ(X,GAMMA,A,B) cumulative hazard LET Y = WEICHAZ(X,GAMMA,A,B) survival LET Y = 1 - WEICDF(X,GAMMA,A,B) inverse survival LET Y = WEIPPF(1-X,GAMMA,A,B) random numbers LET GAMMA = value LET Y = WEIBULL RANDOM NUMBERS FOR I = 1 1 1000 probability plot LET GAMMA = value WEIBULL PROBABILITY PLOT Y ppcc plot LET GAMMA = value WEIBULL PPCC PLOT Y parameter estimation If your data are not censored, enter the commands SET CENSORING TYPE NONE WEIBULL MLE Y If your data have type 1 censoring at fixed time t0, enter the commands LET TEND = censoring time SET CENSORING TYPE 1 WEIBULL MLE Y X If your data have type 2 censoring, enter the commands SET CENSORING TYPE 2 WEIBULL MLE Y X Y is the response variable and X is the censoring variable where a value of 1 indicates a failure time and a value of 0 indicates a censoring time. In addition to the point estimates, confidence intervals for the parameters are generated.
In the above, X can be a number, a parameter, or a variable. GAMMA is the shape parameter and is required. It can be a number, a parameter, or a variable. It is typically a number or a parameter. A and B are the location and scale parameters, respectively, and they are optional (a location of 0 and scale of 1 are used if they are omitted). If given, A and B can be a number, a parameter, or a variable. However, they are typically either a number or a parameter.

These functions can be used in the Dataplot PLOT and FIT commands as well. For example,

PLOT WEIPDF(X,2) FOR X = 0.01  0.01  5

Dataplot Commands for the Weibull Plot The Dataplot commands to generate a Weibull plot are
WEIBULL PLOT Y
WEIBULL PLOT Y X
where Y is the response variable containing failure times and X is an optional censoring variable. A value of 1 indicates the item failed by the failure mode of interest while a value of 0 indicates that the item failed by a failure mode that is not of interest.

The appearance of the plot can be controlled with appropriate settings for the LINE and CHARACTER commands. For example, to draw the raw data with the "X" character and the 2 reference lines as dashed lines, enter the commands

LINE BLANK DASH DASH
CHARACTER X BLANK BLANK
WEIBULL PLOT Y X
Dataplot saves the following internal parameters after the Weibull plot.
ETA - the estimated characterstic life
BETA - the estimated shape parameter
SDETA - the estimated standard deviation of ETA
SDBETA - the estimated standard deviation of BETA
BPT1 - the estimated 0.1% point of failure times
BPT5 - the estimated 0.5% point of failure times
B1 - the estimated 1% point of failure times
B5 - the estimated 5% point of failure times
B10 - the estimated 10% point of failure times
B20 - the estimated 20% point of failure times
B50 - the estimated 50% point of failure times
B80 - the estimated 80% point of failure times
B90 - the estimated 90% point of failure times
B95 - the estimated 95% point of failure times
B99 - the estimated 99% point of failure times
B995 - the estimated 99.5% point of failure times
B999 - the estimated 99.9% point of failure times

Dataplot Command for the Wilk-Shapiro Normality Test The Dataplot command for a Wilk-Shapiro normality test is
WILK SHAPIRO TEST Y
where Y is the response variable.

The significance value is only valid if there is less than 5,000 points.

Dataplot Commands for Yates Analysis The Dataplot command for a Yates analysis is
YATES Y
where Y is a response variable in Yates order.

Dataplot Commands for the Youden Plot The Dataplot command to generate a Youden plot is
YOUDEN PLOT Y1 Y2 LAB
where Y1 and Y2 are the response variables and LAB is a laboratory (or run number) identifier. The LINE and CHARACTER commands can be used to control the appearance of the Youden plot. For example, if there are 5 labs, a typical sequence would be
LINE BLANK ALL
CHARACTER 1 2 3 4 5
YOUDEN PLOT Y X LAB

Dataplot Commands for the 4-plot The Dataplot command to generate the 4-plot is
4-PLOT Y
where Y is the response variable.

Dataplot Commands for the 6-Plot The Dataplot commands to generate a 6-plot are
FIT Y X
6-PLOT Y X
where Y is the response variable and X is the independent variable. 