Y, Columns
|
Assigns the variables that you want to analyze. A histogram and associated reports appear for each variable.
|
Weight
|
Assigns a variable to give the observations different weights. Any moment that is based on the Sum Wgts is affected by weights.
|
Freq
|
Assigns a frequency variable to this role. This is useful if you have summarized data. In this instance, you have one column for the Y values and another column for the frequency of occurrence of the Y values. The sum of this variable is included in the overall count appearing in the Summary Statistics report (represented by N). All other moment statistics (mean, standard deviation, and so on) are also affected by the Freq variable.
|
By
|
Produces a separate report for each level of the By variable. If more than one By variable is assigned, a separate report is produced for each possible combination of the levels of the By variables.
|
Histograms Only
|
Removes everything except the histograms from the report window.
|
Highlighting data
|
Click on a histogram bar or an outlying point in the graph. The corresponding rows are highlighted in the data table, and corresponding sections of other histograms are also highlighted, if applicable. See “Highlight Bars and Select Rows”.
|
Creating a subset
|
Double-click on a histogram bar, or right-click on a histogram bar and select Subset. A new data table is created that contains only the selected data.
|
Resizing the entire histogram
|
Hover over the histogram borders until you see a double-sided arrow. Then click and drag the borders. For more details, see the Using JMP book.
|
Rescaling the axis
|
(Continuous variables only) Click and drag on an axis to rescale it.
Alternatively, hover over the axis until you see a hand. Then, double-click on the axis and set the parameters in the Axis Specification window.
|
Resizing histogram bars
|
(Continuous variables only) There are multiple options to resize histogram bars. See “Resize Histogram Bars for Continuous Variables”.
|
Specifying your selection
|
Specify the data that you select in multiple histograms. See “Specify Your Selection in Multiple Histograms”.
|
Level
|
Lists each value found for a response variable.
|
Count
|
Lists the number of rows found for each level of a response variable. If you use a Freq variable, the Count is the sum of the Freq variables for each level of the response variable.
|
Prob
|
Lists the probability (or proportion) of occurrence for each level of a response variable. The probability is computed as the count divided by the total frequency of the variable, shown at the bottom of the table.
|
StdErr Prob
|
Lists the standard error of the probabilities. This column might be hidden. To show the column, right-click in the table and select Columns > StdErr Prob.
|
Cum Prob
|
Contains the cumulative sum of the column of probabilities. This column might be hidden. To show the column, right-click in the table and select Columns > Cum Prob.
|
Mean
|
Estimates the expected value of the underlying distribution for the response variable, which is the arithmetic average of the column’s values. It is the sum of the non-missing values divided by the number of non-missing values.
|
Std Dev
|
The normal distribution is mainly defined by the mean and standard deviation. These parameters provide an easy way to summarize data as the sample becomes large:
• 68% of the values are within one standard deviation of the mean
• 95% of the values are within two standard deviations of the mean
• 99.7% of the values are within three standard deviations of the mean
|
Std Err Mean
|
The standard error of the mean, which estimates the standard deviation of the distribution of the mean.
|
Upper 95% Mean and Lower 95% Mean
|
Are 95% confidence limits about the mean. They define an interval that is very likely to contain the true population mean.
|
N
|
Is the total number of nonmissing values.
|
Sum Weight
|
The sum of a column assigned to the role of Weight (in the launch window). Sum Wgt is used in the denominator for computations of the mean instead of N.
|
Sum
|
The sum of the response values.
|
Variance
|
The sample variance, and the square of the sample standard deviation.
|
Skewness
|
Measures sidedness or symmetry.
|
Kurtosis
|
Measures peakedness or heaviness of tails.
|
CV
|
The percent coefficient of variation. It is computed as the standard deviation divided by the mean and multiplied by 100. The coefficient of variation can be used to assess relative variation, for example when comparing the variation in data measured in different units or with different magnitudes.
|
N Missing
|
The number of missing observations.
|
N Zero
|
The number of zero values.
|
N Unique
|
The number of unique values.
|
Uncorrected SS
|
The uncorrected sum of squares or sum of values squared.
|
Corrected SS
|
The corrected sum of squares or sum of squares of deviations from the mean.
|
Autocorrelation
|
(Appears only if you have not specified a Frequency variable.) First autocorrelation that tests if the residuals are correlated across the rows. This test helps detect non-randomness in the data.
|
Minimum
|
Represents the 0 percentile of the data.
|
Maximum
|
Represents the 100 percentile of the data.
|
Median
|
Represents the 50th percentile of the data.
|
Mode
|
The value that occurs most often in the data. If there are multiple modes, the smallest mode appears.
|
Trimmed Mean
|
(Does not appear if you have specified a Weight variable.)The mean calculated after removing the smallest p% and the largest p% of the data.
|
Geometric Mean
|
The nth root of the product of the data.
|
Range
|
The difference between the maximum and minimum of the data.
|
Interquartile Range
|
The difference between the 3rd and 1st quartiles.
|
Median Absolute Deviation
|
(Does not appear if you have specified a Weight variable.) The median of the absolute deviations from the median.
|
Robust Mean
|
The robust mean, calculated in a way that is resistant to outliers, using Huber's M-estimation. See Huber and Ronchetti, 2009.
|
Robust Std Dev
|
The robust standard deviation, calculated in a way that is resistant to outliers, using Huber's M-estimation. See Huber and Ronchetti, 2009.
|
Enter (1-alpha) for mean confidence interval
|
Specify the alpha level for the mean confidence interval.
|
Enter trimmed mean percent
|
Specify the trimmed mean percentage. The percentage is trimmed off each side of the data.
|
Uniform Scaling
|
Scales all axes with the same minimum, maximum, and intervals so that the distributions can be easily compared.
|
Stack
|
Changes the orientation of the histogram and the reports to horizontal and stacks the individual distribution reports vertically. Deselect this option to return the report window to its original layout.
|
Arrange in Rows
|
Enter the number of plots that appear in a row. This option helps you view plots vertically rather than in one wide row.
|
Save for Adobe Flash platform (.SWF)
|
Saves the histograms as .swf files that are Adobe Flash player compatible. Use these files in presentations and in Web pages. An HTML page is also saved that shows you the correct code for using the resulting .swf file.
For more information about this option, go to http://www.jmp.com/support/swfhelp/en.
|
Script
|
This menu contains options that are available to all platforms. They enable you to redo the analysis or save the JSL commands for the analysis to a window or a file. For more information, see Using JMP.
|
The Display Options sub-menu contains the following options:
|
|
Frequencies
|
Shows or hides the Frequencies report. See “The Frequencies Report”.
|
Horizontal Layout
|
Changes the orientation of the histogram and the reports to vertical or horizontal.
|
Axes on Left
|
Moves the Count, Prob, and Density axes to the left instead of the right.
This option is applicable only if Horizontal Layout is selected.
|
The Histograms sub-menu contains the following options:
|
|
Histogram
|
Shows or hides the histogram. See “Histograms”.
|
Vertical
|
Changes the orientation of the histogram from a vertical to a horizontal orientation.
|
Std Error Bars
|
Draws the standard error bar on each level of the histogram.
|
Separate Bars
|
Separates the histogram bars.
|
Histogram Color
|
Changes the color of the histogram bars.
|
Count Axis
|
Adds an axis that shows the frequency of column values represented by the histogram bars.
|
Prob Axis
|
Adds an axis that shows the proportion of column values represented by histogram bars.
|
Density Axis
|
Adds an axis that shows the length of the bars in the histogram.
The count and probability axes are based on the following calculations:
prob = (bar width)*density
count = (bar width)*density*(total count)
|
Show Percents
|
Labels the percent of column values represented by each histogram bar.
|
Show Counts
|
Labels the frequency of column values represented by each histogram bar.
|
|
|
Mosaic Plot
|
Displays a mosaic bar chart for each nominal or ordinal response variable. A mosaic plot is a stacked bar chart where each segment is proportional to its group’s frequency count.
|
Order By
|
Reorders the histogram, mosaic plot, and Frequencies report in ascending or descending order, by count. To save the new order as a column property, use the Save > Value Ordering option.
|
Test Probabilities
|
Displays a report that tests hypothesized probabilities. See “Examples of the Test Probabilities Option” for more details.
|
Confidence Interval
|
This menu contains confidence levels. Select a value that is listed, or select Other to enter your own. JMP computes score confidence intervals.
|
The Save sub-menu contains the following options:
|
|
Level Numbers
|
Creates a new column in the data table called Level <colname>. The level number of each observation corresponds to the histogram bar that contains the observation.
|
Value Ordering
|
(Use with the Order By option) Creates a new value ordering column property in the data table, reflecting the new order.
|
Script to log
|
Displays the script commands to generate the current report in the log window. Select View > Log to see the log window.
|
|
|
Remove
|
Permanently removes the variable and all its reports from the Distribution report.
|
The Display Options sub-menu contains the following options:
|
|
Quantiles
|
Shows or hides the Quantiles report. See “The Quantiles Report”.
|
Set Quantile Increment
|
Changes the quantile increment or revert back to the default quantile increment.
|
Custom Quantiles
|
Sets custom quantiles by values or by increments. You can also specify the confidence level. Smoothed empirical likelihood quantile estimates, based on a kernel density estimate, are added to the report. The confidence intervals for these quantile estimates tend to contain the true quantile with the promised confidence level.
|
Summary Statistics
|
Shows or hides the Summary Statistics report. See “The Summary Statistics Report”.
|
Customize Summary Statistics
|
Adds or removes statistics from the Summary Statistics report. See “The Summary Statistics Report”.
|
Horizontal Layout
|
Changes the orientation of the histogram and the reports to vertical or horizontal.
|
Axes on Left
|
Moves the Count, Prob, Density, and Normal Quantile Plot axes to the left instead of the right.
This option is applicable only if Horizontal Layout is selected.
|
The Histograms sub-menu contains the following options:
|
|
Histogram
|
Shows or hides the histogram. See “Histograms”.
|
Shadowgram
|
Replaces the histogram with a shadowgram. To understand a shadowgram, consider that if the bin width of a histogram is changed, the appearance of the histogram changes. A shadowgram overlays histograms with different bin widths. Dominant features of a distribution are less transparent on the shadowgram.
Note that the following options are not available for shadowgrams:
• Std Error Bars
• Show Counts
• Show Percents
|
Vertical
|
Changes the orientation of the histogram from a vertical to a horizontal orientation.
|
Std Error Bars
|
Draws the standard error bar on each level of the histogram using the standard error. The standard error bar adjusts automatically when you adjust the number of bars with the hand tool. See “Resize Histogram Bars for Continuous Variables”, and “Statistical Details for Standard Error Bars”.
|
Set Bin Width
|
Changes the bin width of the histogram bars. See “Resize Histogram Bars for Continuous Variables”.
|
Histogram Color
|
Changes the color of the histogram bars.
|
Count Axis
|
Adds an axis that shows the frequency of column values represented by the histogram bars.
Note: If you resize the histogram bars, the count axis also resizes.
|
Prob Axis
|
Adds an axis that shows the proportion of column values represented by histogram bars.
Note: If you resize the histogram bars, the probability axis also resizes.
|
Density Axis
|
The density is the length of the bars in the histogram. Both the count and probability are based on the following calculations:
prob = (bar width)*density
count = (bar width)*density*(total count)
When looking at density curves that are added by the Fit Distribution option, the density axis shows the point estimates of the curves.
Note: If you resize the histogram bars, the density axis remains constant.
|
Show Percents
|
Labels the proportion of column values represented by each histogram bar.
|
Show Counts
|
Labels the frequency of column values represented by each histogram bar.
|
|
|
Normal Quantile Plot
|
Adds a normal quantile plot that shows the extent to which the variable is normally distributed. See “Normal Quantile Plot”.
|
Outlier Box Plot
|
Adds an outlier box plot that shows the outliers in your data. See “Outlier Box Plot”.
|
Stem and Leaf
|
Adds a stem and leaf report, which is a variation of the histogram. See “Stem and Leaf”.
|
CDF Plot
|
Adds a plot of the empirical cumulative distribution function. See “CDF Plot”.
|
Test Mean
|
Performs a one-sample test for the mean. See “Test Mean”.
|
Test Std Dev
|
Performs a one-sample test for the standard deviation. See “Test Std Dev”.
|
Confidence Interval
|
Choose confidence intervals for the mean and standard deviation. See “Confidence Intervals for Continuous Variables”.
|
Prediction Interval
|
Choose prediction intervals for a single observation, or for the mean and standard deviation of the next randomly selected sample. See “Prediction Intervals”.
|
Tolerance Interval
|
Computes an interval to contain at least a specified proportion of the population. See “Tolerance Intervals”.
|
Capability Analysis
|
Measures the conformance of a process to given specification limits. See “Capability Analysis”.
|
Continuous Fit
|
Fits distributions to continuous variables. See “Fit Distributions”.
|
Discrete Fit
|
Fits distributions to discrete variables. See “Fit Distributions”.
|
Save
|
Saves information about continuous or categorical variables. See “Save Commands for Continuous Variables”.
|
|
|
Remove
|
Permanently removes the variable and all its reports from the Distribution report.
|
Statistics that are calculated for Test Mean:
|
|
t Test (or z Test)
|
Lists the value of the test statistic and the p-values for the two-sided and one-sided alternatives.
|
Signed-Rank
|
(Only appears for the Wilcoxon Signed-Rank test) Lists the value of the Wilcoxon signed-rank statistic followed by the p-values for the two-sided and one-sided alternatives. The test assumes only that the distribution is symmetric. See “Statistical Details for the Wilcoxon Signed Rank Test”.
|
Probability values:
|
|
Prob>|t|
|
The probability of obtaining an absolute t-value by chance alone that is greater than the observed t-value when the population mean is equal to the hypothesized value. This is the p-value for observed significance of the two-tailed t-test.
|
Prob>t
|
The probability of obtaining a t-value greater than the computed sample t ratio by chance alone when the population mean is not different from the hypothesized value. This is the p-value for an upper-tailed test.
|
Prob<t
|
The probability of obtaining a t-value less than the computed sample t ratio by chance alone when the population mean is not different from the hypothesized value. This is the p-value for a lower-tailed test.
|
PValue animation
|
Starts an interactive visual representation of the p-value. Enables you to change the hypothesized mean value while watching how the change affects the p-value.
|
Power animation
|
Starts an interactive visual representation of power and beta. You can change the hypothesized mean and sample mean while watching how the changes affect power and beta.
|
Remove Test
|
Removes the mean test.
|
Test Statistic
|
Provides the value of the Chi-square test statistic. See “Statistical Details for the Standard Deviation Test”.
|
Min PValue
|
The probability of obtaining a greater Chi-square value by chance alone when the population standard deviation is not different from the hypothesized value. See “Statistical Details for the Standard Deviation Test”.
|
Prob>ChiSq
|
The probability of obtaining a Chi-square value greater than the computed sample Chi-square by chance alone when the population standard deviation is not different from the hypothesized value. This is the p-value for observed significance of a one-tailed t-test.
|
Prob<ChiSq
|
The probability of obtaining a Chi-square value less than the computed sample Chi-square by chance alone when the population standard deviation is not different from the hypothesized value. This is the p-value for observed significance of a one-tailed t-test.
|
Command
|
Column Added to Data Table
|
Description
|
Level Numbers
|
Level <colname>
|
The level number of each observation corresponds to the histogram bar that contains the observation. The histogram bars are numbered from low to high, beginning with 1.
|
Level Midpoints
|
Midpoint <colname>
|
The midpoint value for each observation is computed by adding half the level width to the lower level bound.
|
Ranks
|
Ranked <colname>
|
Provides a ranking for each of the corresponding column’s values starting at 1. Duplicate response values are assigned consecutive ranks in order of their occurrence in the data table.
|
Ranks Averaged
|
RankAvgd <colname>
|
If a value is unique, then the averaged rank is the same as the rank. If a value occurs k times, the average rank is computed as the sum of the value’s ranks divided by k.
|
Prob Scores
|
Prob <colname>
|
For N nonmissing scores, the probability score of a value is computed as the averaged rank of that value divided by N + 1. This column is similar to the empirical cumulative distribution function.
|
Normal Quantiles
|
N-Quantile <colname>
|
Saves the Normal quantiles to the data table. See “Statistical Details for the Normal Quantile Plot”.
|
Standardized
|
Std <colname>
|
Saves standardized values to the data table. See “Statistical Details for Saving Standardized Data”.
|
Centered
|
Centered <colname>
|
Saves values for centering on zero.
|
Spec Limits
|
(none)
|
Stores the specification limits applied in a capability analysis as a column property of the corresponding column in the current data table. Automatically retrieves and displays the specification limits when you repeat the capability analysis.
|
Script to Log
|
(none)
|
Prints the script to the log window. Run the script to recreate the analysis.
|
<Distribution type>
|
By default, the normal distribution is assumed when calculating the capability statistics and the percent out of the specification limits. To perform a capability analysis on non-normal distributions, see the description of Spec Limits under “Fit Distribution Options”.
|
<Sigma type>
|
Estimates sigma (σ) using the selected methods. See “Statistical Details for Capability Analysis”.
|
Specification
|
Lists the specification limits.
|
Value
|
Lists the values that you specified for each specification limit and the target.
|
Portion and % Actual
|
Portion labels describe the numbers in the % Actual column, as follows:
• Below LSL gives the percentage of the data that is below the lower specification limit.
• Above USL gives the percentage of the data that is above the upper specification limit.
• Total Outside gives the total percentage of the data that is either below LSL or above USL.
|
Capability
|
Type of process capability indices. See Table 3.19.
Note: There is a preference for Capability called Ppk Capability Labeling that labels the long-term capability output with Ppk labels. Open the Preference window (File > Preferences), then select Platforms > Distribution to see this preference.
|
Index
|
Process capability index values.
|
Upper CI
|
Upper confidence interval.
|
Lower CI
|
Lower confidence interval.
|
Portion and Percent
|
Portion labels describe the numbers in the Percent column, as follows:
• Below LSL gives the percentage of the fitted distribution that is below the lower specification limit.
• Above USL gives the percentage of the fitted distribution that is above the upper specification limit.
• Total Outside gives the total percentage of the fitted distribution that is either below LSL or above USL.
|
PPM (parts per million)
|
The PPM value is the Percent column multiplied by 10,000.
|
Sigma Quality
|
Sigma Quality is frequently used in Six Sigma methods, and is also referred to as the process sigma. See “Statistical Details for Capability Analysis”.
|
Z Bench
|
Shows the values (represented by Index) of the Benchmark Z statistics. According to the AIAG Statistical Process Control manual, Z represents the number of standard deviation units from the process average to a value of interest such as an engineering specification. When used in capability assessment, Z USL is the distance to the upper specification limit and Z LSL is the distance to the lower specification limit. See “Statistical Details for Capability Analysis”.
|
Capability Animation
|
Interactively change the specification limits and the process mean to see the effects on the capability statistics. This option is available only for capability analyses based on the Normal distribution.
|
Diagnostic Plot
|
Creates a quantile or a probability plot. See “Diagnostic Plot”.
|
Density Curve
|
Uses the estimated parameters of the distribution to overlay a density curve on the histogram.
|
Goodness of Fit
|
Computes the goodness of fit test for the fitted distribution. See “Goodness of Fit”.
|
Fix Parameters
|
Enables you to fix parameters and re-estimate the non-fixed parameters. An Adequacy LR (likelihood ratio) Test report also appears, which tests your new parameters to determine whether they fit the data.
|
Quantiles
|
Returns the un scaled and un centered quantiles for the specific lower probability values that you specify.
|
Set Spec Limits for K Sigma
|
Use this option when you do not know the specification limits for a process and you want to use its distribution as a guideline for setting specification limits.
Usually specification limits are derived using engineering considerations. If there are no engineering considerations, and if the data represents a trusted benchmark (well behaved process), then quantiles from a fitted distribution are often used to help set specification limits. See “Statistical Details for Fit Distribution Options”.
|
Spec Limits
|
Computes generalizations of the standard capability indices, based on the specification limits and target you specify. See “Spec Limits”.
|
Save Fitted Quantiles
|
Saves the fitted quantile values as a new column in the current data table. See “Statistical Details for Fitted Quantiles”.
|
Save Density Formula
|
Creates a new column in the current data table that contains fitted values that have been computed by the density formula. The density formula uses the estimated parameter values.
|
Save Spec Limits
|
Saves the specification limits as a column property. See “Statistical Details for Fit Distribution Options”.
|
Save Transformed
|
Creates a new column and saves a formula. The formula can transform the column to normality using the fitted distribution. This option is available only when one of the Johnson distributions or the Glog distribution is fit.
|
Remove Fit
|
Removes the distribution fit from the report window.
|
Plot Format
|
Applicable Distributions
|
The fitted quantiles versus the data
|
• Weibull with threshold
• Gamma
• Beta
• Poisson
• GammaPoisson
• Binomial
• BetaBinomial
|
The fitted probability versus the data
|
• Normal
• Normal Mixtures
• Exponential
|
The fitted probability versus the data on log scale
|
• Weibull
• LogNormal
• Extreme Value
|
The fitted probability versus the standard normal quantile
|
• Johnson Sl
• Johnson Sb
• Johnson Su
• Glog
|
Rotate
|
Reverses the x- and y-axes.
|
Confidence Limits
|
Draws Lilliefors 95% confidence limits for the Normal Quantile plot, and 95% equal precision bands with a = 0.001 and b = 0.99 for all other quantile plots (Meeker and Escobar (1998)).
|
Line of Fit
|
Draws the straight diagonal reference line. If a variable fits the selected distribution, the values fall approximately on the reference line.
|
Median Reference Line
|
Draws a horizontal line at the median of the response.
|
Index
|
Index Name
|
Formula
|
CP
|
process capability ratio, Cp
|
(USL - LSL)/6s where:
• USL is the upper spec limit
• LSL is the lower spec limit
|
CIs for CP
|
Lower CI on CP
|
|
Upper CI on CP
|
||
CPK (PPK for AIAG)
|
process capability index, Cpk
|
min(CPL, CPU)
|
CIs for CPK
See Bissell (1990)
|
Lower CI
|
|
Upper CI
|
||
CPM
|
process capability index, Cpm
|
Note: CPM confidence intervals are not reported when the target is not within the Lower and Upper Spec Limits range. CPM intervals are only reported when the target is within this range. JMP writes a message to the log to note why the CPM confidence intervals are missing.
|
CIs for CPM
|
Lower CI on CPM
|
, where γ =
|
Upper CI on CPM
|
where γ = same as above.
|
|
CPL
|
process capability ratio of one-sided lower spec
|
(mean - LSL)/3s
|
CPU
|
process capability ratio of one-sided upper spec
|
(USL - mean)/3s
|
Distribution
|
Parameters
|
Goodness of Fit Test
|
Normal1
|
μ and σ are unknown
|
Shapiro-Wilk (for n ≤ 2000) Kolmogorov-Smirnov-Lillefors (for n > 2000)
|
μ and σ are both known
|
Kolmogorov-Smirnov-Lillefors
|
|
either μ or σ is known
|
(none)
|
|
LogNormal
|
μ and σ are known or unknown
|
Kolmogorov's D
|
Weibull
|
α and β known or unknown
|
Cramér-von Mises W2
|
Weibull with threshold
|
α, β and θ known or unknown
|
Cramér-von Mises W2
|
Extreme Value
|
α and β known or unknown
|
Cramér-von Mises W2
|
Exponential
|
σ is known or unknown
|
Kolmogorov's D
|
Gamma
|
α and σ are known
|
Cramér-von Mises W2
|
either α or σ is unknown
|
(none)
|
|
Beta
|
α and β are known
|
Kolmogorov's D
|
either α or β is unknown
|
(none)
|
|
Binomial
|
ρ is known or unknown and n is known
|
Kolmogorov's D (for n ≤ 30) Pearson χ2 (for n > 30)
|
Beta Binomial
|
ρ and δ known or unknown
|
Kolmogorov's D (for n ≤ 30) Pearson χ2 (for n > 30)
|
Poisson
|
λ known or unknown
|
Kolmogorov's D (for n ≤ 30) Pearson χ2 (for n > 30)
|
Gamma Poisson
|
λ or σ known or unknown
|
Kolmogorov's D (for n ≤ 30) Pearson χ2 (for n > 30)
|
3.15.229.111