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PaceXL 2
Summary of options and features
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Publishing details
PaceXL Add-in for Statistics, Version 2
ISBN: 0-958-6201-1-3
© 2003
Authors: Robin G. Boyle and Hugh B. Sarjeant
Published by:
Cicada Bay Pty Ltd
PO Box 343
Hampton Vic
Australia 3188
Phone/Fax 61-3-9521 0319
Email: [email protected]
www.pacexl.com
TOPICS COVERED IN THIS DOCUMENT:
Key features of PaceXL
Charts and Graphs
Ungrouped Data
Tabulations and Histograms
Grouped Data
Probability Distributions
Intervals and Tests
Analysis of Variance
Regression and Correlation
Time Series Analysis
Index Numbers
Quality Control Charts
PaceXL 2
Summary of Options
Page 1
KEY FEATURES OF PACEXL
PaceXL is a statistics and charts add-in for Microsoft Excel, designed for introductory and intermediate
courses.
PaceXL as an Add-in
 PaceXL operates inside Excel using its own added-in menu and toolbars.
 The full capabilities of Excel are retained.
 Excel is used for the input of data, and for the output for calculations, graphs and reports.
 PaceXL. works 'inside' Excel to streamline statistical analysis.
 It provides an extended range of statistical and graphical options.
 Several Excel workbooks using PaceXL can be open at once.
 Several distinct data sets can be stored on the one worksheet.
 In theory, over 200 variables and over 15,000 observations are permitted in a data set.
Menu, toolbar and dialogs
 PaceXL has its own menu item in the main Excel menu.
 Special PaceXL toolbars are used for general options and for charts.
 PaceXL options are activated by selecting from the PaceXL menu or toolbars.
 Each selection opens its own dialog window, from which calculations and charts can be generated.
Data Area and data types
 PaceXL works by selecting a data matrix (called a Data Area) before calling up any option.
 Column headers/labels in the Data Area are used as variable names for easy selection.
 The user can switch between different options using the same variables and data set.
 Variables in the Data Area itself can be numerical (age, height, weight, etc) or categorical (gender,
country of residence, etc).
 PaceXL identifies each of the two types.
 PaceXL will attempt to allow for missing values automatically: a blank cell is taken to be a missing value.
Analysing subsets and transformations
 As columns are identified by name, variables are easily selected / deselected.
 The 'Unstack' option breaks one variable into groups according to another index, or grouping, variable.
(For example, weights of adults can be grouped into 'weights for males' and the 'weights for females'.)
 The 'Select' option enables certain rows from the Data Area to be used in an analysis or for certain rows
to be excluded. (For example, if we wished to perform a regression analysis on just 'males'; or to exclude
an outlier observation set.)
 Common mathematical transformations (square, square root, logarithms, reciprocal, etc) are available.
Statistics options and results
 PaceXL has specific statistics routines (see Contents).
 All calculations from these routines are written to a single worksheet called the Results Sheet.
 New calculations are placed under each other in the Results Sheet.
 The Results Sheet can be renamed/saved. A new one is automatically created.
Charts and charts
 Hundreds of different types of charts and graphs can be calculated using PaceXL.
 The Charts and Graphs dialog is a separate dialog which generates a wide range of charts not available
directly in Excel (box plots, standard histogram, etc) and combination charts such as a box plot on top of
a histogram, or a normal distribution over a dot plot.
 Each of the statistics routines also generate charts and graphs specific to those routines, for example,
residuals for regression.
 Charts can be displayed in “large format” (suitable for lecture demonstrations).
 The user still has access to all the Excel chart/graph options, and the Chart Wizard.
 Many standard Excel charts (column, bar, etc) can also be generated through PaceXL.
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Saving and opening files
 PaceXL workbooks are normal Excel workbooks, and saved as disk files, and reopened, in the normal
manner.
 A Data Area still remains set, and the workbook can be used immediately with PaceXL.
 The Results Sheet and associated results are also retained.
Printing and print preview
 PaceXL output is all captured in standard Excel worksheets.
 Use normal Excel functions for printing and print preview.
Help system
 PaceXL has an extensive help system, including step-through instructions.
 Help is context sensitive, meaning that if F1 is pressed while in a particular routine, Help in that routine is
displayed.
Sample data sets
 Several sample data sets are available with PaceXL.
 Some are used for demonstrating PaceXL concepts via the tours-tutorials.
 Others may be useful to instructors for assignment and case study work.
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PaceXL - CHART AND GRAPH – TYPES
The Charts and Graphs dialog provides the options detailed below, grouped by page tab from the dialog
window.
One Variable
Notes:
 The charts from this tab use just one variable (column) from the Data Area
 Charts are based around frequency counts of occurrences of individual values (or categories)
For numerical variables:
 Column chart
 Bar chart
 Pie chart
 Dot plot
 Polygon
 Frequency curve
 Frequency column
 Ogive
 Box plot
 Pareto chart
 Normal probability plot
 Quick histogram
For categorical variables:
 Column chart
 Bar chart
 Pie chart
 Pareto chart
One or More 'Y'
Notes:
 These charts require at least two variables (columns) from the Data Area
 Only one variable may be drawn on the horizontal (X) axis
 The variable on the X axis may be either numerical or categorical
 More than one may be drawn on the vertical (Y) axis
 The variable(s) on the Y axis must be numerical
 Many of the charts on this tab are matched directly by charts available from Excel's Chart Wizard
Cross-tab (contingency table) or grouped (frequency distribution) input format recommended:
 Column chart
 Bar chart
 Pie chart
 Line chart
 Area chart
Numerical variables (raw / ungrouped) required or recommended:
 Scatter
 Multiple box plot
 Confidence intervals
Two or More 'X'
 Scatter - Grouped
Notes:
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One numerical selected for the Y axis, one numerical variable selected for the X axis, one categorical /
coded variable used as a grouping variable. In effect, a different scatter plot is drawn for each category /
value in the grouping variable.
 Scatter - Multiple 'X'
Notes:
Multiple pairs of Y and X variables are selected. Only numerical variables permitted.
Scatter 'Matrix'
Notes:
Able to draw a scatter plot of each pair of variables included in the selection list.
'Line' of best fit options:
 Linear
 Parabolic / quadratic
 Exponential
 Cubic
 Power
 No fit
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PaceXL - UNGROUPED DATA
Ungrouped Data - Broad functions
The Ungrouped Data routine is used for analysing 'raw' data, in particular, for calculating summary measures
such as the mean and standard deviation, but also for ranking, checking for outliers, etc. for numerical
variables, and for frequency counts for categorical variables.
If you wish to group your raw data into frequency distributions or cross-tabs, use Tabulations and
Histograms. If your data are already grouped, use Grouped Data.
The Ungrouped Data routine provides the options detailed below, grouped by page tab from the dialog
window.
Numerical Data
 Sample data - Summary measures (mean, standard deviation, etc.)
 Population data - Summary measures (mean, standard deviation, etc.)
 Percentile - for the percentage entered
 Trimmed Mean - for the percentage entered
Choose Measures
 Add to or reduce the range of summary measures calculated
Rank/Sort/Count
 Rank and Percent Rank
 Sort, Rank and Percent Rank
 Frequency Count
 Check Outliers
 Normal Distribution Ranks
 Test for Normal (goodness of fit)
Categorical Data
 Frequency count
Plots
For numerical variables:
 Column chart
 Bar chart
 Pie chart
 Dot plot
 Polygon
 Frequency curve
 Frequency column
 Ogive
 Box plot
 Pareto chart
 Normal probability plot
 Quick histogram
For categorical variables:
 Column chart
 Bar chart
 Pie chart
 Pareto chart
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PaceXL - TABULATIONS AND HISTOGRAMS
Tabulations and Histograms - Broad functions
The Tabulations and Histogram routine proceeds from the Ungrouped Data routine. It is used to group raw
data into frequency distributions or two-way cross-tabs (or cross-classification tables or contingency tables).
It also plots histograms and column graphs of those tables. If the data set is already in grouped form, use the
Grouped Data routine.
The Tabulations and Histograms routine provides the options detailed below, grouped by principal options
from the dialog window.
Frequency Interval option
 Frequency distribution table for equal or unequal classes
 Histogram for absolute, relative and cumulative frequencies
This is the default tab of the dialog. It operates on just one variable at a time. Users can specify intervals for
grouping values by entering the lower bounds of each class. (Only the first two are required if Equal Classes
are selected). The histogram plot includes a wide range of combination plots (such as histogram-dot plot).
Frequency Count option
 Frequency count table of individual values (no grouping by intervals)
 Frequency count plots (includes column, dot, box plot, quick histogram)
Operates on one variable at a time. Counts how often each value occurs. This option is suitable for discrete
numerical data or categorical data and is equivalent to the Frequency Count and Categorical options in
'Ungrouped Data'.
Multiple Plots option
 Frequency curve, polygon or histogram for two or more numerical variables grouped into classes
Useful for comparing two or more distributions. Because histograms plot on top of each other, obscuring
underlying plots, the default is the frequency curve. Classes are set in a similar way to Frequency Interval.
Two Variable Cross-Tab option
 Cross-tabs for total frequencies, percent of total, percent of rows and percent of columns
 Column graphs of the above tables
Operates on two variables at a time and in interval form. Numerical or categorical variables permitted. Either
discrete or continuous numerical data can be used. For example, we may want a cross-tabulation of 'Gender'
by 'Hours Worked' in 10-hour groups. The lower bounds of each interval for each variable are entered as for
Frequency Interval. A 'Chi-Square Test of Independence' is given as an option: note that it uses an alpha
level of 5%. The p-value from the test is also shown, which can be used to compare to other alpha levels if
required. Cross-tabulations can be graphed. For example, Percent of Row and Percent of Column tables can
be plotted as column graphs, thus providing an alternative to the tables for identifying whether relationships
exist between two variables.
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PaceXL - GROUPED DATA
Grouped Data - Broad functions
The Grouped Data routine is used when data are already in grouped form, either as a frequency distribution
or as a cross-tab. (A cross-tab is also known as either a two-way cross-tabulation or a contingency table).
With Frequency Distributions, the Data Area may include more than one frequency column (for example,
frequencies for males and for females and for the two combined).
(Note that the Tabulations and Histograms dialog is used for grouping ungrouped/raw data into frequency
distributions and cross-tabs. Once the data are grouped, the output from 'Tabulations and Histograms' is
similar to that obtained from 'Grouped Data'.)
The Grouped Data routine provides the options detailed below, grouped by page tab from the dialog window.
Frequency Distribution
With frequency distributions, the Data Area must include two data columns, including one column for the
midpoints of the classes for each interval. (The midpoints should not be included as the Row Headers.)
Output available includes:
 Sample data - Summary measures (mean, standard deviation, etc.)
 Population data - Summary measures (mean, standard deviation, etc.)
 Percentile - For the percentage entered
 Table/Plot: Single column - this generates a frequency distribution or histogram for the selected column
of frequencies
 Goodness of fit: Normal
 Multiple Plots - Frequency curve, polygon or histogram for two or more frequencies plotted on the one
graph
Choose Measures
 Add to or reduce the range of summary measures calculated
Two Variable Cross-Tab
 Cross-tabs for total frequencies, percent of total, percent of rows and percent of columns
 Column graphs of the above tables
 Test of independence
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PaceXL - PROBABILITY DISTRIBUTIONS
Probability Distributions - Broad functions
The two broad functions of the Probability Distributions routine are to:
 calculate probabilities for a given probability distribution
 calculate values of the 'variable' corresponding to given probabilities for a particular distribution
A Data Area is not required for any calculations.
Probability distributions available
The eight distributions available are:
 Uniform distribution
 Binomial distribution
 Poisson distribution
 Normal distribution
 Standard normal distribution
 t distribution
 Chi-Square distribution
 F distribution
Uses of this routine
The probability distribution routines can be used in a number of ways but in particular:
 to replace or extend probability tables of the type given in the back of textbooks
 to help solve standard textbook-type problems involving probability distributions
Performing calculations
In each case you need to:
 enter the parameters for the distribution, and
 either a probability value or the values of the 'variable'
Calculations are displayed in the dialog itself. Updating is immediate, once a value of a parameter or input
value is updated.
Use the Update button to update values once an input has been altered.
You can repeat calculations for the same distribution or change distributions. However, after changing
distributions, defaults are reset.
Saving results
 Current calculations will be written to the Results Sheet when the Save Results button is clicked.
 Plots of distributions can be saved using the normal Save Plot options
Plots of distributions
Plots are available for each distribution as calculations are performed. Plots can be in:
 normal format on the Results Sheet, or
 large format as a Chart Sheet.
Plots are updated immediately with any change in input or with the scroll bars. That is, the plots are linked to
the dialog window.
The Dialog box can be reduced in size using the 'Less Dialog' button in order to view more of a particular
plot.
Scroll-bars
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The scroll-bars can be used to examine incremental values in key parameters and input values. For
example, the standard deviation for the normal distribution can be altered gradually by clicking the scroll-bar.
If the plot is shown, the height/width of the distribution will gradually alter.
Note that the scroll bars may jump to a default value before returning to your required value. This can apply
to calculations and to plots.
Two X values
Two values for X may be entered for the normal, uniform, binomial and Poisson distributions (and two z
values for the standard normal distribution). The probability between these two values is calculated and can
be shown on a plot.
What-if possibilities
Using the scroll-bars, and other display options, 'what-if' situations can be investigated. For example,
gradually changing a parameter and viewing the effects on a result or plot. Or plotting a normal distribution
over a t distribution and altering the degrees of freedom, etc.
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PaceXL - INTERVALS AND TESTS
Intervals and Tests - Broad functions
The Intervals and Tests routine is used for inferential statistics for one or two sample situations. It is one of
the most powerful of the PaceXL statistics options. Input may come from a Data Area or from Summary
Measures. Numerical variables and categorical variables are permitted.
The Intervals and Tests option provides the options detailed below. (Functions are grouped by operation,
type of calculation, etc.)
Variable type
Calculations may be performed on:
 Numerical variables
 Categorical variables
One or two samples
Calculations can involve:
 'One Sample' data: just one variable or category/attribute involved
 'Two Sample' data: two variables or categories/attributes involved
Calculation types
There are three main types of calculations:
 Confidence Interval
 Hypothesis Test
 Sample Size
Numerical variables
In this tab, calculations are performed for numerical variables with the following options:
 One Sample, Mean: using the t distribution
 One Sample, Mean: using the z distribution
 One Sample, Variance/SD: using the Chi-square distribution
 One Sample, Median: Sign test
 Two Sample, difference between Means, Equal Variances: using the t distribution
 Two Sample, difference between Means, Unequal Variances: using the t distribution
 Two Sample, difference between Means, Dependent Samples: using the t distribution
 Two Sample, difference between Variances/SDs: using the F distribution
 Two Sample, difference between Medians: Wilcoxon Rank Sum, using the z distribution
Note that PaceXL uses the t distribution as the default distribution, even for large sample sizes (30 or more).
'Categorical' variables
In this tab, calculations are performed for categorical variables (including numerical coded categorical
variables) with the following options:
 One Sample, Proportion: using the z distribution
 Two Sample, difference between Proportions: using the z distribution
With categorical variables, an attribute value may need to be selected, eg 'Male' or 'Female', or '0' or '1'.
Parametric v nonparametric methods
Statistical inference techniques are sometimes categorized as:
 parametric methods
 nonparametric methods
The Intervals and Tests routines includes both categories of techniques.
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Finite population correction
If a population is not large:
 the Finite Population Size correction factor can be employed for one sample tests
Data source
The input source for the sample data can be:
 via the Data Area (the variables in the Data Area are listed for selection and PaceXL automatically
calculates sample summary measures for each variable selected)
 via Summary Measures (if the sample summary measures are already known the user enters the values
directly into the dialog)
Extra input required
Extra input may be required in the form of selecting or entering the:
 Confidence coefficient
 Level of significance (alpha)
 Value of the population parameter for the Null Hypothesis
 Required Margin of Error
 Estimates of population parameters
 Sample size, mean, standard deviation, etc.
Results
Calculations are written to the Results Sheet and include:
 Full details of the type of calculation performed
 Details of the data input
 A summary of the intermediate calculations and key results
 A final result or suggested conclusion
Unstack/Select
Note that Unstacking and Selecting from a Data Area are options for this routine. These options provide a
powerful means of analysing subsets of your data, for example, testing for any difference between Male and
Female respondents
Calculating the sample size
Two choices for calculating the required sample size, n are:
 numerical variable and estimating the population mean
 categorical variable and estimating the population proportion
Plots
The following plots are available:
 hypothesis tests (for selected numerical variable tests or categorical variable tests)
 confidence intervals for one or more numerical variables
 box plots for one or more numerical variables
 single variable plots for numerical variables (box plots, dot plots, quick histogram) and categorical
variables
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PaceXL - ANALYSIS OF VARIANCE
Analysis of Variance - Broad functions
The Analysis of Variance routine is used for one factor and two factor analysis of variance.
The options available with the Analysis of Variance routine are given below.
One Factor
 Standard one factor ANOVA output
 Summary measures for input data
Options include:
 Confidence interval calculations
 Multiple comparisons
 Repeated measures
Two Factor
Data are assumed to be in Stacked format and an Index variable must be included in the Data Area.
 Standard two factor ANOVA output with interaction
 Summary measures for input data
Plots
 Box plots for all samples, including values
 Confidence intervals using pooled standard deviation
 Confidence intervals using individual sample standard deviations
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PaceXL - REGRESSION AND CORRELATION
Regression and Correlation - Broad functions
The Regression and Correlations routine is used for exploring for relationships between variables using
regression modelling, correlation and scatter plots.
Models are generated by simply selecting and deselecting variables as required from the dialog window.
Examples of the types of output include:
 regression model with standard output
 optional full table of residuals
 correlation matrices
 'scatter matrix' plot option
 prediction intervals
 residual plots, including histogram, normal curve and box plot options
 automated regression options including stepwise and best subsets
The Regression and Correlation routine provides the options detailed below, grouped by page tab from the
dialog window.
Correlation
Produces the following matrices for the pairs of variables chosen:
 pairwise linear correlation coefficients
 t statistics for zero correlation
 probability values (p-values) for zero correlation
 r-squared (optional)
Matrices can be printed as three (or four) separate tables, or printed as a table with three (four) rows per
combination.
Scatter
 Draws scatter plots for every pair of variables chosen in the list
 Fit options include linear, quadratic, exponential, cubic and no fit
Regression
This is the default, and principal, tab of the Regression and Correlation dialog. It is used to generate all
regression models, including automated methods. Once a mode has been generated, predictions and plots
can be made for that model.
Input includes:
 Y (dependent variable)
 X (independent or explanatory) variable(s)
 confidence % level required
 which residuals to be analysed for normality
Standard Output includes the following:
 regression equation and model
 regression coefficients, including p-values and confidence limits
 variance inflation factor (VIF) for each explanatory variable
 summary measures, such as R-squared, adjusted R-squared, standard error of estimate, Durbin-Watson
 ANOVA table
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Optional output including:
 fitted results
 residuals, including standardized residuals, studentized (TResiduals), leverage, Cook's distance
 flagging of observations for influence analysis
Optional table of:
 intermediate calculations, including sums of cross-products
Optional calculation:
 with intercept
 no intercept
Optional input:
confidence interval %
Predictions
Provides intervals for:
 mean of Y (confidence interval)
 individual Y (prediction interval)
Predictions relate to the latest regression model generated.
Regression Plots
Produces the following plots:
 Fitted
 Residuals
 Adjusted residuals
 standardized residuals
 Residuals v Normal
 Prediction Bands (for simple regression - one explanatory variable)
Automated Methods
This tab is used to select, and set up, 'automated' regression methods. The default is multiple regression.
Automated options are:
 stepwise regression
 forward selection
 backward elimination
 best subsets (with Cp statistics)
The output level can be set to:
 brief
 medium
 full
The variables to be included in the model are selected from the Regression tab.
Select and New Variable
 Select can be used to choose / exclude rows from the Data Area
 Unstack cannot be used
 Temporary variables can be created (squared, logs, etc) for inclusion in regression models
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PaceXL - TIME SERIES ANALYSIS
Time Series - Broad functions
The Time Series Analysis routine is used for analysing time series data. A range of calculation and plotting
methods are available for exploring past behaviour, and for forecasting. Annual and sub-annual (monthly,
quarterly, etc) data can be analysed.
Data period:
 PaceXL attempts to keep track of time periods and sub-periods
 Except for 'Seasonal Analysis' the data can be annual or sub-annual, or some other period (such as,
five-yearly)
Number of variables:
 Except for Plot Series, only one variable is analysed at a time
Accuracy of fit measures given:
 R-squared
 MSE (Mean Square Error)
 MAD (Mean Absolute Deviation)
 RMSE (Root Mean Square Error)
 MAPE (Mean Absolute Percentage Error)
Forecasts available for:
 trend fitting
 exponential Smoothing
 autoregression
Plots available for all tabs:
 including fitted results and residuals
 semi-log options
 trend fits
The Time Series Analysis routine provides the options detailed below, grouped by page tab on the dialog
window.
Set Period
Data period choices:
 year
 half year, quarter, month, week or day
 other (for example, hourly data for a day, or daily data for a five-day week, or five-yearly)
Data period input:
 year/period of first observation
 number of first year or sub-period
Plot Series
Options for plotting past data for one or more variables:
 arithmetic scale
 semi-log scale
 percentage change
 index form
 trend
 trend: semi-log
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Differences
Calculation and/or plot options:
 first differences
 second differences
 percentage differences
 index form
Moving Average
Calculation and/or plot options:
 moving average
 percent of moving average
 absolute and percent residuals
 moving average differences
 trend fits
Moving averages are centered for an even number of periods per cycle.
Seasonal Analysis
Calculation and/or plot options:
 seasonal indexes
 seasonally adjusted (deseasonalized) results
 absolute and percent residuals
 trend fits
Conditions and notes:
 with Seasonal Analysis, annual data are not permitted
 seasonal indexes are calculated using the modified mean method
 there must be 2 or more sub-periods per main period (year/cycle)
 there must be at least four complete years/cycles of data because
 cycles may be lost due to use of a centered moving average
 two cycles are lost due to the modified mean method
 thus if the data being analysed are quarterly data, there must be 16 or more data points
 note that 'Actual Y as a Percentage of Moving Average' calculations, which are used to calculate
seasonal indexes, can be obtained from the Moving Average option
Trend Fitting
Trend types:
 linear
 exponential
 quadratic
Calculation and/or plot options:
 trend equation only
 full calculations
 seasonal adjustment option for sub-annual data
 forecasts only
 all trends summary (for the trend types given above)
 absolute and percent residuals
 seasonalized trends (sub-annual data)
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Exponential Smoothing
Calculation and/or plot options:
 single exponential smoothing
 double exponential smoothing
 triple exponential smoothing models
 seasonal adjustment option for sub-annual data
 different alphas summary (0.1, 0.2 to 0.9)
 absolute and percent residuals
 seasonalized forecasts ('sub-annual' data)
Conditions and notes:
 one constant (alpha) for single and triple smoothing
 two constants (alpha and gamma) for double smoothing
 the triple exponential model is not a specific model, but is included for demonstration/example purposes
Autoregression
Calculation and/or plot options:
 lags of 1 to 12 permitted
 one predictor lag included for lag value
 all predictor lags included
 autocorrelation coefficients
 fitted and residuals plots
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PaceXL - INDEX NUMBERS
Index Numbers - Broad functions
The Index Numbers routine has two broad functions.
 The first is the calculation of index numbers. This option includes calculating unweighted and weighted
index numbers (price or quantity) based on inputs of prices and quantities.
 The second is the use of price index numbers. This option allows for deflating a value series using a
price index series, calculating percentage changes in an index series (for example, calculating the
percentage rate of change in the Consumer Price Index to provide an estimate of the period inflation
rate), and deriving other index series.
The Index Numbers routine provides the options detailed below, grouped by page tab from the dialog
window.
Calculating Indexes
Index types:
 price indexes
 quantity indexes
Weighting:
 unweighted
 weighted (based on selection of a quantities column)
Output options:
 indexes only
 full calculations
Notes:
To calculate price (quantity) indexes, select the column for base period prices and the given (latest) period
prices, and the quantities period.
Unweighted indexes
 unweighted aggregate of prices index
 unweighted average of price relatives index
Weighted indexes
 weighted aggregate price index
 weighted average of price relatives index
PaceXL cannot detect the period to which the weights belong. However, by selecting the appropriate weights
in the Data Area, a Laspeyres Index (base year weights) or a Paasche Index (latest year weights) can be
calculated.
Using indexes
One value series and one price series:
 deflated series using a value and price series
 percentage changes in the value and price series
 various index forms of both series
 plots of these calculations
Two or more value series and one price series:
 deflated results
 percentage changes
 index forms of results
 plots of these calculations
PaceXL 2
Summary of Options
Page 19
PaceXL - QUALITY CONTROL CHARTS
Quality Control Charts - Broad functions
This is mainly a plotting routine, but calculations underlying these plots can also be generated. Control charts
can be drawn for numerical variables and categorical variables (or attributes). The Quality Control Charts
routine provides the options detailed below, grouped by page tab from the dialog window.
Numerical Variables
Chart types and calculations:
 X-Bar chart
 s chart
 R chart
Options with one/some of the above (Exclusions permitted):
 Pooled SD (standard deviation)
 SD-Bar
 R-Bar
 Population SD (standard deviation)
 Population mean
 Tests for non-conformity (X-Bar chart only)
Test available for X-Bar chart for non-conformance:
 Test 1: One or more means more than 3 sigmas from CL (center line)
 Test 2: Nine means in a row on same side of CL
 Test 3: Six means in a row, all increasing or all decreasing
 Test 4: Fourteen means in a row, alternating up and down
 Test 5: Two out of three means more than 2 sigmas from CL on same side
 Test 6: Four out of five means more than 1 sigma from CL on same side
 Test 7: Fifteen means in a row within 1 sigma either side of CL
 Test 8: Eight means in a row more than 1 sigma either side of CL
Attributes (categorical variables)
Chart types and calculations:
 p chart
 np chart
 C chart
 U chart
Options with one/some of the above (Exclusions permitted):
 Population proportion, p
 Population mean
 Tests for non-conformity (p chart only)
Test available for p chart for non-conformance:
 Test 1: One or more proportions more than 3 sigmas from CL
 Test 2: Nine proportions in a row on same side of CL
 Test 3: Six proportions in a row, all increasing or all decreasing
 Test 4: Fourteen proportions in a row, alternating up and down
Factors
 Prints table of factors for control charts
Exclusions
 Samples can be excluded from the analysis from the Numerical Variables or Attributes tab
PaceXL 2
Summary of Options
Page 20