statistiXL 1.8

statistiXL 1.8 is a data analysis package used as as a Windows add-in, which is quick to learn and easy to use. With this tool, you do not have to spend hours with manuals just learning how to perform the analyses you need in order to get to the really important results.

With various features, Excel™ provides an ideal environment for data input, manipulation and calculation. By leveraging this familiar environment, statistiXL greatly extends this feature set to encompass high powered statistical analysis without the need to learn how to use an entirely new application from scratch.

Data stored in existing Excel™ spreadsheets can instantly be subjected to a wide range of statistical tests (many frequently not seen in other analysis software) including:

• Analysis of Variance (ANOVA)
• Cluster Analysis
• Contingency Tables
• Simple, Partial, Multiple and Canonical Correlation
• Linear and Circular Descriptive Statistics
• Classification and Grouping Discriminant Analysis
• Factor Analysis
• Goodness of Fit Tests such as Binomial, Circular, Normal and Poisson
• Simple and Multiple Linear Regression
• Nonparametric Tests such as Friedman, Kruskal-Wallis and Mann-Whitney
• Principal Component Analysis (PCA)
• Univariate and Multivariate t-Tests

And because statistiXL outputs the results of its analyses straight into an Excel™ spreadsheetData analysis: Editing the axis of a plot that has been created by statistiXL you can use the tools that you are already familiar with to arrange and format both textual and graphical output: changing fonts, rearranging cells, altering the scale on the axis of a graph etc etc. You can even subject  the results to further analysis using either statistiXL or Excel’s™ numerous built in functions.

Major Benifits:

1. A familiar and powerful user interface for entering and manipulating data
2. A wide variety of formatting options for altering the appearance of results
3. The presence of a sophisticated charting package that allows both the manipulation of charts produced by statistiXL and the manual creation of new charts based on statistiXL's output.
4. The ability to perform further ad hoc analysis using Excel's™ own built in functions and calculating abilities.

Major Features:

1. Analysis of Variance
   • Analysis of Variance (ANOVA) is used to determine whether there is a difference between three or more categorical sets of values e.g. three species, four types of drug, 7 days of the week. Analysis of Covariance (ANCOVA) on the other hand, while also used to determine whether there is a difference between categorical sets of values (two or more in this case) also takes into account the effect of one or more numerical variables called covariates e.g. three species as categorical variables taking into account the effect of differences in body mass as a numerical covariate.
   • statistiXL provides a very comprehensive module for the analysis of variance and covariance. Both univariate and multivariate ANOVA and ANCOVA are supported. Factors can be specified as fixed or random and the nesting of factors is also supported. Simplified dialog boxes aid the rapid analysis of full factorial and repeated measures models, while for more advanced analyses a comprehensive dialog box is available that allows custom models to be specified precisely detailing the factors and interactions to be included in the analysis. Post Hoc Tests are provided so that you can drill down into your dataset and see what, if any, the major differences between groups are. Tukey, Student-Newman-Keul and Scheffe test are included so you are not constrained to a single type of analysis.
   • Results from the ANOVA moduleResults are presented in tabulated form, starting optionally with a table of simple descriptive statistics for each group (e.g. mean, standard error, count etc). The overall test for the model is presented next, followed by individual tests for each effect included in the model. Finally, if post hoc analyses were chosen to be performed, a table of all pairwise group comparisons is presented for each factor and for each test type chosen.
   • The help file included with statistiXL provides and introduction to Analysis of Variance and a comprehensive range of 17 examples detailing how to use statistiXL to analyse different design models of ANOVA and ANCOVA including Single and Multivariate, Full Factorial and User Defined, Fixed and Random Factors, Nested, Latin Square, Randomised Block, Split Plot and Repeated Measures.
2. Clustering
   • Cluster Analysis, also called Numerical Classification, is used to arrange objects of interest into a branching hierarchy of groups (a tree, or dendrogram) based on how similar or dissimilar the objects are in terms of a number of attributes that are known for each object. For example, countries (the objects or cases) could be clustered based on a number of socioeconomic attributes such as population size, average annual income, life expectancy and annual per capita expenditure on health. The outcome of such an analysis would be to show which countries are most similar in terms of these attributes.
   • Such hierarchical clustering can be either agglomerative, where clustering starts with the individual cases and proceeds by grouping the most similar cases together, or divisive, where the analysis starts with all cases in a single group and proceeds by dividing groups into two until only individual cases remain. statistiXL currently supports Agglomerative Cluster Analysis which can be used for data exploration or reduction, model or hypothesis testing, and tree (dendrogram) summary of groups.
   • statistiXL provides a variety of options for hierarchical agglomerative clustering. Firstly, there are a variety of methods for deriving the similarity matrix, dissimilarity matrix or distance matrix that forms the basis for grouping cases together. Methods are provided for clustering binomial data (e.g. Jaccard, Hamann, Kulczynski, Pattern Difference, Euclidean Distance, Squared Euclidean distance), quantitative data (e.g. Bray & Curtis, Canberra, City Block, Euclidean Distance, Squared Euclidean distance, Pearsonian correlation), or mixed data. Secondly, having fused two groups, the matrix is recalculated using a combinatorial algorithm, and the next fusion is determined. statistiXL provides a variety of combinatorial algorithms, including nearest neighbour, furthest neighbour, median, centroid, group average, Ward’s and flexible methods.
   • Results from the Clustering moduleResults are presented both in tabular and graphical form. They start with the similarity (or dissimilarity or distance) matrix derived from the attribute data according to the selected method. Then, the order of fusion of cases is given, with the corresponding similarity (or distance), until the final completely fused group (root) is reached. A cophenetic correlation coefficient is provided, to indicate how similar the final hierarchical pattern and initial similarity (or distance) matrix are. A dendrogram (tree graph) is provided to graphically summarise the clustering pattern. The dendrogram starts with all individuals as separate clusters and shows the combination of fusions back to a single root and can be either text (character) based or presented as an Excel™ plot.
   • The help file included with statistiXL provides an introduction and detailed overview of clustering, and describes the general input and output options. A comprehensive range of eleven different clustering examples are provided, to detail how statistiXL is used to analyse different types of data (e.g. binomial, quantitative or mixed) and to illustrate the commonly used combinations of similarity/distance matrices and combinatorial algorithms.
3. Contingency Tables
   • Dialog Box for Contingency Table AnalysisA contingency table is a table of counts or frequencies. It lists the number of times that each of 2 or more variables falls into a variety of different categories. For example, if you were examining differences in the frequency of hair colour between the sexes, you would have a table with 2 variables (sex and hair colour) and a number of categories (eg male and female for sex and black, brown, red, blonde etc for hair colour.). The table would then record the frequency with which each combination of categories was observed e.g. how many blonde haired males, how many black haired females etc. A contingency test simply examines the null hypothesis that the frequencies of observations found for one variable are independent of the frequencies of observations in the other. For the above example this could be stated as “The frequency of hair colour is the same for each sex”.
   • statistiXL provides a flexible module for analysis of contingency tables. Two-way and multi-way contingency tables can be analysed. The statistics available for the frequency test are Chi² and log-likelihood, the latter being a good alternative approach to Chi² if the expected frequencies are small. Yates’ and Cochran’s corrections for continuity are provided for 2x2 contingency where such an adjustment is recommended because of the low degrees of freedom.
   • There are no post hoc tests for contingency table analysis, but a divided Chi² analysis can be used if the null hypothesis is rejected (i.e. if it is concluded that the observed distribution differs significantly from the expected distribution) to explore which particular category or categories is contributing to the difference. statistiXL explains how to subdivide a contingency table and warns of the limited statistical value of this approach. A Heterogeneity Chi² can be used in a contingency table analysis to determine if a number of sets of observed frequency data can be combined into a single set. statistiXL explains how to analyse contingency tables for heterogeneity.
   • Results from the Contingency moduleResults are presented in tabulated form, starting with an optional table of a summary of the observed and expected frequencies. The test results are then presented, with the Chi² and Log-likelihood values, along with their degrees of Freedom and P values.
   • The help file included with statistiXL provides an overview of contingency table analysis and three examples, a 2x2 contingency table with heterogeneity Chi² analysis, a 2x4 contingency table with subdivided Chi² analysis, and a 2x2x2 multiway contingency table analysis.
4. Correlation
   • Correlation is a measure of the relationship between two variables, or sets of variables. Do the variables increase and decrease together (positive correlation)? Does one variable increase as the other decreases (inverse correlation)? Or is there no relationship at all between the variables (no correlation)? The correlation coefficient is a measure of the strength of the correlation; it varies from –1 (perfect inverse correlation) through 0 (no correlation) to +1 (perfect positive correlation). The computations for correlation are similar to those for the regression of independent and dependent variables, but for correlation there is no assumption of causation (i.e. while the variables may change together in some way, one variable is not necessarily causing the other to change).
   • statistiXL provides an extensive module for parametric correlation analyses, with options for bivariate, multivariate, partial, multiple and canonical correlation. Nonparametric correlation procedures are also available. Bivariate (simple) correlation is the measure of interrelationship between one variable and another. Multivariate correlation is a simple extension of bivariate correlation to more than two variables, exploring the simple bivariate correlation for all pair-wise combinations of the variables. Partial correlation is the correlation between two variables when taking into account one or more additional variables (e.g. correlating the times it took participants to complete each of 2 obstacle courses while taking into account measures of their fitness and IQ). Multiple correlation is the interrelationship between multiple variables examined collectively. Canonical correlation is a multivariate statistical method which determines the linear relationship between two sets of multivariate variables (e.g. the relationship between measures of environmental type and the species of plants found in different environments).
   • Results from the Correlation moduleThe format of Results varies somewhat with the different forms of correlation. In general, summary descriptive statistics are provided as an option. The correlation matrix of r values for all combinations of the selected variables is presented, along with a corresponding matrix of P values for each of the r values. A graphical scatterplot (or scatterplots) is also provided as an option (except for partial and multiple correlations).
   • The help file included with statistiXL provides an introduction to correlation, discusses input and output options, and provides an example for each of the types of correlation, simple bivariate (for 2 and for 5 Y variables), partial correlation, multiple correlation, and canonical correlation.
5. Descriptive Statistics
   • The descriptive statistics feature of statistiXL provides a quick and easy summary of the basic parametric and nonparametric statistics that describe a sample of values. Unlike many packages, statistiXL provides modules for both linear data and circular descriptive statistics.
   • A sample of values collected using a linear scale (e.g. length, mass, temperature) can be described by a variety of descriptive statistics, the more common being the mean, median, variance and standard deviation. The descriptive statistics which can be provided (by user selection) with the linear descriptive statistics module are: mean, median, mode, standard error, standard deviation, variance, coefficient of variation, lower and upper confidence limits, 25th and 75th percentiles, sum, minimum and maximum values, nth smallest and largest values (with user input of n), range, count, skewness (with probability if count >9) and kurtosis (with probability if count >19). If these data are sampled at random from a normal distribution, then the data are best summarized by the parametric statistics such as mean, variance and standard deviation. If the data are sampled from a non-normal distribution, then the non-parametric statistics such as median, mode, percentiles and range may be more appropriate statistics for summarizing these data. Options for graphical output includes a “box and whisker” plot and an error bar plot.
   • Results for Linear Descriptive StatisticsA sample of values collected using a circular scale (e.g. time of day or compass bearing) can be described by a variety of descriptive statistics, the more common being the mean angle, angular variance and angular standard deviation. The information provided in the circular descriptive statistics module is mean angle, angular standard deviation, angular variance, circular standard deviation, circular variance, lower and upper 95% confidence limits for the mean angle, and the count. Circular data can be examined for goodness-of-fit to a circular distribution using the Goodness-of-Fit module. Optional graphical output includes a circular plot.
   • The help file included with statistiXL provides an overview of descriptive statistics, and an example of linear and circular descriptive statistical analysis.
6. Discriminant Analysis
   • Discriminant Analysis is a technique used to determine which of a number of measured variables are important in distinguishing between objects belonging to known groups. For example a biologist could measure different morphological characteristics (e.g. limb lengths, skull sizes etc) of a range of species and use discriminant analysis to determine which of the measured traits are most useful in predicting species membership. This analysis can typically have two different objectives: 1) To identify the relative contributions of the variables in maximally discriminating between the groups, or 2) To determine mathematical functions based on the measured variables that can then be used to classify new data into the original groups.
   • statistiXL provides modules for both grouping and classification discriminant analysis. Both analyses provide discriminant functions that best allow for separation of the known groups based upon the measured variables.  Testing for excessive colinearity between variables is catered for via estimates of tolerance. In addition to this, the classification module allows for the classification of cases with unknown group membership based on these previously determined functions. The effectiveness of these functions is estimated by also reclassifying the original data (i.e. that belonging to cases from known groups) in order to determine the proportion that are correctly classified. statistiXL also provides an improved estimate of the error rate via the holdout method, in which each case to be classified is in turn excluded from the dataset when calculating the discriminant functions to be used for that particular classification.
   • Results from the Discriminant Analysis moduleResults from the Discriminant Analysis moduleResults are presented with an optional display of descriptive statistics for each group/variable combination and the covariance matrix showing the relationships between measured variables. Next, eigenvalues are given (indicators of the amount of variance in the dataset encompassed in a discriminant function), along with values for Wilk’s lambda, Chi2, degrees of freedom, and P value for each discriminant function. Unstandardised and standardised discriminant functions (i.e. the coefficient scores) are then tabulated, along with group centroids. Individual case scores are provided and optional scatterplots of casewise discriminant scores can be created for each pair-wise set of selected discriminant functions; the scatterplots can include graphical representations of the contributions of each variable to the discriminant functions. For classification analysis, a classification table is given for the data set used to derive the classification functions, indicating the proportion of correct classifications for each group. Optionally, a classification table derived from the holdout procedure can also be presented. The classification group scores for an alternate data set (if entered) are then given.
   • The help file included with statistiXL provides an overview to discriminant analysis, and gives two examples of grouping discriminant analysis (2 groups and 3 groups), and two examples of classification discriminant analysis (2 groups and 3 groups).
   • Factor Analysis
   • Factor Analysis is a procedure that seeks to determine a reduced number of variables, called factors, that explain much of the variation present in a larger number of measured variables. For example Factor Analysis of the results of a questionnaire given to students may reveal that groups of questions relating to differences in mechanical and artistic ability are important in influencing the choice of career path. If so, these groups of questions would come out as separate factors, a factor comprised mainly of the results of mechanical ability questions and another based on the artistic questions.
   • statistiXL provides a comprehensive module for Factor Analysis, with a variety of analytical options. Either the correlation or covariance matrix can be used in calculating the factors. The number of factors to be extracted can be established by several different criteria: 1) the number of factors can be chosen to encompass a specified percentage of the total variance in the original data, 2) you can choose to extract factors with eigenvalues greater than a set value, 3) you can extract a specific number of factors. A variety of methods are included for determining the factors including the principal component method (not to be confused with principal component analysis), principal factor method and maximum likelihood method. The axes of the resulting factors can then be rotated to improve the factor structure using either Varimax, Quartimax or Equamax procedures.
   • Results from Factor AnalysisResults are presented in tabular form. Descriptive statistics and the correlation or covariance matrix are provided, if these options were selected. Eigenvalues are then listed, along with the percent and cumulative percent of the variation in the original data that they encompass. Communalities are then given for each extracted factor. Unrotated factor loadings (and rotated loadings if selected) are listed, along with case-wise factor scores. A scree plot and factor plot are produced if these options are selected.
   • The help file included with statistiXL provides an overview of factor analysis, and an example of factor analysis using the principal component method, with rotation.
7. Goodness of Fit
   • A Goodness of Fit Test simply examines whether a data set conforms to an expected distribution. It is often needed to test whether a set of numerical data come from a certain theoretical and continuous distribution, such as those described as Normal, Binomial, Poisson or Circular. For example, a Normal Distribution has most values grouped around a mean, with progressively fewer values as you move away from the mean in either direction. Such a distribution could be expected to reflect the pattern of body weights in a given area with a few really light or really heavy people but most people being of average weight.
   • statistiXL’s module for goodness of fit testing provides a number of options for testing Binomial, Circular, Normal, Poisson, Uniform, and User-Defined theoretical distributions. Further options with uniform distribution analysis are for nominal categories (categories without any particular order e.g. hair colour), ordinal groups (categories with an inherent order e.g. moisture level categories ordered from moist to dry), and ordinal data on a continuous scale (e.g. frequencies of moths at various heights on a tree). The distribution test for ordinal groups also includes the Kolmogorov-Smirnov goodness of fit test for discrete data, which uses the cumulative frequencies (possible because the categories are ordered and can be meaningfully cumulated to determine differences between expected and observed frequencies) and is more powerful than the traditionally used Chi² test, especially if the sample size is small or expected frequencies are small. The distribution test for ordinal data on a ratio scale also includes the Kolmorogov-Smirnov goodness of fit test.
   • Results for Chi Square Goodness of Fit testResults are presented in tabular form, starting with the basic frequency table if this option was selected. Then, the statistical results for the Chi² and log-likelihood test of fit are given, along with the degrees of freedom and P value. For normal distribution tests, the Kolmogorov-Smirnov test statistic, df and P are presented along with measurements of skewness and kurtotis (and P values for sufficiently large data sets).
   • The help file included with statistiXL provides an introduction to distribution testing, and has a comprehensive set of examples, including Binomial, Circular, Normal and Poisson Distribution tests, three Uniform Distribution tests (for nominal, ordinal groups, and ordinal on a ratio scale data), and a User-Specified Distribution test.
8. Linear Regression
   • Linear regression attempts to explain the variation present in one variable (for example Height) in terms in terms of a linear relationship to variation in one or more predictor variables (for example Age). The variable you are attempting to predict is assumed to be dependent upon the predictor variables in some way i.e. there is a cause effect relationship, and this variable is termed the dependent variable. In the above example, height may be expected to depend on age whereas the reverse is not true (i.e. your age isn’t determined by your height). Linear regression can be performed with a single predictor variable, this is called Simple Linear Regression, or with 2 or more predictor variables, a process called Multiple Linear Regression. Stepwise Multiple Regression attempts to select a subset of predictor variables that best describe any existing relationship with the dependant variable, excluding those variables that add little to the predictive power.
   • statistiXL provides comprehensive Model I regression analysis. The module allows the selection of one or more predictor variables for each single dependent variable. Options include forward or backwards stepwise regression (with P level to enter or remove), forcing of the relationship through the origin, and graphical output (normal probability plot, residuals plot, scatterplots).
   • Results from a Simple Linear RegressionResults from a Simple Linear Regression Results from regression analysis are presented in tabular form and graphical form. Summary statistics are provided, if this option is selected. Statistics include the R², the correlation coefficient, the adjusted R², and the standard error of the estimate. An ANOVA table is given, to summarise the significance of the regression relationship. The regression coefficients, including intercept and regression slope, are given with standard errors, confidence limits, t and P values. Optionally, Residuals, Standardised Residuals and Studentised Residuals can be output and for multiple regression Partials can also be produced.
   • The help file included with statistiXL provides an introduction to linear regression analysis, and a number of examples including simple  regression, regression forced through the origin, multiple regression, stepwise multiple regression and polynomial regression.
9. Nonparametric Tests
   • Nonparametric statistical tests are distribution-independent tests that are used to analyse data for which an underlying distribution (such as the normal distribution) is not assumed. Non-parametric statistics have a number of advantages over parametric statistics. They can be quick and easy to use as they often use ranks or signs of differences rather than the values themselves. They can reduce the work of data collection because data can be ranks or simple scores rather than precise measurements. Sampling procedures do not assume homogeneity of variances, for example between locations or over time. There are fewer assumptions about, for example, the underlying distribution. But, there are possible disadvantages, particularly the failure to use a distribution if one is appropriate and the failure to use all of the available information.
   • statistiXL provides a diverse array of nonparametric tests. The sign test can be used to examine whether two populations have the same median, and for observations in pairs with one of each pair coming from each population. Various modifications of the sign test can be used for specific tests (e.g. trend, sign correlation, and a sign test for predicted patterns). The Friedman test for blocked data is equivalent to a sign test, but for more than 2 groups. The Mann-Whitney test (U statistic) is a nonparametric test that uses the ranks of two independent samples, from the highest to lowest (or lowest to highest), to calculate the U statistic; it is the nonparametric analog to the parametric two-sample t-test. The Wilcoxon's signed-rank test ranks the differences between pairs of data (or single data set of a sample) and compares the sum of positive and negative ranks with a critical U value; it is the nonparametric analog to the parametric paired t test. The Kruskal-Wallis test is a nonparametric test for the comparison of 3 or more treatment groups, which are independent; it is the nonparametric equivalent to analysis of variance (ANOVA). A common nonparametric correlation test is Spearman’s rho rank correlation coefficients; this is analogous to the parametric Pearson’s correlation coefficient. Mood's Median Test examines whether two or more samples come from a population having the same median. The Wald-Wolfowitz Runs test analyses a sequence of observations, or compares random samples which are mutually independent, for two or more outcomes e.g. two species of antelope, or three brands of automobile.
   • Results from a Wilcoxon Paired Sample testResults are presented in tabular form. Format varies for different nonparametric tests, but generally includes an optional descriptive statistics or ranks summary, and the appropriate statistic, degrees of freedom, and P value.
   • The help file for statistiXL provides an overview of nonparametric statistical test, and a comprehensive range of 14 examples, including sign tests, Mann-Whitney U test, Wilcoxon paired-sample test and test of symmetry about the median, median tests, Kruskal-Wallis test, Friedman’s test, Wald-Wolfowitz runs test, and Spearman rank correlation.
10. Principal Components
    • As with Factor Analysis, Principal Component Analysis is a technique that attempts to reduce complex data sets consisting of many different variables to a smaller set of new variables that still manage to describe much of the variation in the original data. These new variables, called Principal Components, are chosen to be independent (i.e. the new variables are not correlated whereas the original, untransformed variables may have been correlated) and to maximise the variance found in the original data set. The more significant PCAs are selected based on their eigenvalues, and hopefully far fewer PCA variables (e.g. one or two) are required than there were original variables. These fewer Principal Components can then be further analysed by Regression Analysis or ANOVA/MANOVA. Thus, the role of Principal Component Analysis has been to reduce a large number of variables into fewer, simpler ones. Principal Component Analysis is an alternative to Factor Analysis (both seek to find a simpler structure for a set of variables) but Principal Components are linear combinations of variables whereas variables are linear combinations of Factors.
    • statistiXL provides a number of options for Principal Component Analysis. Either the correlation or covariance matrix between variables can be selected as the basis for analysis. All Principal Components can be extracted or a subset of these based on limits such as the number to extract, the percent of variance to explain or the value of an eigenvalue. Screeplots can be produced to help in the visual determination of the appropriate number of Principal Components to extract.
    • Results from a Principal Component AnalysisResults from a Principal Component AnalysisResults are presented in tabular and graphical form. Descriptive statistics and the correlation or covariance matrix are displayed, if these options were selected. The eigenvalues are then tabulated along with the percent of variance and cumulative percent of total variance evident in the original dataset that each of the extracted Principal Components explains. The Component Loadings are then listed followed by the Principal Component score coefficients (eigenvectors). The case-wise PCA scores for each extracted component are listed, if this option was selected. Optional graphical output includes a Scree Plot and Bivariate Scatterplots of the various pair-wise combinations of extracted Principal Components.
    • The help file of statistiXL provides an introduction to Principal Component Analysis, and gives an example of Principal Component Analysis.
11. t Tests
    • A t test is used either to compare the mean of a sample to the hypothesised mean for a population (e.g. comparing the body temperature of a group of people to an expected 37°C), or to compare the means from two samples (e.g. the weights of two populations of crab). t tests should not generally be used for multiple 2-way comparisons when there are more than two samples as this is the realm of ANOVA. A paired t test is the t  test of two samples where there is some relationship between the two samples such that the data occur in pairs (e.g. measuring the hindlimb and forelimb on the same animal) and is therefore unlike the standard t test where there is no association between the order of the data in each sample. A multivariate T² test compares two samples based on a number of multivariate measures for each sample, or, for a single sample, compares the mean for each measure to a hypothesised mean.
    • statistiXL’s t test module provides for analysis of both univariate and multivariate samples. Both single and two sample tests are provided with the option of analysing paired samples in the univariate two sample test. A test for the equality of variances between samples can be performed in the univariate two sample test with the resulting measure of t adjusted appropriately.
    • Results for a Two Sample t TestResults are presented in tabular form. Descriptive statistics are provided, if this option is selected. The general t statistics that are provided are; hypothesised mean, actual mean, standard error of the mean, t value, degrees of freedom, and P value. If the variance test option is selected, then the variance is listed for each variable, the F value is given with degrees of freedom, and the P value is given. For multivariate t tests, the T² statistic and its degrees of freedom, the F statistic and its degrees of freedom, and the P value are given.
    • The help file included with statistiXL provides an overview of univariate and multivariate t tests, and gives examples including single sample t test, variance test, two-sample t test, paired t test, single sample multivariate t test, and two sample multivariate t test.

Enhancements:

• Mainly intended to correct some charting layout issues experienced with Excel 2007.
  

Requirements:

• Microsoft Excel™ (Excel 97, 2000 and 2003). 
• Not supported under Excel™ on the Apple Macintosh™.
• 13MB of free disk space.