Basic Statistical Methods

Statpoint Technologies products provide a wide range of procedures for accomplishing basic statistical tasks. This section describes some procedures in basic statistics for handling data sampled from one or more populations:

Start Using Statgraphics Today!

Procedure Statgraphics Centurion Statgraphics
Sigma express
Statgraphics
stratus
Statgraphics
Web Services
StatBeans
One Sample Analysis
Outlier Identification
Comparing Two Independent Samples
Comparing Two Paired Samples
Comparing Multiple Samples
Comparing Rates and Proportions    

One Sample Analysis

stats

The One Variable Analysis procedure is one of the primary procedures for analyzing a single column of numeric data. It calculates summary statistics and confidence intervals, performs hypothesis tests, and creates a variety of graphical displays. The graphs include a scatterplot, histogram, box-and-whisker plot, quantile plot, normal probability plot, density trace, and symmetry plot. The tables include percentiles and a stem-and-leaf display. 

More: One Variable Analysis.pdfonebox

Outlier Identification

outlier

The Outlier Identification procedure is designed to help determine whether or not a sample of n numeric observations contains outliers. Outliers are observations that do not come from the same distribution as the rest of the sample. Both graphical methods and formal statistical tests due to Grubbs and Dixon are included. The procedure will also save a column of flags identifying the outliers in a form that can be used to exclude those observations when running other procedures.

More: Outlier Identification.pdf

twosam

Two Sample Comparison

The Two Sample Comparison procedure is designed to compare two independent samples of variable data. Tests are run to determine whether or not there are significant differences between the means, variances, and/or medians of the populations from which the samples were taken. In addition, the data may be displayed graphically in various ways, including a dual histogram, a multiple box-and-whisker plot, and a quantile-quantile plot.

More: Two Sample Comparison.pdf

Paired Sample Comparison

paired

The Paired Sample Comparison procedure is designed to compare data in 2 numeric columns where the values in each row are paired, i.e., correspond to the same subject or experimental unit. The primary reason for such a comparison is typically to determine whether or not the factor that differentiates the columns has a significant effect on the data.

More: Paired Sample Comparison.pdf

multiple sample comparison

outlier

The Multiple Sample Comparison procedure is designed to compare two or more independent samples of variable data. Tests are run to determine whether or not there are significant differences between the means, variances, and/or medians of the populations from which the samples were taken. In addition, the data may be displayed graphically in various ways, including a multiple scatterplot, a means plot, an ANOM plot, and a medians plot.

More: Multiple Sample Comparison.pdf

Comparison of Rates and Proportions

outlier

Procedures are also available for comparing the observed rates of an event amongst k samples (based on a Poisson distribution), or comparing the observed proportions (based on a binomial distribution). Tests provided include a dispersion test, a chi-square test, and a likelihood ratio test. The procedures also perform an analysis of means (ANOM) to determine which samples differ significantly from the overall average.

More: Comparison of Rates.pdf, Comparison of Proportions.pdf

Start Using Statgraphics Today!