NCSS PASS 11 11.0.4 | 48 MB
The Mixed Models procedure can be used to test and estimate means (including pair-wise comparisons among levels), compare models, estimate variance-covariance matrix components, and produce graphs of means and repeated measurements of subjects. Restricted maximum likelihood and full maximum likelihood techniques are implemented in this procedure.
Circular Data Analysis
This procedure computes summary statistics, generates rose plots and circular histograms, computes hypothesis tests appropriate for one, two, and several groups, and computes the circular correlation coefficient for circular data. Angular data, recorded in degrees or radians, is generated in a wide variety of scientific research areas. Examples of angular (and cyclical) data include daily wind directions, ocean current directions, departure directions of animals, direction of bone-fracture plane, and orientation of bees in a beehive after stimuli.
Data Matching - Optimal and Greedy
This procedure is used to create treatment-control matches based on propensity scores and/or observed covariate variables. Both optimal and greedy matching algorithms are available (as two separate procedures), along with several options that allow the user to customize each algorithm for their specific needs. The user is able to choose the number of controls to match with each treatment (e.g., 1:1 matching, 1:k matching, and variable (full) matching), the distance calculation method (e.g., Mahalanobis distance, propensity score difference, sum of rank differences, etc.), and whether or not to use calipers for matching. The user is also able to specify variables whose values must match exactly for both treatment and controls in order to assign a match. NCSS outputs a list of matches by match number along with several informative reports and optionally saves the match numbers directly to the database for further analysis.
This procedure allows you to simulate, store, and visualize data from various discrete and continuous distributions, including Beta, Binomial, Cauchy, Constant, Exponential, F, Gamma, Multinomial, Normal, Poisson, T, Tukey's Lambda, Uniform, and Weibull. Mixture distributions may also be simulated. This module creates a histogram of a specified distribution as well as a numerical summary of simulated data. By storing the data, you can investigate the effects of varying data distributions on hypothesis tests and confidence intervals for your specific investigational situation.
This procedure is used to create stratum assignments based on quantiles from a numeric stratification variable (often a propensity score variable). The user is able to choose the number of strata to create and the amount of data used in the quantile calculations. Stratification is commonly used in the analysis of data from observational studies where covariates are not controlled.
Double dendrograms display clusters for individuals (rows) and variables (columns) in a single graph. A set of eight hierarchical clustering algorithms are available including single linkage, complete linkage, and group average. The procedure outputs lists of the items in each cluster, linkage reports, and a double-dendrogram.
Merging Two Databases
Occasionally, it is useful to merge two databases according to the value of one or more common (index) variables. This module allows you to merge two databases, or, alternatively, update one database with the contents of another.
Multiple Regression with Serial Correlation
This procedure uses the Cochrane-Orcutt method to adjust for serial correlation when performing multiple regression. The regular Multiple Regression routine assumes that the random-error components are independent from one observation to the next. However, this assumption is often not appropriate for business and economic data. Instead, it is more appropriate to assume that the error terms are positively correlated over time. Consequences of the error terms being serially correlated include inefficient estimation of the regression coefficients, under estimation of the error variance (MSE), under estimation of the variance of the regression coefficients, and inaccurate confidence intervals. The presence of serial correlation can be detected by the Durbin-Watson test and by plotting the residuals against their lags.
This procedure computes summary statistics, generates EDF plots, and computes hypothesis tests appropriate for two or more groups for data with nondetects (left-censored) values. Nondetects analysis is the analysis of data in which one or more of the values cannot be measured exactly because they fall below one or more detection limits. Detection limits often arise in environmental studies because of the inability of instruments to measure small concentrations. Some examples of sampling scenarios that lead to datasets with nondetects values are finding pesticide concentrations in water, determining chemical composition of soils, or establishing the number of particulates of a compound in the air.
The nondetects regression procedure fits the regression relationship between a positive-valued dependent variable (with, possibly, some nondetected responses) and one or more independent variables. The distribution of the residuals (errors) is assumed to follow the exponential, extreme value, logistic, log-logistic, lognormal, lognormal10, normal, or Weibull distribution. Nondetected responses occur when one or more of the values cannot be measured exactly because they fall below one or more detection limits.
NCSS has an interactive (point and click) user interface which makes in easy to learn and use. At times, however, it is necessary to repeat the same steps over and over. When this occurs, a batch system becomes more desirable. This procedure utilizes a batch language that lets you create a macro (script or program) and then run that macro. With the click of a single button, you can have the program run a series of procedures.
Color Selection Windows
The color selection windows let you choose appropriate colors from the 16 million colors that are available on today's monitors. Although choosing a color sounds like a trivial task, it can become time-consuming and frustrating. When you have invested a lot of time and money in a project and now have important results to communicate, you probably want to take the time to make outstanding graphics. A few, well-chosen charts can communicate results quickly and effectively. An important feature of a chart is the color scheme that you use. The goal of the color selection window is to provide a tool that will allow you to pick a set of colors that are pleasing to the eye when viewed together, and let the viewer interpret the results quickly and effectively.
Quick Launch Window
The Quick Launch window contains a button corresponding to each statistical and graphical procedure in the system. As you mouse over each button, a brief paragraph explaining the main purpose of the currently selected procedure will appear in the message box to the right. The Quick Launch makes it easy to find and launch any procedure from a single screen.
Enhanced User Interface
The procedure interfaces have been redesigned to make the user input easier to complete. Boxes containing descriptive titles have been added to group similar options together. This allows for easier navigation of the procedure windows and faster identification of input categories.
Improved Help System
The NCSS Help System has been improved in NCSS 2007 to make it easier to find the topic you are looking for. It is also now easier than ever to print help topics. The new help system consolidates the PDF documentation and the Help System. Adobe® Reader® Version 7 or later is required to use the new NCSS Help System. You can download the latest version of Adobe® Reader® by clicking on the link below. The new help system is fully compatible with Windows Vista.
PASS is easy to navigate with a simple procedure selection window. Simply scroll down the list and expand the tree to find the procedure you want. A message on the right gives an explanation of each procedure.