NCSS PASS 2008 8.0.15
PASS performs power analysis and calculates sample sizes. Use it before you begin a study to calculate an appropriate sample size (it meets the requirements of government agencies that demand technical justification of the sample size you have used). Use it after a study to determine if your sample size was large enough. PASS lets you solve for power, sample size, effect size, and alpha level. It automatically displays charts and graphs along with numeric tables and text summaries in a portable format that is cut and paste compatible with all word processors so you can easily include the results in your proposal. PASS is a standalone system. Although it is integrated with NCSS, you do not have to own NCSS to run it. You can use it with any statistical software you want. PASS 2008 runs under Windows Vista, XP, 2000, NT, ME, 98, and 95.

New Procedures:
Mixed Models
The Mixed Models procedure analyzes results from a wide variety of experimental designs in which the outcome (response) is continuous, including

· Two sample designs (replacing the t test)
· One way layout designs (replacing one way ANOVA)
· Factorial designs (replacing factorial GLM)
· Split plot designs (replacing split plot GLM)
· Repeated measures designs (replacing repeated measures GLM)
· Cross over designs (replacing GLM)
· Designs with covariates (replacing GLM)

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.

Data Simulator
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.

Data Stratification
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
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.

Nondetects Analysis
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.

Nondetects Regression
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.




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