Statistix is a mathematical software used for data analysis. It is a great tool for researchers and data analysts as it can perform intricate data manipulation techniques such as power analysis, survival analysis, linear modeling and so much more quickly and with reliable accuracy. The software was developed in 1985, and since then, it has enabled researchers and statisticians to perform both basic and advanced data analyses with easy to use menu driven procedures. Statistix offers the users a wide range of data analysis tools including:
Export/import support for files
Linear regression models
Logistic regression models
Poisson regression models
Analysis of variance models
Nonlinear regression models
Quality control tools
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Features of Statistix
Statistix has a number of exciting features that make it a favorite of many when it comes to data analysis. These include:
Data and files: Some of the features provided by Statistix for files and data manipulation include:
Spreadsheet data entry
Up to 1, 000, 000 cases and 1, 000 variables
If…Then…Else and other powerful data transformation procedures
Missing values handling
Date, integer, real, and string data types
Value and variable labels
Export/import support for both XLSX and XLS Excel formats
Column control formats
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Linear models: Linear models are one of the most powerful and commonly used tools in data analysis. Statistix offers you a wide range of these tools including:
Linear regression: Statistix can fit models with multiple independent variables, sometimes up to one hundred independent variables. Some of the tools provided for linear regression include ANOVA table, coefficient table, Durbin-Watson test, stepwise ANOVA table, variance-covariance matrix, prediction, residual plots, fitted line plot, and normal probability plot. Statistix also allows you to perform weighted regression, best subsets regression, as well as forward and backward regression.
Logistic regression: Tools provided for logistic regression include optional stepwise procedure, odd ratios, Hosmer-Lemeshow Statistic, save residues, and variance-covariance matrix of betas.
Poisson regression: Statistix provides tools for count data analysis such as deviance test, coefficient table, save residues, and variance-covariance matrix.
Two-stage least squares regression: This feature is provided to help researchers estimate a linear equation when the right-hand side variables or one or multiple predictor variables is an endogenous variable (variable determined by the equation being solved). To find out more about endogenous variables, connect with our Statistix project help experts.
Analysis of variance: Tools provided for analysis of variance include Latin Square, complete block, dialog boxes for one way, balanced lattice, split-plot, analysis of covariance, unbalanced designs, least-square means, polynomial contrasts, multiple comparisons of means, normal probability plot, and residual plots.
Correlations: Statistix allows researchers to compute Pearson’s correlations for up to one hundred variables.
Variance-covariance: This tool computes covariance and variances for data with up to one hundred variables.
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Nonlinear models: These models are used to fit smoothed surfaces and curves to multidimensional or two-dimensional data. Some of the tools and procedures provided by Statistix for nonlinear modeling include:
Spline interpolation: This procedure uses cubic, tension, or linear spline interpolation to fit smooth curves to X-Y data.
Polynomial regression: The technique uses polynomials to fit polynomial regression models of independent variables on one or multiple independent variables. Tools provided to achieve this include stepwise AOV table, coefficient table, residuals, prediction, variance-covariance of regression, Durbin-Watson statistic, contour plot, fitted curve, normality probability plot, residual plots, and surface coplot.
Nonlinear regression: This method fits a nonlinear regression model to data with one or multiple independent variables using the Levenberg-Marquardt-Nash algorithm. Models may consist of up to 20 parameters and 20 independent variables. The tools provided to create nonlinear regression models include coefficient estimates, prediction, confidence intervals, correlations, variance-covariance or regression, residuals, normal probability plots, surface coplot, residual plots, contour plots, and fitted curves.
Loess: This technique uses local linear regression to fit smoothed surfaces and curves to multivariate scattered data.
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Association tests: Statistix provides a host of association tests for studying the relationship or similarity among two or multiple variables. Some of these include:
Multinomial test: This is usually goodness of fit test used to determine how well frequencies of mutually related data fit a normal distribution.
Chi-square tests: Calculates the chi square’s goodness of fit for two way tables. The test can also be used to compute the hypothesis of homogeneity and the hypothesis of independence.
Kolmogorov-Smirnov test: Compares the similarities between distributions obtained from two different samples. Compared to the Chi-square test, the Kolmogorov-Smirnov test is more powerful because it can analyze information in ordered data while the chi-square test cannot.
McNemar’s symmetry test: A goodness of fit test usually used to measure the change in square contingency tables.
Other association test features provided by Statistix include:
Two by two tables
Spearman rank correlations
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Time-series: A time series is a set of data collected sequentially over time. This data can be anything from population levels, stock prices, product sales, to temperature and rainfall. In time series analysis, it is assumed that this data is collected at uniform time intervals like daily, weekly, monthly, or yearly. Statistix provides several tools to perform time series analysis including:
Time series plot
Partial autocorrelation plot
Seasonal AutoRegressive Integrated Moving Average (SARIMA)
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Survival analysis: Survival time is the period a specific event occurs such as an illness, response to treatment, or death. The technique of survival analysis is applied in biomedical studies, marketing, sociology, and electrical engineering. Some of the tools that Statistix provides to perform survival analysis include:
The Kaplan-Meier procedure
Two-sample survival test
Multi-sample survival test
Proportional hazard regression
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Power analysis: The power analysis is performed to determine whether one is going to accept or reject a null hypothesis while carrying out a hypothesis test. Statistix offers a variety of tools for power analysis including:
One sample T-test
One way analysis of variance
Two sample T-test
Randomized complete block design
Latin square design
Split plot design
Factorial in blocks
Strip plot design
Repeated measures design
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