Software regression

The developer may be trying to fix a bug or update the version but incorrectly introduces some code that regresses the program. Other events include version or configuration changes. To alleviate the chances of software regression, regression testing is usually employed. This type of testing looks at the new program and compares it to all old versions of the program.

It then runs the programming through a test to see if there is any sign of regression and to ensure that all coding is functional. Any signs of regressed or non-functioning code will be brought back to the developer or user and will report what has regressed or where the regression is found. Next, each of the first two variables is omitted and another, even better, variable is searched for. The algorithm continues until no switching improves R-Squared.

This algorithm is extremely fast. It quickly finds the best or very near best subset in most situations. It is particularly useful for the case where you are specifying more than one dependent variable in joint relation to a number of independent variables. This procedure can also be valuable in discriminant analysis where each group may be considered as a binary 0, 1 variable.

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Privacy Policy Terms of Use Sitemap. Technical Details This page is designed to give a general overview of the capabilities of the NCSS software for regression analysis. Simple Linear Regression [Documentation PDF] Simple Linear Regression refers to the case of linear regression where there is only one X explanatory variable and one continuous Y dependent variable in the model.

NCSS includes three procedures related to simple linear regression: 1. Linear Regression and Correlation 2. Example Linear Regression Plot A large number of specialized plots can also be produced in this procedure, such as Y vs.

Sample Output Multiple Regression Multiple Linear Regression refers to the case where there are multiple explanatory X variables and one continuous dependent Y variable in the regression model. NCSS includes several procedures involving various multiple linear regression methods: 1. Multiple Regression 2. Multiple Regression — Basic 3. Multiple Regression for Appraisal 4. Multiple Regression with Serial Correlation 5. Principal Components Regression 6. Response Surface Regression 7.

Ridge Regression 8. Robust Regression Multiple Regression [Documentation PDF] Multiple Regression refers to a set of techniques for studying the relationship between a numeric dependent variable and one or more independent variables based on a sample. Data NCSS is designed to work with both numeric and categorical independent variables. The Regression Model Regression models up to a certain order can be defined using a simple drop-down, or a flexible custom model may be entered.

Sample Data Procedure Input Sample Output The output includes summary statistics, hypothesis tests and probability levels, confidence and prediction intervals, and goodness-of-fit information.

Logistic Regression Logistic Regression is used to study the association between multiple explanatory X variables and one categorical dependent Y variable.

NCSS includes two logistic regression procedures: 1. Logistic Regression 2. Nonlinear Regression 2. Curve Fitting — General 3. Michaelis-Menten Equation 4. Sum of Functions Models 5. Fractional Polynomial Regression 6. Ratio of Polynomials Fit — One Variable 7. Ratio of Polynomials Search — One Variable 8. Harmonic Regression 9. Ratio of Polynomials Fit — Many Variables Starting Values Many people become frustrated with the complexity of nonlinear regression after dealing with the simplicity of multiple linear regression analysis.

Assumptions and Limitations Usually, nonlinear regression is used to estimate the parameters in a nonlinear model without performing hypothesis tests. Regression for Method Comparison Method comparison is used to determine if a new method of measurement is equivalent to a standard method currently in use.

NCSS includes two regression procedures with application to method comparison: 1. Deming Regression 2. Passing-Bablok Regression Deming Regression [Documentation PDF] Deming regression is a technique for fitting a straight line to two-dimensional data where both variables, X and Y, are measured with error.

Deming Regression Plot Passing-Bablok Regression [Documentation PDF] Passing-Bablok Regression for method comparison is a robust, nonparametric method for fitting a straight line to two-dimensional data where both variables, X and Y, are measured with error. Regression with Survival or Reliability Data Survival and reliability data present a particular challenge for regression because it involves often-censored lifetime or survival data which is not normally distributed.

NCSS includes two procedures that perform regression with survival data: 1. Cox Proportional Hazards Regression 2.

NCSS includes five procedures that can be used to model count data: 1. Poisson Regression 2. Zero-Inflated Poisson Regression 3. Negative Binomial Regression 4. Zero-Inflated Negative Binomial Regression 5. Geometric Regression Poisson Regression [Documentation PDF] Poisson regression is similar to regular multiple regression analysis except that the dependent Y variable is a count that is assumed to follow the Poisson distribution. Negative Binomial Regression [Documentation PDF] Negative Binomial Regression is similar to regular multiple regression except that the dependent variable Y is an observed count that follows the negative binomial distribution.

Geometric Regression [Documentation PDF] Geometric Regression is a special case of negative binomial regression in which the dispersion parameter is set to one. Regression with Time Series Data One of the basic requirements of regular multiple regression is that the observations are independent of one another. NCSS includes two procedures for regression with serially correlated time series data: 1. Multiple Regression with Serial Correlation 2.

Plot from Harmonic Regression Analysis in NCSS Regression with Nondetects Data The Nondetects-Data Regression procedure fits the regression relationship between a positive-valued dependent variable with, possibly, some nondetected responses and one or more independent variables. These variables are defined and used as follows: A Dependent Variable is the response variable Y that is to be regressed on the exogenous and endogenous but not the instrument variables.

Sample Output Subset Selection Often theory and experience give only general direction as to which of a pool of candidate variables should be included in the regression model. You can easily enter a dataset in it and then perform regression analysis. The results of the regression analysis are shown in a separate Output Viewer window with all steps. Besides regression analysis algorithms, it has several other statistical methods which help you perform data analysis and examination.

Plus, scatterplot, bar chart, and histogram charts can be plotted for selected variables or dataset. It is a nice and simple regression analysis software using which you can perform data analysis with different kinds of statistical methods. Statcato is a free, portable, Java-based regression analysis software for Windows, Linux, and Mac.

To run this software, you need to have Java installed on your system. You can download Jave from here. Like many other listed software, it is also a statistical analysis software that contains a lot of data analytic methods for data estimation and evaluation.

Plus, you can also compute probability distributions , p-Value , and frequency table using it. Furthermore, it offers several data visualization graphs to analyze data using charts which include bar chart, box plot, dot plot, histogram, normal quantile graph, pie chart, scatterplot, stem and leaf plot, and residual plot.

Statcato is a free open source regression analysis software that lets you perform statistical analysis on a numerical dataset and you can also visualize data on various graphs.

It is a nice, clean, and user friendly statistical analysis software that is dedicated to performing data analysis tasks. On its main interface, you can find a Regression module with related techniques. Some additional modules can be installed and added to this software from Jamovi Library. It is a nicely designed regression analysis software with comprehensive results. Regression Testing. ISTQB Definition regression testing: A type of change-related testing to detect whether defects have been introduced or uncovered in unchanged areas of the software.

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