Regression online software

R package version 0. R package version Send output to:. Data X click to load default data. Top Output Charts References.

Cite this software as:. Wessa P. Net 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 may be 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.

The Harmonic Regression procedure calculates the harmonic regression for time series data. To accomplish this, it fits designated harmonics i. 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. The Exogenous Variables are independent variables that are included in both the first and second stage regression models.

They are not correlated with the random error values in the second stage regression. The Endogenous Variables become the dependent variable in the first stage regression equation. Each is regressed on all exogenous and instrument variables. The predicted values from these regressions replace the original values of the endogenous variables in the second stage regression model.

Two-Stage Least Squares is used in econometrics, statistics, and epidemiology to provide consistent estimates of a regression equation when controlled experiments are not possible. Often theory and experience give only general direction as to which of a pool of candidate variables should be included in the regression model. The actual set of predictor variables used in the final regression model must be determined by analysis of the data. Determining this subset is called the variable selection problem.

Finding this subset of regressor independent variables involves two opposing objectives. First, we want the regression model to be as complete and realistic as possible. We likely want every regressor that is even remotely related to the dependent variable to be included.

Second, we want to include as few variables as possible because each irrelevant regressor decreases the precision of the estimated coefficients and predicted values. Also, the presence of extra variables increases the complexity of data collection and model maintenance.

The goal of variable selection becomes one of parsimony: achieve a balance between simplicity as few regressors as possible and fit as many regressors as needed. A number of procedures are available in NCSS for determining the appropriate set of terms that should be included in your regression model. The Subset Selection in Multiple Regression procedure has various forward selection methods including hierarchical forward selection, where interaction terms are included only if all terms of a lesser degree are included.

This procedure is also especially useful when categorical variables are in the model, as it keeps the dummy variables associated with the categorical variables together. This procedure also provides a regression analysis for the final model of the search.

The All Possible Regressions procedure provides an exhaustive search of all possible combinations of up to 15 independent variables. The top models for each number of independent variables are displayed in order according to the criterion of interest R-Squared or Root MSE. When selecting among a large number of candidate independent variables, the Stepwise Regression procedure may be used to determine a reasonable subset. Three selection methods are available in this procedure.

The forward selection method begins with no candidate variables in the model. The variable that has the highest R-Squared is chosen first.

At each step, the candidate variable that increases R-Squared the most is selected. Variable addition stops when none of the remaining variables meet the specified significance criterion. In this method, once a variable enters the model, it cannot be deleted.

The backward selection model starts with all candidate variables in the model. At each step, the variable that is the least significant is removed. This process continues until only variables with the user-specified significance remain in the model.

Stepwise regression is a combination of the forward and backward selection techniques. In this method, after each step in which a variable was added, all candidate variables in the model are checked to determine if their significance has been reduced below the specified level.

If a nonsignificant variable is found, it is removed from the model. Stepwise regression requires two significance levels: one for adding variables and one for removing variables. Built into the Logistic, Conditional Logistic, Cox, Poisson, Negative Binomial, and Geometric Regression analysis procedures is the ability to also perform subset selection. In each of these procedures, subset selection can be performed with both numeric and categorical variables, where the dummy variables associated with each categorical variable are maintained as a group.

This procedure is also useful in both multiple and multivariate regression when you wish to force some of the X variables to be in the model. The algorithm first finds the best single variable. To find the best pair of variables, it tries each of the remaining variables and selects the one that improves the model the most.

It then omits the first variable and determines if any other variable would improve the model more. If a better variable is found, it is kept and the worst variable is removed. Another search is now made through the remaining variables. This switching process continues until no switching will result in a better subset. Once the optimal pair of variables is found, the best three variables is searched for in much the same manner. First, the best third variable is found to add to the optimal pair of variables from the last step.

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.

I am a very satisfied user of NCSS. It is not only my statistical analysis program of choice but I have recommended it to many of my clients as well. When I've had questions and called NCSS, I have always gotten expert help and advice and never had a problem go unsolved. Keep up the good work. I have been exposed to many other analytical and statistical software applications and have found there is no other product on the market that can match the ease of learning, comprehension of function, and the frugality of price the NCSS product offers All trademarks are the properties of their respective owners.

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. It is a statistical analysis software that provides regression techniques to evaluate a set of data.

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. You can also improve its functionality by manually adding some more statistical models to it. It provides a variety of techniques and models which help you in data estimation, evaluation, and analysis. You can use regression analysis models and other statistical techniques for statistical data analysis.

Also, various types of plots are provided in it for data visualization, such as histogram, pie chart, bar chart, mosaic chart, radar chart, network chart, 3D plot, etc.