## Feature spotlights

### Binary logistic regression

Predict the presence or absence of a characteristic or binary outcome based on values of a set of predictor variables. It is similar to a linear regression model, but is suited to models where the dependent variable is dichotomous and assumed to follow a binomial distribution. The estimated coefficients can be used to estimate odds ratios for each of the independent variables in the model.

### Logit response models

Use the logit link function to model the dependence of a polytomous ordinal response on a set of predictors. In the logit model, the log odds of the outcome is modeled as a linear combination of the predictor variables.

### Multinomial logistic regression

Classify subjects based on values of a set of predictor variables. This type of regression is similar to logistic regression, but it is more general because the dependent variable is not restricted to two categories.

### Nonlinear regression

Find a nonlinear model of the relationship between the dependent variable and a set of independent variables. Unlike traditional linear regression, which is restricted to estimating linear models, nonlinear regression can estimate models with arbitrary relationships between independent and dependent variables. This is accomplished using iterative estimation algorithms.

### Probit response analysis

Use probit and logit response modeling to analyze the potency of responses to stimuli, such as medicine doses, prices or incentives. This procedure measures the relationship between the strength of a stimulus and the proportion of cases exhibiting a certain response to the stimulus. It is useful for situations where you have a dichotomous output that is thought to be influenced or caused by levels of some independent variable(s) and is particularly well suited to experimental data.

### Two stage least squares

Uses instrumental variables that are uncorrelated with the error terms to compute estimated values of the problematic predictor(s) (the first stage), and then uses those computed values to estimate a linear regression model of the dependent variable (the second stage). Since the computed values are based on variables that are uncorrelated with the errors, the results of the two-stage model are optimal.

### Weighted least squares

Control the correlations between the predictor variables and error terms that can occur with time-based data. The Weight Estimation procedure tests a range of weight transformations and indicates which will give the best fit to the data.

### Quantile regression

Quantile regression models the relationship between a set of predictor (independent) variables and specific percentiles (or "quantiles") of a target (dependent) variable, most often the median. It has two main advantages over Ordinary Least Squares regression: Quantile regression makes no assumptions about the distribution of the target variable and tends to resist the influence of outlying observations.

## Technical details

### Software requirements

IBM SPSS Regression requires a valid IBM SPSS Statistics Base license.

• Prerequisite: IBM SPSS Statistics

### Hardware requirements

• Processor: 2 GHz or faster
• Display: 1024*768 or higher
• Memory: 4 GB of RAM required, 8 GB of RAM or more recommended
• Disk space: 2 GB or more