![]() ![]() The new ROC Analysis procedure also includes precision-recall (PR) curves and provides options for comparing two ROC curves that are generated from either independent groups or paired subjects. These are often represented by a ROC curve that plots the true positive rate (TPR) against the false positive rate (FPR) at various threshold settings. ROC (Receiver Operator Characteristic) analysis is specifically concerned with the classification accuracy of models, especially as regards the relationships between the accurate classifications (known as the True Positives and True Negatives) and the inaccurate predictions (the False Positives and False Negatives). The new ROC procedure makes it easier to assess the accuracy and performance of predictive classification models. Figure 3 illustrates that not only do we get different intercept values for data in the 20 th percentile (quantile 0.2) vs the 80 th percentile (quantile 0.8), but we also get different parameter estimates for the coefficient values. The charts even show the parameter values for a standard (OLS) linear regression model for comparison (as indicated by the red line). Figure 3 – Separate regression parameter estimates for different quantiles The new Quantile regression procedure even plots these values as shown in figure 3. ![]() As such, Quantile regression produces separate coefficients and intercept values for each requested quantile. However, there’s no reason to assume that the same formula applies to the data in the top 10% of the current salary distribution, or the say, the bottom 25%. The formula also contains a constant value (or intercept) of $1,928. ![]() This formula consists of a single coefficient of 1.9, meaning that for every extra dollar of beginning salary, the respondent earns $1.9 dollars in their current salary. Using standard Linear Regression on the same dataset we get a single formula for estimating a respondent’s current salary. The effect of this is that we can produce separate predictions for the different parts of the dependent variable’s distribution. Figure 2 – Quantile regression showing different lines of fit for separate percentiles For example, we can request estimates for the lowest 10 percent (quantile = 0.1) or the top 90 percent (quantile = 0.9) of the dependent variable. In other words, we can fit separate regression lines for different percentiles. Although there’s no reason to believe that a regression based on line fitted about the median would be more accurate than one based on a mean, quantile regression is flexible enough to allow us to fit a model based on other percentile values. We should bear in mind that a median is also called the 50 th percentile and in this context percentile and quantile refer to the same thing. Quantile regression offers us the opportunity to fit the model using a median value rather than a mean. Technically, this is referred to as ‘heteroscedasticity’, but more prosaically, it just indicates that the model is likely to be worse at estimating higher values than lower ones since the points vary more about the line. You may note from the chart that there seems to be a slight ‘funnelling’ of the points near the higher values in the scatterplot. This line can be used to estimate the mean value of the dependent variable as represented by the points clustering about line at a given value of the independent (predictor) variable (see figure 1) Figure one: regression using mean least squares function In standard ‘least squares’ regression the model predictions are based on a single regression line. New analytical procedures Quantile Regression If you’re an existing SPSS user and you’d like to upgrade to v26 there’s more information about how to do that here. If you’re interested in trying SPSS Statistics for the first time then do please get in touch – we’ll be happy to help. ![]() Version 26 introduces a number of additional analysis procedures as well as new command enhancements. Special software packages are also available for students, professors and others in higher education.In April of this year, IBM released the latest version of SPSS Statistics. Or choose from more than a dozen specialty modules to meet your unique analytical requirements. You can choose pre-configured editions to ensure that everyone performing analytics has all the functionality they need. Support for Python as a “front-end” cross-platform scripting language and support for R algorithmsĬollaboration capabilities boost the productivity of analysts using IBM® SPSS® Statistics, and server-based options increase scalability and performance.Mac and Linux users can connect clients to IBM SPSS Statistics Server.Support for IBM System z servers running Linux®.Support for Snow Leopard™ on Mac OS® X 10.6.Support for 64-bit hardware on desktop for Windows and Mac. ![]()
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