Jarlath Quinn

A veteran of the predictive analytics industry working under the auspices of SAS, IBM and SPSS, Jarlath is pre-sales director at Smart Vision Europe. He is one of the most experienced SPSS trainers in the industry.

Understanding correlation

This is the latest in our ‘eat your greens’ series – a back to basics look at core statistical concepts that are often misunderstood or misapplied. In everyday speech the term ‘correlation’ refers to a mutual connection or relationship between two things. In statistics correlations are specific measures or values that attempt to quantify the …

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Finding normality – why is the normal distribution so important when we so rarely encounter it in real life?

This is the fourth post in our ‘eat your greens’ series – a back to basics look at some of the core concepts of statistics and analytics that, in our experience, are frequently misunderstood or misapplied. In this post we’ll look in more depth at the concept of the normal distribution.  One of the first …

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Are algorithms evil?

None of us can have failed to notice the recent debacle over Ofqual’s (the Office of Qualifications and Examinations Regulation) use of an algorithm to predict pupil grades. Once again, ‘algorithm fever’ has generated a flurry of news articles questioning whether we are sleepwalking into a dystopian future where human expert decision-making is replaced with …

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Testing versus inferring

This is the second post in our ‘eat your greens’ series – a back to basics look at some of the core concepts of statistics and analytics that, in our experience, are frequently misunderstood or misapplied. In this post we’ll look in more depth at the concept of testing versus inferring. One of most daunting …

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PS Imago Pro – an overview of additional charting capabilities not available in IBM SPSS Statistics

PS Imago Pro is a statistical analysis and reporting solution based on IBM SPSS Statistics. Indeed, apart from the inclusion of an additional menu, users of SPSS Statistics may find that the data analysis module of PS Imago Pro looks almost identical to its SPSS counterpart. However, as Figure 1 shows, this additional menu contains …

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An overview of the four main approaches to predictive analytics

This infographic provides an overview of the four main families of approaches to predictive analytics. Prediction encompasses applications that aim to estimate or predict the values of a key target field. Segmentation refers to techniques such as cluster analysis which attempt to find the most ‘naturally occurring” groups within a dataset. Association modelling discovers groups …

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6 secrets of building better models part three: feature engineering

Feature Engineering is really just a fancy term for creating new data. Very often we can help an algorithm build better models by preparing the input data in a way that allows it to detect a clearer signal in the often noisy data. In machine learning variables are often referred to as ‘features’, so feature engineering refers to the transformation of variables or the creation of new ones.

6 secrets of building better models part five: meta models

The idea of meta modelling is to build a predictive model using the predictions or scores generated by another model. By adding the predictive scores generated by an initial modelling algorithm to an existing pool of predictor fields, a second algorithm can then exploit these scores in to build a final more accurate model.

6 secrets of building better models part six: split models

Split models or split population modelling is another technique that allows the user to build multiple models which can then be combined to create a single prediction. The idea with split modelling is that if the data represent different populations or contain separate groups that behave in very different ways, assuming that a single model can explain all the inherent variability across these distinct populations might be unreasonable.

Regular Expressions for IBM SPSS Modeler: performance comparison

The Regular Expressions for IBM SPSS Modeler node pack provides 4 nodes that integrate the power and flexibility of regular expression pattern matching into SPSS Modeler. However, some of these capabilities can be supported using the extension nodes built into SPSS Modeler and that begs the question – why buy the Regular Expression nodes? One …

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How to merge files in SPSS Statistics

In this video Jarlath Quinn demonstrates how to merge data files within SPSS Statistics using each of the two main methods, either adding cases (combining files with the same fields but additional rows) or adding variables (combining files by joining variables to a target file using something like an ID field as a ‘keyed variable’).

How to select cases in SPSS Statistics

In this video Jarlath Quinn demonstrates how to use SPSS Statistics to define data filters in order to select particular cases for analysis. This can be done either to create a temporary selection or to create a permanent new file with only a subsection of cases included within it.

How to reverse a scale in SPSS Statistics

In this video Jarlath Quinn demonstrates how to reverse the values of a rating scale (such as an agreement scale or a satisfaction scale) in SPSS Statistics, so that the highest value becomes the lowest value and vice versa.

How to combine variables in SPSS Statistics

SPSS users often want to know how they can combine variables together. In this video Jarlath Quinn demonstrates how to use the compute procedure to calculate the mean of a number of variables to create one combined variable, and also how to use the count values procedure to count how many times a particular value occurs across a series of variables in order to create an overall count.

TURF analysis with SPSS Statistics

In this video Jarlath Quinn introduces the popular TURF analysis technique and demonstrates how to apply it in IBM SPSS Statistics. TURF analysis is used in many industries to find the optimal sub-group of options from a wider portfolio in order to maximise their appeal to an audience or market.

Introduction to the filter node in SPSS Modeler

Sometimes you may have problems with your data issues not related so much to the values of the data but to the fields themselves, such as awkward field names. The filter node is a really useful tool that offers a bunch of tricks for dealing with awkward fields.

How to change the defaults in SPSS Statistics

SPSS enables quite a high level of customisation so you can set up the software in a way that enables you to be a lot more productive, however many people are unaware of just how powerful these customisation options are. In this video we explore the options edit menu.

Introduction to the data audit node in SPSS Modeler

The data audit node is a powerful tool you can use to help understand the shape and structure of your data before your analysis begins. You can also make some decisions here regarding how you might want to clean up your data, for example by dealing with missing values or extremes and outliers.

How alternative interfaces can help you get more out of R

Contemporary analytical platforms like SPSS and SAS represent the some of the earliest and yet longest-lived examples of proprietary software in the industry. When we think of the tectonic shifts the technology landscape has witnessed in last four decades, through the mainframe era, the rise of the PC, browser wars, the dotcom bubble, the smartphone …

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Predictive operational analytics part 1

In part one of this video series Jarlath Quinn explains how operational analytics works, what data sources may be utilised and introduces the example case study that forms the basis of the subsequent videos in the series.

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