Smart Vision Europe recently announced an exciting new partnership with DataRobot, a rapidly rising player in the data science market […]
Cloud AI at the cutting edge: taking DataRobot for a spin Read More »
IBM has just announced the general availability of IBM SPSS Statistics 28.0.1. This modification release introduces new statistical tests, enhancements
What’s new in SPSS Statistics 28.0.1? Read More »
SPSS Modeler is a robust data science software for professional analysts and data scientists. The software scales from supporting line-of-business predictive analysis to enterprise-scale
What’s new in IBM SPSS Modeler 18.3? Read More »
IBM is about to release the latest version of SPSS Statistics. Here’s a summary of some of the new enhancements
What’s new in SPSS v28? Read More »
A fascinating recent report by MIT Sloan in collaboration with BCG has confirmed what many industry insiders have known for
Failure to launch: the hard realities of AI Read More »
In this series of ‘eat your greens‘ posts looking at core statistical concepts in more detail, we have looked closely
Introduction to Power Analysis Read More »
IBM have released a new update to SPSS v27. Interestingly, this is a ‘dot’ release that contains a number of
What’s new in SPSS v27.0.1.0? Read More »
In these videos Jarlath Quinn introduces the concept of Structural Equation Modelling with SPSS AMOS. In the first video Jarlath uses the example of creating a simple linear model to illustrate the functionality in the AMOS interface.
Introduction to structural equation modelling with SPSS Amos – part one Read More »
In these videos Jarlath Quinn introduces the concept of Structural Equation Modelling with SPSS AMOS. In the second video Jarlath shows how to perform a basic Confirmatory Factor Analysis in AMOS.
Introduction to structural equation modelling with SPSS Amos – part two Read More »
A worked example of a classic inferential statistical procedure: the independent samples t-test.
Making sense of significance tests Read More »
This is the latest in our ‘eat your greens’ series – a back to basics look at core statistical concepts
Understanding correlation Read More »
This is the fifth post in our ‘eat your greens’ series – a back to basics look at some of
The gateway to inference – the standard error and confidence intervals Read More »
This is the fourth post in our ‘eat your greens’ series – a back to basics look at some of
Watch the other videos in this series
Multivariate analysis with Camo Analytics Unscrambler part one Read More »
Watch the rest of the videos in this series
Multivariate analytics with Camo Analytics Unscrambler part two Read More »
Watch the other videos in this series
Multivariate analysis with Camo Analytics Unscrambler part three Read More »
Watch the rest of the videos in this series
Multivariate analysis with Camo Analytics Unscrambler part four Read More »
None of us can have failed to notice the recent debacle over Ofqual’s (the Office of Qualifications and Examinations Regulation)
Are algorithms evil? Read More »
This is the third post in our ‘eat your greens’ series – a back to basics look at some of
What’s ‘standard’ about a standard deviation? Read More »
This is the second post in our ‘eat your greens’ series – a back to basics look at some of
Testing versus inferring Read More »
What do we mean when we say something is statistically significant? A guide to one of the most misused concepts in statistics.
In June of this year, IBM released the latest version of SPSS Statistics. Version 27 introduces several additional analysis procedures
What’s new in IBM SPSS Statistics v27? Read More »
PS Imago Pro is a statistical analysis and reporting solution based on IBM SPSS Statistics. Indeed, apart from the inclusion
You may have noticed that many media outlets are illustrating the tragic course of the coronavirus (COVID-19) pandemic by employing
Are Log scales appropriate for COVID-19 Charts? Read More »
This infographic provides an overview of the four main families of approaches to predictive analytics. Prediction encompasses applications that aim
An overview of the four main approaches to predictive analytics Read More »
At Smart Vision we’re in a pretty strong position to talk authoritatively about the reality of predictive analytics. That’s because
Choosing a predictive analytics project Read More »
Like a lot of consultants working in the analytics industry, I’ve built up an extensive portfolio of materials to illustrate
Statistics in court: the story of a dataset Read More »
Bootstrap aggregation, also called bagging, is a random ensemble method designed to increase the stability and accuracy of models. It involves creating a series of models from the same training data set by randomly sampling with replacement the data.
6 secrets of building better models part one: bootstrap aggregation Read More »
Boosting is another ensemble model-building method that was designed to help develop strong classification models from weak classifiers. Boosting methods focus on error (or misclassifications) that occur in prediction.
6 secrets of building better models part two: boosting Read More »
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 three: feature engineering Read More »
Ensemble modelling refers to the practice of combining the predictions of separate models on the old principle that “two heads are better than one”. Ensemble methods can be particularly effective when combining models that have been created using completely different algorithms.
6 secrets of building better models part four: ensemble modelling Read More »
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 five: meta models Read More »
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.
6 secrets of building better models part six: split models Read More »
In April of this year, IBM released the latest version of SPSS Statistics. Version 26 introduces a number of additional
What’s new in IBM SPSS Statistics v26? Read More »
The Regular Expressions for IBM SPSS Modeler node pack provides 4 nodes that integrate the power and flexibility of regular
Regular Expressions for IBM SPSS Modeler: performance comparison Read More »
In this video Jarlath Quinn takes a first look at SPSS Modeler v18.2 and demonstrates some of the new functionality that’s included within this release.
A first look at SPSS Modeler v18.2 Read More »
You may have noticed by now that there seem to be a couple of recurring themes in the plethora of
Three questions to ask when reading articles about artificial intelligence Read More »
We’re often asked how you can change the appearance of the tables that SPSS generates as output. In this video Jarlath Quinn demonstrates two different ways to do this, either by choosing a different table look in the edit / options function, or by editing the table properties directly yourself.
How to change the appearance of your output in SPSS Statistics Read More »
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 merge files in SPSS Statistics Read More »
SPSS users often want to be able to create grouped or banded data from continuous fields such as, for example, creating age groups or income bands from continuous fields. In this video Jarlath Quinn demonstrates how to use the visual binning procedure within SPSS Statistics to do this.
How to create grouped or banded variables in SPSS Statistics Read More »
Recoding your data means changing the values of a variable so that they represent something else. Within SPSS Statistics there is more than one type of recode that can be performed.
How to recode your data in SPSS Statistics Read More »
In this video Jarlath Quinn demonstrates how to use the functions within the explore command in SPSS Statistics to test for normality.
How to check your data for normality in SPSS Statistics Read More »
In this video Jarlath Quinn demonstrates how to work with date and time variables in SPSS using the SPSS date and time wizard.
How to calculate with dates in SPSS Statistics Read More »
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 select cases in SPSS Statistics Read More »
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 reverse a scale in SPSS Statistics Read More »
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.
How to combine variables in SPSS Statistics Read More »
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.
TURF analysis with SPSS Statistics Read More »
In this video Jarlath Quinn explains what cluster analysis is, how it is applied in the real world and how easy it is create your own cluster analysis models in SPSS Statistics.
Cluster Analysis with IBM SPSS Statistics Read More »
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.
Introduction to the filter node in SPSS Modeler Read More »
In this video we explore variable sets – a procedure in SPSS that allows you to generate subsets of variables or fields for display within dialogue boxes and in the data editor itself.
How to work with variable sets in SPSS Statistics Read More »
