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.
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.
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.
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.
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.
Used correctly, the generate menu offers analysts some substantial time saving benefits. Watch this video to learn more about how you can use the generate menu effectively.
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.
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.
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.
In part two of our predictive operational analytics video series, Jarlath Quinn introduces the IBM SPSS Modeler Software before showing how to explore the example data from the case study and carry out text analytics of engineer logs to categorise the information and create key fields for further analysis.
In part 3 of this predictive operational analytics guide Jarlath uses the results from the previous analysis stage to show how to build and assess a predictive model that identifies whether or not a part replacement will be required during a maintenance visit.
In this final part of our predictive operational analytics video series Jarlath shows how we can use the predictive technology to go further by predicting the actual outcome of the visit including which part is likely to require replacement.
Jarlath provides a brief history of the concept of net promoter score before introducing the case study whereby Predictive Promoter was applied to data from the guests of a major hotel chain.
In this video Jarlath shows how to automatically produce a model that predicts customer’s recommendation scores based upon transactional and operational data.
In this video Jarlath shows how easy it is to take a previously built model that has been generated based on known outcomes and apply it to customer data where the outcome is not known.
In this final part of our predictive promoter video series, Jarlath Quinn tackles the project’s most ambitious task: mining the open-ended guest comments to uncover important insights. Here you can discover how we can use text analytics to extract a series of concepts and sentiments from customer comments in order to categorise the guests’ responses.
In this short video Jarlath Quinn demonstrates how to use the powerful simulation tools within IBM SPSS Modeler to perform What If analysis (also known as ‘Scenario Planning’). What if analysis allows business-focused analysts to go beyond simple predictive modelling to evaluate the impact of different choices and scenarios on predicted outcomes.
This video shows you how organisations with substantial capital assets can use IBM SPSS Modeler to predict when asset failure is most likely. Predicting asset failure can prevent problems before they happen and enables organisations to save money, reduce asset downtime and increase efficiency.
This video shows you how you can analyse customer comments or indeed any free-text data, using the power of the text analytics engine contained in SPSS Modeler Premium
Learn how to exploit the power of the text analytics engine contained in SPSS Modeler Premium (video 2 of 3)
Learn how to exploit the power of the text mining engine contained in SPSS Modeler Premium (video 3 of 3).
This short video shows how you can perform a simple affinity analysis using IBM SPSS Modeler. Affinity analysis can be used to understand interconnected relationships between key factors. For example, in retail it can be used to perform basket analysis, whereby retailers can identify which products are most commonly purchased together by customers in a single transaction or over a given period time.
If you are considering making your first foray into predictive analytics or are interested in seeing the automated capabilities of IBM’s flagship analytical platform, this video will demonstrate the power and ease of building a predictive model in SPSS Modeler.
You will learn how to: Summarise your data fields using simple descriptive statistics such as frequency counts, percentages, means, standard deviations and so on Run different kinds of descriptive statistics for different kinds of data Display your data visually via customisable tables Examine the relationships between variables using crosstabs Select different kinds of charts and …
Part two You will learn how to: Get your data ready for analysis in IBM SPSS Statistics Transform your data and create new fields e.g. creating an ‘age range’ field from date of birth information Ways in which you can transform different types of data
You will learn how to: Read data into IBM SPSS Statistics from other sources such as Excel files Format your data and tidying it up for effective analysis Save time by using previously formatted files as templates for your new IBM SPSS Statistics data file
