This free educational webinar focuses on the application and value of regression techniques in the IBM SPSS Statistics products. The session will give you a straightforward overview of how simple regression techniques can help your business and how to get started with regression analysis in SPSS. From this recording you will learn how easy it is to perform simple but powerful regression analysis using IBM SPSS software.
Using regression you can:
- Accurately predict the response to your marketing campaigns, both what the overall response volume is likely to be and how likely individual recipients are to respond
- Predict what effect a change in advertising spend or price will have on your sales
- Make your marketing resources go further and be more effective – by enabling you to rank recipients of marketing campaigns by their likelihood to respond and therefore select the list to optimise response or conversion
- Understand how changes you make to one aspect of your marketing might affect others – for example, how the number of tweets you send influences the number of visits to your website, or how the availability of parking influences footfall in your store.
- Understand the relative strength of different factors that influence your sales – do your sales depend more strongly on price or on advertising volume?
During the webinar we cover:
- The principles of prediction
- What linear regression is and how it can help you
- How to build a simple linear regression model in SPSS
- How you can predict response likelihood with logistic regression
- How to apply predictive models to your data
Who will benefit?
- Anyone who has used SPSS in the past and needs a refresher, or is currently using it and would like more confidence in some of the processes.
- Anyone who is considering making an investment in statistical software and wants to learn more about what it can do for them.
- Anyone who has heard of regression but isn’t sure why they should be using it.
- Anyone who’d like to know the difference between the different regression techniques and when to use them.