About this course

Course code TPDOEF92
Duration 2 Days

This course teaches you how to design marketing experiments with more than one factor and how to maximise the information that is gleaned from a marketing campaign.

Prerequisites

Before attending this course, you should have completed the Introduction to Statistics using SAS 9.2: ANOVA, Linear Regression & Logistic Regression course or equivalent training and have experience with regression models.

Who should attend?
Business analysts and market researchers with experience using regression analysis.

Delegates will learn how to

  • determine the appropriate sample size for your tests
  • build efficient experimental designs that generate as much information as possible for minimum cost
  • identify challenges associated with analysing experimental designs
  • test as many factors as possible in a given campaign
  • apply well-known experimental design practices to direct marketing efforts.

Outline

Efficient Experimentation

  • practical and technical definitions of efficiency

Testing a Single Factor

  • hypothesis testing
  • experimental design terminology
  • power and sample size
  • balance

Testing Multiple Factors

  • two or more factors
  • orthogonality
  • blocking

Too Many Treatments

  • fractional factorials
  • orthogonal arrays
  • optimal design and efficiency
  • augmenting and customizing designs

Software Addressed

This course addresses the following software product(s): SAS/QC, SAS/GRAPH. This course also addresses SAS/IML and SAS STAT. Without SAS/IML software you can still generate many of the designs used in this course. Without SAS/QC, none of the designs in the final chapter can be generated.

2 Days

Duration

This is a QA approved partner course

Delivery Method

Delivery method

Classroom

Face-to-face learning in the comfort of our quality nationwide centres, with free refreshments and Wi-Fi.

Find dates and prices

Online booking is currently not available for this course, to find out more please call us on 0345 074 7998 or email us at info@qa.com to discuss how we can help.

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