Statistics for Business Analytics: Modelling and Forecasting (edX)

We want to be able to answer questions like:

• What is the strength of the relationship between quality of customer experience and the likelihood of repeat purchases?
• How accurately can we predict residents’ use of a local park based on their known demographics?
• In what way are sales likely to fluctuate over the next 6 months, based on past data?

We consider questions like these across three topics:

• Topic 1 starts with simple, familiar ideas like correlation and builds on these to consider how simple linear regression can be applied to quantify the relationships between variables.
• Topic 2 examines multiple linear regression and considers how we can establish models that allow us to predict values for variables of interest in circumstances where there are many variables at work.
• Topic 3 considers the details of time series forecasting , using different methods of trend fitting to make predictions about future data.

This course is part of the Statistics for Business Analytics Professional Certificate.

What you'll learn
Upon successful completion of this course, you will be able to:

• Interpret the different components of a linear regression equation.
• Distinguish between statistical measurements such as R, R2 and adjusted R2 to assess goodness-of-fit for a regression model.
• Use technological tools to construct simple and multiple linear regression models.
• Describe the components of a time series.
• Select from a range of different methods to determine the most appropriate choice for trend fitting and forecasting for a given set of time series data.