Excel Regression Models for Business Forecasting

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  • Welcome and Critical Information
  • Regression Models
    • In this module, we explore the context and purpose of business forecasting and the three types of business forecasting using regression models. We will learn the theoretical underpinning for a regression model, and understand the relationship between explanatory variables and dependent variables. We will first focus on single variable or simple regression, and learn how to critically evaluate the model using regression diagnostic tools and then use our models for forecasting to suit our organisation's needs.
  • Multiple Variable Regression
    • In this module, we extend the simple regression model to take in multiple explanatory variables. We will extend the theoretical underpinning for a regression model by involving multiple dependent variables. We will learn how to critically evaluate the multiple regression models using regression diagnostic tools and then use our models for forecasting to suit our organisation's needs.
  • Dummy Variable Regression
    • In this module, we extend the multiple regression model to take in qualitative binary explanatory variables. We will extend the theoretical underpinning for a multiple regression model by creating dummy variables for binary qualitative data. We will learn how to critically evaluate the dummy variable regression models using regression diagnostic tools and then use our models for forecasting to suit our organisation's needs.
  • Seasonal Dummy Regression
    • In this module, we extend the binary dummary variable regression model to take in seasonal variables. We will extend the theoretical underpinning for a binary dummy variable regression model by creating a series of dummy variables to capture seasonality. We will learn how to critically evaluate the seasonal dummy regression models using regression diagnostic tools and then use our models for forecasting to suit our organisation's needs.
      In this module we will also explore autoregressions - their theoretical underpinning, creating an autoregression, critically evaluating this, and utilising our model for business forecasting.
      We will end the module by learning how to create a composite forecast by combining two forecasts across this course and the first course in this specialisation.

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