Description
In this course, we will be combining the practical and theoretical aspects of regression analysis by building a ordinary least squares model to answer real world business questions then validating it using the Gauss Markov Theorem to ensure it is theoretically accurate as well. We will be taking the place of a management consultant who has been tasked by the client to find the key drivers of their sales using a regression model and then answer business questions has using that model. These are a few of the questions the client has asked us to answer: What was the ROI from marketing efforts in 2022? What was the impact of competitor marketing in 2022? What was the impact of the increase in price of our product?What is the price elasticity of demand for our product? Which month had the highest baseline sales?We expect our marketing to be 10% more efficient next year. If we keep spend at the same level, how many incremental units will we get from our marketing efforts next year? We would like to increase price by 10% next year, what impact will that have on our business? In addition to this, we will also briefly go over the theory of ordinary least squares regression and the Gauss Markov theorem. Which we will then put into practice to validate our model by analyzing the residuals. We will also learn how to evaluate our model using key statistics like the R Square, Durbin Watson and the MAPE. All analysis will be done in excel, so we will learn how to setup a model workbook in excel and learn how to use essential functions such as Index Match, Sumifs and Countifs. We will also go over how to build line graphs, scatterplots and histograms. This course is ideal for anyone interested in working in consulting, data analytics, data science, econometrics and academic research.
