The Ordinary Least Squares (OLS) approach is the most frequently used estimation technique for linear models. For linear regression, there are seven conventional OLS assumptions. The first six are required to obtain the most accurate estimations. While the seventh hypothesis has no bearing on the quality of the estimates, analysts frequently evaluate it for other relevant reasons.
For any retailer, the holiday season is an important period of the year. For a store to effectively plan inventory, investments, and customer interaction strategies for the season, revenue forecasting during this time is essential. A “Linear Regression Model” needs to be produced by the consultant, who claims that it is founded on a set of hypotheses. The executive team of the retailer requires that the data scientist provide data-supported arguments for his assumptions.
Learn the different assumptions made by the Data Scientist in building a Linear Regression Model.
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