MIS171 Lecture Notes - Lecture 5: Simple Linear Regression, Predictive Modelling, Linear Regression

• School: Deakin University
• Department: Business and Law
• Course: MIS171
• Professor: All

Document Summary

Topic 5: predictive models part 1: simple linear regression (part a) Build a predictive model to answer the analytics problem. Assess developed model(s) rigorously to gain confidence that they are valid and reliable. Implementing a predictive model in some information systems or business process. Predictive analytics analyses past performance to predict the future by examining historical data, detecting patterns or relationships in these data, and then extrapolating these relationships forward in time. There are many techniques and tools that we can use for predictive modelling. We briefly discuss a couple of key techniques in the next slide. Classification: classify a categorical outcome into one of two or more categories based on various data attributes. Clustering: finding natural groups or clusters in data association: identify attributes that frequently occur together text mining: extracting information from unstructured data anomaly detection: finding changes or outliers. In descriptive analytics, we looked for relationships between variables.