Math 315 Applied Regression Analysis
Have you ever thought about the current price of your or your family’s property? Do you want to know how experts predict the prime rate for the next season? Do you want to know if a specific medicine has any significant effect on the odds of a positive result in a medical test? These and similar questions can be answered in a regression analysis course.
Regression analysis is one of the most widely used techniques for analyzing the logical process of using an equation to express the relationship between a variable of interest and a set of related predictor variables. Applications of regression are numerous and occur in almost every field, including engineering, the physical and chemical sciences, economics, managements, life and biological sciences, and the social sciences. Regression models are used for several purposes, including: data description, parameter estimation, prediction and estimation, and control. Some examples of the use of regression analysis are:
Suppose that you want to predict the sale price of your property. The price of your property depends on many factors such as floor area, number of bedrooms, location, heating system, age of property, lot size, etc. You can collect these data from internet sources and use regression analysis to predict the value of your property.
A chemical engineer uses the Michaelis-Menten equation y=β1x/(x+β2)+ε to describe the relationship between the velocity of a reaction y and the concentration x. In this model, β1 is the asymptotic velocity of the reaction, that is, the maximum velocity as the concentration gets large. If a sample of observed values of velocity at different concentrations is available, then regression analysis can be used to estimate the maximum velocity.
In Math 315 you will learn to specify an appropriate model and use a statistical software package to analyze your data. You will also learn to check the adequacy of the proposed model and perform model validation.