Regression models analysis is a predictive modelling technique used for determining the relationship between dependent and independent variables. This free online course on Alison describes the point and interval estimation as well as the differences between these estimations, including how point estimations uses a single value while interval estimation uses a range of numbers to get information from the population. You will be introduced to the different types of residual analysis.
This course describes how the Python code is used for the scatter plot diagram and for the regression equation. You will learn about the importance of assumptions on error terms. You will also learn about the concept of the assumed regression model and the differences between simple and multiple regression. You will also be introduced to the assumed regression model and how it is not an adequate representation.
Lastly, this course explains the intuition behind the maximum likelihood estimation theory. You will learn about the estimation of Poisson and exponential distribution parameters. You will study the python demo for the estimation of population parameters for the regression equation. This course also takes you through the relationship between the odds ratio and the coefficients of independent variables. If you are interested in learning how regression analysis models are used for forecasting, then this exciting course is for you.
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View All Learning Outcomes View Less All Alison courses are free to enrol study and complete. To successfully complete this course and become an Alison Graduate, you need to achieve 80% or higher in each course assessment. Once you have completed this course, you have the option to acquire an official Diploma, which is a great way to share your achievement with the world.
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Knowledge & Skills You Will Learn
