Completion of this course will give you an understanding of the concepts of the Bayesian approach, understanding the key differences between Bayesian and Frequentist approaches, and the ability to do basic data analyses. The lectures provide some of the basic mathematical development as well as explanations of philosophy and interpretation. For computing, you have the choice of using Microsoft Excel or the open-source, freely available statistical package R, with equivalent content for both options. This course combines lecture videos, computer demonstrations, readings, exercises, and discussion boards to create an active learning experience. On the Data tab, in the Analysis group, click Data Analysis. Hello Alejandro, If a good slope is say -3.45 (with a 95 confidence interval of -3.6 to -3.3), then using a slope of -4.0 will mean that the predictions from this model will be worse than those from the model with the good slope estimate. In particular, the Bayesian approach allows for better accounting of uncertainty, results that have more intuitive and interpretable meaning, and more explicit statements of assumptions.
They tell you how well the calculated linear regression equation fits your data. Here’s a breakdown of what each piece of information in the output means: EXCEL REGRESSION ANALYSIS OUTPUT PART ONE: REGRESSION STATISTICS.
We will compare the Bayesian approach to the more commonly-taught Frequentist approach, and see some of the benefits of the Bayesian approach. Excel Regression Analysis Output Explained: Multiple Regression. We will learn about the philosophy of the Bayesian approach as well as how to implement it for common types of data. This course introduces the Bayesian approach to statistics, starting with the concept of probability and moving to the analysis of data.
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