Data Analytics: Regression Analysis and Tests
Learn about the concept of regression analysis and tests in data analytics in this free online course on Alison
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Regression analysis is a powerful statistical tool, used in data analysis for the examination of the relationship between two or more variables. This free online course describes the confusion matrix and receiver operating characteristics curve that are commonly used in regression analysis. You will get to know how the classification matrix is used in sorting and determination on whether the predicted value matched the actual value. You will be introduced to receiver operating characteristics and how it is used for selecting a threshold.
This course explains the interpretation of different points in the receiver operating characteristics plot. You will learn about the process of model building and the general linear progression model. You will be introduced to the detection and modelling of curvilinear relationships. The course will also describe the use of python code for Reynolds' example and how linearity is commonly applied in generalized linear models.
Finally, you will get an insight into the test of independence using the investment ideologies. You will be able to use the Chi-square test with more than two proportions. The course will explain to you how to use python to test the independence of variables. You will also learn how to perform goodness of fit test for Poisson and for uniform distribution. If you are interested in acquiring knowledge on types of data and how to handle them, then this free online course on Alison is for you.Start Course Now
Regression Analysis - Learning Outcomes
Confusion matrix and Receiver Operating characteristics
Performance of Logistics Model
Regression Analysis Model Building
Regression Analysis - Lesson Summary
Chi Square Test
Chi Square Test - Learning Outcomes
Chi Square Test of Independence
Chi Square Goodness of Fit Test
Chi Square Test - Lesson Summary
Upon successful completion of this course, you will be able to:
- Describe confusion matrix and receiver operating characteristics curve
- Explain the classification matrix
- Describe how to select a threshold using receiver operating characteristics
- Describe the area under the receiver operating curve
- Discuss the interpretation of different points in the receiver operating characteristics plot
- Describe the test of independence using the investment example
- Explain the chi-square test statistics
- Explain how to test with more than two proportions
- Describe how to use python to test the independence of variables
- Discuss the goodness of fit test for Poisson
- Discuss the goodness of fit for uniform distribution
- Explain the effect of standardization
- List the different types of distances computation between objects
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