Explanation
Applied Linear Regression Analysis (using R,SPSS,SAS,Python) This course teaches you how to perform linear regression analysis from the very basic, advanced/expert level, depending on your needs. The basic philosophy of teaching (or knowledge transfer) that I have adopted for this course is that students learn and understand the ‘basics of analytical methods’ first, before learning how to use those methods to perform data analysis through software. This is different from some (similar) courses where the focus is on teaching how to use software for regression analysis (without an in-depth understanding of the regression process itself). . My aim is for you to develop knowledge of regression analysis as a modeling technique first, and have the confidence to solve any modeling/forecasting problem that requires linear modeling. This means that the first part of the course is mostly independent of the software, although I use R-software to illustrate the concepts and also to help you understand and interpret the output of the software for re-analysis. return.
We believe that once you have mastered this important part of the system, you should be able to use any software for regression analysis. As you will notice, the rules and steps/methods for automatic regression analysis are very similar in different software programs (including the four we use in this course). What is also very important, is that the output of the regression analysis is remarkably and comprehensibly similar to the structure, mostly of the software. Therefore, my point of view (and the reason I took this approach) is that, if you understand the basics of the regression process from the beginning, you should then be able to use any software and interpret whatever comes out of it. regression analysis, and you should also be able to easily navigate and use different software (if you learn how to use or code that particular software, of course).
What will you learn?
- Understanding how automatic regression analysis works, including theoretical basics, techniques, working examples, live demonstrations of four software
- Basics and requirements for performing a good linear regression, including data requirements, and materials for preliminary investigations (e.g. graphs)
- How to use different tools, parameters, and measurements to evaluate if a linear regression model fits your data, and ways to improve the fit.
- Perform direct regression analysis in one (or all) of the four software covered, namely R, SPSS, SAS, Python. You will see learning about software demonstrations
- The course fully covers applied linear regression analysis. So you don’t need to do the same course again (except to learn to use other software)
Who is this course for?
- Statistical modelers, data analysts, data scientists, students, and researchers who want to understand exactly how linear regression works in practice/applications, AND/OR people interested in learning how to perform regression analysis by using one or more of the software used in this course (ie SAS, R, SPSS, Python).
- People interested in understanding how the four different software (used in this course) are used for linear regression analysis
Details of Applied Linear Regression Analysis (using R, SPSS, SAS, Python)
- Publisher: Udemy
- Teacher: Charles R Lawoko
- Language : English
- Level : All Levels
- Number of courses: 115
- Duration : 13 hours and 52 minutes
Content of Applied Linear Regression Analysis (using R, SPSS, SAS, Python)
Requirements
- Some familiarity with basic statistical terminology (typically a first or second year introductory university course in applied statistics or statistical data analysis). Some basic understanding of mathematical equations if you want to understand the basic theory part (although this is not a pre-requisite for the course.
- I think you should be able to use whatever software you choose to learn (like the four software used). Whatever software you choose to use, you should be able to run that software, fetch data, etc., at a minimum.
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