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Updated in [February 21st, 2023]

What does this course tell?
(Please note that the following overview content is from the original platform)


Simple Linear Regression in R | R Tutorial 5.1 | MarinStatsLectures.
Checking Linear Regression Assumptions in R | R Tutorial 5.2 | MarinStatsLectures.
Multiple Linear Regression in R | R Tutorial 5.3 | MarinStatsLectures.
Changing Numeric Variable to Categorical in R | R Tutorial 5.4 | MarinStatsLectures.
Dummy Variables or Indicator Variables in R | R Tutorial 5.5 | MarinStatsLectures.
Change Reference (Baseline) Category in Regression with R | R Tutorial 5.6 | MarinStatsLectures.
Including Variables/ Factors in Regression with R, Part I | R Tutorial 5.7 | MarinStatsLectures.
Including Variables/ Factors in Regression with R, Part II | R Tutorial 5.8 | MarinStatsLectures.
Multiple Linear Regression with Interaction in R | R Tutorial 5.9 | MarinStatsLectures.
Interpreting Interaction in Linear Regression with R | R Tutorial 5.10 | MarinStatsLectures.
Partial F-Test for Variable Selection in Linear Regression | R Tutorial 5.11| MarinStatsLectures.
Polynomial Regression in R | R Tutorial 5.12 | MarinStatsLectures.


We consider the value of this course from multiple aspects, and finally summarize it for you from three aspects: personal skills, career development, and further study:
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What skills and knowledge will you acquire during this course?
Completing the Linear Regression in R-Series 5 course provides learners with the skills and knowledge necessary to effectively use linear regression in their own data analysis projects. Learners will gain an understanding of how to check linear regression assumptions, create multiple linear regression models, convert numeric variables to categorical variables, create dummy variables, change the reference category in regression, include variables/factors in regression, create multiple linear regression models with interaction, interpret interaction in linear regression, use partial F-test for variable selection, and create polynomial regression models. These skills and knowledge can be applied to various career paths, such as Data Scientist, Business Analyst, Machine Learning Engineer, and Research Scientist.

How does this course contribute to professional growth?
Completing the Linear Regression in R-Series 5 course provides learners with the knowledge and skills to apply linear regression in R. This course contributes to professional growth by equipping learners with the skills and knowledge necessary to effectively use linear regression in their own data analysis projects. This knowledge can be used to check linear regression assumptions, create multiple linear regression models, convert numeric variables to categorical variables, create dummy variables, change the reference category in regression, include variables/factors in regression, create multiple linear regression models with interaction, interpret interaction in linear regression, use partial F-test for variable selection, and create polynomial regression models. This course can help learners pursue a career in data science, business analysis, machine learning engineering, and research science, all of which are in high demand and have a positive job outlook.

Is this course suitable for preparing further education?
This course is suitable for preparing further education, as the knowledge and skills gained can be applied to a variety of career paths, such as Data Scientist, Business Analyst, Machine Learning Engineer, and Research Scientist.