Learn Linear Regression in R

Dev colleagues, the Learn Linear Regression in R equips you to o implement linear regression using the R programming language. Linear regression models are used in machine learning, so this course serves as an introduction to the topic as well. R is used by professionals in the Data Analysis and Data Science fields as part of their daily work. Take-Away Skills - learn how to make linear regression models using R.  Skill-based training modules include: 1) Introduction to Linear Regression in R - Linear Regression is the workhorse of applied Data Science; it has long been the most commonly used method by scientists and can be applied to a wide variety of datasets and questions, 2) Assumptions of Simple Linear Regression - While the linear regression is perhaps the most widely applied method in Data Science, it relies on a strict set of assumptions about the relationship between predictor and outcome variables, 3) Assumptions of Linear Regression (Outliers - Our next step is to check for outlier data points. Linear regression models also assume that there are no extreme values in the data set that are not representative of the actual relationship, 4) Building a Simple Linear Model - Simple linear regression is not a misnomer–– it is an uncomplicated technique for predicting a continuous outcome variable, Y, on the basis of just one predictor variable, X, 5) Quantifying Model Fit - Once we have an understanding of the kind of relationship our model describes, we want to understand the extent to which this modeled relationship actually fits the data., 6) Checking Model Residuals, 7) Visualizing Model Fit - it is alway a best practice to produce visual summaries to assess our model., 7) Reading Model Results - We’ve done our due diligence and confirmed that our data fulfills the assumptions of simple linear regression models, 8) Assessing Simple Linear Regression - Let’s practice our model interpretation skills! We know that for continuous independent variables, like podcasts, the regression coefficient, 9) Making Predictions - Data Scientists are often interested in building models to make predictions on new data. While the add_predictions() function from the modelr package, 10) Multiple Linear Regression - the results of simple linear regression models and show how the results convey a substantial amount of information about the relationship between two variables, 11) Assessing Multiple Linear Regression.

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Much career success, Lawrence E. Wilson - Online Learning Central