# Statistical Analysis and Modeling in R: Performing Regression Analysis

R Programming 4.0+    |    Expert
• 9 videos | 1h 23s
• Includes Assessment
Rating 4.9 of 15 users (15)
Regression models are used to predict continuous values and are some of the most commonly used machine learning models. Use this course to grasp what exactly machine learning (ML) algorithms are and how you can use ML models to predict outcomes based on input data. Learn how regression models work, train them, and evaluate regression results using metrics such as R2 and RMSE. Perform regression analysis in R using the ordinary least squares regression. Build models using simple and multiple regression and train a regression model using cross-validation. Upon completing this course, you'll be able to perform regression to predict continuous values and evaluate these models using metrics such as the R2 and adjusted R2.

## WHAT YOU WILL LEARN

• Discover the key concepts covered in this course Recall the basic characteristics of machine learning models Examine how to fit a straight line on data to build a regression model and evaluate the model Identify and visualize the relationships in data Perform simple linear regression with a single predictor
• Perform multiple regression using multiple predictors Apply the regression model to get predictions for test data Build a regression model using cross-validation Summarize the key concepts covered in this course

## IN THIS COURSE

• In this video, you’ll learn more about your instructor and this course. You’ll learn about machine learning algorithms and how machine learning models are used to predict outcomes based on input data once they've been trained. You’ll also learn how regression models work, train regression models, and evaluate the results of a regression using metrics such as the R square and the root mean square error. You’ll also perform regression analysis in R.
• In this video, you’ll learn more about machine learning. Machine learning is when algorithms learn from your data, allowing those algorithms to make predictions. Machine learning algorithms are algorithms that automatically improve through experience without explicitly being programmed. These algorithms learn from data. You’ll feed in data to the algorithm, and the algorithm will use this data to learn and improve. Once your algorithm learns from data, this is a machine learning model.
• 3.  Building and Evaluating Regression Models Using R
In this video, you’ll learn more about regression. Regression algorithms allow you to predict a continuous outcome variable or target based on the value of predictor variables. Your x variables or features are the predictors. These variables or targets in regression analysis are continuous values. With regression analysis, you have a cause that’s an independent variable. You’ll look at example scenarios here.
• 4.  Visualizing Data Relationships Using R
In this video, you’ll watch a demo. In this demo, you’ll learn how to perform linear regression on your data. You'll fit a regression model using ordinary least squares regression. First, you’ll view the data you’ll be working with. You’ll open up the Files pane and scroll to find the data that says kc_house_data.csv, right click, and View File. You’ll see the data you’ll use to train a regression model and use it for prediction.
• 5.  Performing Simple Linear Regression in R
In this video, you’ll watch a demo. In this demo, you’ll fit a regression model. First, you’ll need to split the data into training data and test data. You’ll do that using a function available in the caTools package. You’ll run the command install.packages caTools. Then, you’ll use the library function to include the caTools package in your current program. Next, you’ll use the sample.split function in this package.
• 6.  Performing Multiple Regression in R
In this video, you’ll watch a demo. In this demo, you’ll see how to use the lm function to perform multiple regression. Multiple regression is when you have multiple predictors to predict the target variable. You can perform multiple regression using the lm function once again, the only thing that changes is the formula. You’ll see the first input argument, price ~ bedrooms + view.
• 7.  Deriving Predictions Using Regression Models in R
In this video, you’ll watch a demo. In this demo, you’ll use your fully trained regression model for prediction on the test data. You’ll perform this prediction by invoking the predict function. First, in R, you’ll pass in the model you’re using for prediction. That’s your linear model. You’ll pass in the data you want the prediction to be performed on, which is your test.data.
• 8.  Building Regression Models Using Cross-validation
In this video, you’ll watch a demo. In this demo, you'll see how to use cross-validation to train and evaluate your linear regression model. You’ll see cross-validation is a technique where you train your model on a subset of data, and then evaluate using a complementary subset. You’ll do this multiple times until you get the best possible model. Cross-validation is a technique you can use to mitigate overfitting your model on the training data.
• 9.  Course Summary
In this video, you’ll summarize what you’ve learned in the course. You learned the basics of linear regression and how to perform regression and interpret the results of regression. You learned machine learning algorithms are algorithms that learn from the data used to train them. You saw that machine learning helps us build predictive models that understand patterns in the training data, and then use these patterns to make predictions on unseen instances.

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