ML for Business Managers: Build Regression Model in R Studio
In this free online course, learn about the techniques and analysis involved in building regression models in R Studio.
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Are you a business manager looking to be an expert in solving complex business problems using linear regression in R Studio? This course will lead you through the necessary steps you should take when developing solutions to your business problems using linear regression. Discover that the most crucial aspect to consider when creating a model is having sound knowledge of the issue you intend to solve. This information is critical since the quality of the output depends upon the quality of the inputs. You will gain insight into the fact that the frequency distribution of qualitative data lists all categories and the number of elements belonging to each category. In contrast, the frequency distribution of quantitative data lists all the classes and the number of values that belong to each class. The course will discuss the importance of data pre-processing and data interpretation using machine learning techniques.
At first, you will be introduced to the concept of bivariate analysis, which refers to the simultaneous analysis of two variables. It explores the possibility of a relationship or differences between two variables and the resulting consequence. Then, you will discuss correlation, a statistical measure that indicates the extent to which two or more variables fluctuate together. A positive correlation suggests how those variables increase or decrease in parallel, whilst a negative correlation indicates the degree to which one variable increases as the other one decreases. You will gain insight into the fact that linear regression refers to a linear approach to modelling the relationship between a dependent variable and one or more independent variables. Discover that when you are handling and interpreting qualitative variables in a linear model, you first have to transform them into dummy variables, then run the regression whilst observing the beta and p values. The course will discuss the different linear methods and techniques.
Next, you will learn that the Residual Standard Error (RSE) is the average amount that the response will deviate from the true regression line, and it can also be considered as a measure of the absence of fit between the model and data. Simple linear regression is an approach for predicting a quantitative response Y based on a single predictor variable X. Understanding this section will help you recognise that the shrinkage method refers to fitting a model involving all predictor variables. The estimated coefficients are shrunk towards zero, relative to the least square estimates. Finally, you will study subset selection techniques, ridge regression and lasso in R. Assessing the accuracy of predicted coefficients, and the correlation matrix in R will follow. This course will be of interest to business managers, executives or students interested in studying about machine learning and linear regression. Why wait? Start this course today and become a linear regression and problem-solving expert.Start Course Now
Statistics and Machine Learning
Statistics and Machine Learning - Learning Outcomes
Getting Started with R and R Studio
Statistics and Machine Learning - Lesson Summary
Linear Regression - Learning Outcomes
Outlier Treatment in R
The F-statistic and Interpretation
Linear Methods and Techniques
Linear Regression - Lesson Summary
Upon successful completion of this course, you should be able to:
- Discuss the three ways of inputting data in R
- Outline the classifications of statistical tools
- Describe the steps in the process of building a machine learning model
- Discuss the steps in the data exploration process
- Explain the three measures of dispersion
- Discuss the three methods of treating outliers
- Outline the steps in the handling and interpretation of qualitative variables in a linear model
- Explain the contributors to the expected test error
- Describe the three main test-train split techniques
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