Logistic Regression in RStudio
In this free online course, learn about the techniques and models involved in logistic regression in RStudio.
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Do you want to be an expert in solving business problems using the logistic regression model? Solving complex business problems using different classification techniques will make you a valuable asset in any organization. This course will help you gain insight into the three main classification techniques (logistic regression, linear discriminant analysis and k-nearest neighbors) used to predict a qualitative categorical type of response, which offer fairly accurate predictions. You will discover that there are two types of business questions you need to ask to build the logistic regression model. This course will also help you to predict whether a particular outcome will be realized within a specific time frame. Imagine not needing to worry about the effect of individual variables on the output. You will learn how to predict the value of a response variable. In this process, you will learn to accurately estimate the effect of each predictor variable on the response variable.
You will be introduced to machine learning which refers to programming a computer to maximize or minimize a particular performance criterion based on past data. Then, you will find out that linear regression cannot be used for classification if the response variable contains more than two levels, which is why the logistic regression technique is more appropriate. This technique uses the sigmoid function to limit the output range. This ensures that the output of that particular function can capture the range of probabilities compared to the linear regression model. Discover that the reasons for estimating the relationship between dependent and independent variables are prediction and inference. The prediction reason is focused on the value of Y and not its relationship with other variables. The inference reason seeks to establish how the output variable differs with a change in each predictor variable.
Next, you will learn that categorical variables cannot assume a numerical value but can be classified into two or more non-numeric categories. In contrast, continuous variables can assume any numerical value over a specific interval or intervals. The course will discuss the basics of statistics and RStudio. Gain insight into the classification of statistical tools into descriptive and inferential categories. Descriptive statistics consists of methods for organizing, displaying and describing data through tables, graphs and summary measures. Inferential statistics consists of methods that use sample results to help make decisions or predictions about a population. Finally, you will learn about creating bar plots and histograms in R, plotting a decision tree, missing value imputation, results of simple logistic regression models and interpreting the results of classification models. This course will be of interest to data scientists, executives or students interested in learning about logistic regression. Why wait? Start this course today and become a logistic regression expert.Start Course Now
Statistics and Analysis
Statistics and Analysis - Learning Outcomes
Getting Started with R and R Studio
Outlier Treatment in R
Statistics and Analysis - Lesson Summary
Classification Models - Learning Outcomes
Confusion Matrix and LDA
Test-Train Split and Classifiers
Classification Models - Lesson Summary
Upon successful completion of this course, you should be able to:
- Describe the three ways of inputting data in R
- Explain the classifications of statistical tools
- Discuss the examples of classification techniques
- Explain the components of a comprehensive data dictionary
- Describe the steps in the data exploration process
- Outline the steps for solving business problems that require the classification of results
- Explain the three examples of classification techniques
- Describe the linear discriminant analysis technique
- Discuss the k-nearest neighbors classification technique
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