{"id":9297,"date":"2022-11-01T13:30:55","date_gmt":"2022-11-01T13:30:55","guid":{"rendered":"https:\/\/www.gologica.com\/elearning\/?p=9297"},"modified":"2025-04-09T07:25:54","modified_gmt":"2025-04-09T07:25:54","slug":"chapter-6-getting-started-with-linear-regression-in-r","status":"publish","type":"post","link":"https:\/\/www.gologica.com\/elearning\/chapter-6-getting-started-with-linear-regression-in-r\/","title":{"rendered":"Chapter 6: Getting Started With Linear Regression In R"},"content":{"rendered":"\t\t
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Table Of Content<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\tChapter : Overview \u2013 Data Science Tutorial for Beginners<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t<\/a>\n\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t
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\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\tChapter 1: What is data and the importance of data 2022?<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t<\/a>\n\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t
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\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\tChapter 2: Introduction to Data Science<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t<\/a>\n\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t
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\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\tChapter 3: Data Scientist vs Data Analyst vs Data Engineer: Job Role, Skills, and Salary<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t<\/a>\n\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t
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\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\tChapter 4: Top 15 Data Science Tools Everyone Should Know in 2022<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t<\/a>\n\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t
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\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\tChapter 5: Data Science with R: Getting Started<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t<\/a>\n\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t
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\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\tChapter 6: Getting Started With Linear Regression In R<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t<\/a>\n\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t
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\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\tChapter 7: A Guide to Time Series Forecasting in R You Should Know<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t<\/a>\n\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t
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\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\tChapter 8: How to Build a Career in Data Science<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t<\/a>\n\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t
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\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\tChapter 9: How to Become a Data Scientist in 2022<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t<\/a>\n\t\t\t\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t<\/div>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t
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Getting Started With Linear Regression In R<\/span><\/h1>

\"\"<\/a><\/p>

We live in an information-driven world where data reigns supreme. Unsurprisingly, we must assess relevant data to make critical business decisions. One of the more popular data analysis techniques is regression. The discipline of machine learning is expanding, and with it, a popular algorithm: linear regression. This post will teach you about linear regression and how it works in R.<\/span><\/p>

Why Linear Regression?<\/span><\/h2>

Before delving into linear regression, consider the requirement for a linear regression process using an analogy.<\/span><\/p>

Assume we were asked to forecast the number of skiers at a resort based on snowfall in the vicinity. The simplest method would be to draw a basic graph with snowfall quantities and skiers on the ‘X’ and ‘Y’ axes. Then, based on the graph, we may deduce that as the amount of snowfall grew, so will the number of skiers.<\/span><\/p>

As a result, the graph makes the link between skiers and snowfall clear. The number of skiers grows according to the amount of snowfall. Therefore, we can make better judgments on ski area operations based on the information provided by the graph.<\/span><\/p>

To grasp linear regression, we must first understand “regression.” Regression is a technique for determining correlations between a dependent variable (Y) and many independent variables (X). The independent variables are predictors or explanatory factors in this context, while the dependent variable is a response or target variable.<\/span><\/p>

A linear regression equation looks like this:<\/span><\/p>

y = B0 + B1x1 + B2x2 + B3x3 + ….<\/span><\/p>

Where B0 is the intercept(value of y when x=0),<\/span><\/p>

B1, B2, and B3 are the slopes.<\/span><\/p>

x1, x2, and x3 independent variables.<\/span><\/p>

Snowfall is an independent variable in this situation, whereas the number of skiers is a dependent variable. So, given that regression identifies correlations between dependent and independent variables, what precisely is linear regression?<\/span><\/p>

What is Linear Regression?<\/span><\/h2>

Linear regression is a statistical analytic technique demonstrating the link between two or more continuous variables. It develops a prediction model based on relevant data to demonstrate patterns. To develop the model, analysts often employ the “least square approach.” Other approaches exist, but the least square method is often used.<\/span><\/p>

The graph below demonstrates the link between a sample of people’s heights and weights. The red line is the linear regression that reveals a person’s height is positively connected to weight.<\/span><\/p>

Now that we know what linear regression is, let’s look at how it works and how we can use the linear regression formula to get the regression line.<\/span><\/p>

How Does Linear Regression Work?<\/span><\/h2>

We can better understand how linear regression works by considering the example of a dataset with two fields, Area and Rent, and is used to forecast the rent of a property depending on its location. The dataset is as follows:<\/span><\/p>

  1. We create a graph using the supplied data, with Area on the X-axis and Rent on the Y-axis. The graph will look somewhat like this. Take note of the linear pattern with a small dip.<\/span><\/li>
  2. The mean of Area and Rent is then calculated.<\/span><\/li>
  3. The mean is then plotted on the graph.<\/span><\/li>
  4. We draw a best-fit line that goes through the mean.<\/span><\/li>
  5. But there’s an issue. Multiple lines can be drawn across the mean, as seen below:<\/span><\/li>
  6. To solve this issue, we keep adjusting the line until the best-suited line has the shortest square distance from the data points.<\/span><\/li>
  7. Adding the squares of the residuals yields the least-square distance.<\/span><\/li>
  8. We can now deduce that Residual is the difference between Y-actual and Y-pred.<\/span><\/li>
  9. These formulae may be used to get the values of m and c for the best fit line, y = mx + c:<\/span><\/li>
  10. This assists us in determining the relevant values:<\/span><\/li><\/ol>
    1. We can then calculate the values of m and c.<\/span><\/li>
    2. We can now calculate the value of Y-pred.<\/span><\/li>
    3. We calculate that the least square value for the following line is 3.02.<\/span><\/li>
    4. Finally, we may plot the Y-pred, which is determined to be the best fit line.<\/span><\/li><\/ol>

      This demonstrates the linear regression technique in action. Let us now go on to our use case.<\/span><\/p>

      Use Case of revenue prediction, featuring linear regression<\/span><\/h2>

      Using a linear regression model in R, predict income from paid, organic, and social media visits.<\/span><\/p>

      We will now examine a real-world scenario in which we will estimate income using regression analysis in R. The following is an example dataset with which we will be working:<\/span><\/p>

      In this demonstration, we will use the following three attributes to forecast revenue:<\/span><\/p>