In this tutorial, you will learn how to report **Linear Regression** results using **R**. Linear Regression is a fundamental statistical learning technique and a powerful prediction analysis tool for data scientists. Reporting results in a clear and concise way is crucial to effectively communicate important insights and findings.

### Step 1: Load the required libraries and the dataset

First, load the necessary libraries and a dataset to perform the linear regression analysis. In this tutorial, we will use the built-in `mtcars`

dataset in R.

The required libraries are:

1. **tidyverse** for data manipulation and visualization

2. **ggplot2** for data visualization

3. **broom** for statistical summary information

You can install and load them using the following code:

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install.packages(c("tidyverse", "ggplot2", "broom")) library(tidyverse) library(ggplot2) library(broom) |

Now, let’s load the `mtcars`

dataset:

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data(mtcars) head(mtcars) |

### Step 2: Fit a linear regression model

Now that we have loaded the dataset and necessary libraries, let’s fit a linear regression model using the `lm()`

function in R. For demonstration purposes, we will use `mpg`

as the dependent variable (response) and `hp`

(horsepower) as the independent variable (predictor).

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linear_model <- lm(mpg ~ hp, data = mtcars) summary(linear_model) |

### Step 3: Create a table with regression results

Use the **tidy** function from the **broom** package to create a table with the coefficients, standard errors, t-values, and p-values of the linear regression model.

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linear_results <- tidy(linear_model) linear_results |

With the tidy output, you can easily visualize results in a neat, organized table format.

### Step 4: Visualize the linear regression results

Another great way to report linear regression results is by visualizing them using **ggplot2**. The following code generates a scatter plot with the linear regression line.

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ggplot(mtcars, aes(x = hp, y = mpg)) + geom_point() + geom_smooth(method = "lm", se = FALSE, color = "red") + labs(title = "Scatter plot of MPG vs HP", x = "Horsepower (hp)", y = "Miles per gallon (mpg)") |

## Output

## Full Code

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# Load libraries and dataset install.packages(c("tidyverse", "ggplot2", "broom")) library(tidyverse) library(ggplot2) library(broom) data(mtcars) # Fit linear regression model linear_model <- lm(mpg ~ hp, data = mtcars) # Create a table with regression results linear_results <- tidy(linear_model) # Visualize the linear regression results ggplot(mtcars, aes(x = hp, y = mpg)) + geom_point() + geom_smooth(method = "lm", se = FALSE, color = "red") + labs(title = "Scatter plot of MPG vs HP", x = "Horsepower (hp)", y = "Miles per gallon (mpg)") |

## Conclusion

In this tutorial, you have learned how to report linear regression results using R programming. Now you can perform linear regression analyses, create tables with relevant model information, and visualize the results using scatter plots with regression lines. These methods will help you effectively communicate the key findings when working with linear regression models.