OLS Regression in R, OLS Regression is a statistical method used for modeling in the R programming language.

Additionally, the examination of linear relationships between a response variable is done using it.

A straight line can be used to represent the relationship between the two variables if it is linear.

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Additionally, this will appropriately fit our dataset.

A bivariate regression’s linear equation has the following structure:

y = mx + c where, y = response(dependent) variable m = gradient(slope) x = predictor(independent) variable c = the intercept

## OLS Data Analysis: Statistically Descriptive

R contains a number of built-in commands for describing data. To obtain the output of every element of an object, we utilize the list() command.

A data frame’s variables are all described using the summary() command. The command summary() is used with specific variables.

Simple charts can also help with data familiarisation. For any given set of data values, the hist() command generates a histogram.

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For any given item, the plot() command generates both univariate and bivariate graphs.

For data analysis, the following OLS regression instructions are helpful:

lm â€“ Linear Model lme â€“ Mixed effects glm â€“ General lm Multinomial â€“ Multinomial Logit Optim â€“ General Optimizer

## How to Use R’s OLS Regression Function

We’ll utilize R’s lm command, which handles linear modeling, to create OLS.

The UCI Boston Housing Prices dataset, which is freely available, will be used.

We employ – Data to implement OLS regression in R. (CSV)

Let’s begin by taking the first step using our R linear regression model.

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**Step 1:Â Â we import the crucial library that will be used throughout our work.**

library(caTools)

**Step 2: Now, we read our data that is present in the .csv format**

data = read.csv("/Desktop/Data/all.csv")

**Step 3: Now, using the str() method, we will show the concise structure of our data and its variables.**

str(data)

**Step 4: Then, using the head() function, we will show the first 6 data values to provide you with a quick overview of our data.**

head(data)

**Step 5: Now, we utilize the summary() function to grasp the various statistical characteristics of our labels, such as mean, median, 1st Quartile value, etc.**

summary(data)

**Step 6: We’ll start the process of developing our linear model right away. The set.seed() procedure is first started with the number 125. **

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set.seed() in R enables you to generate random numbers for modeling and simulation.

set.seed(125)

**Step 7: The division of our data into training and test sets is a crucial next step. **

We set the data division percentage to 75%, which means that 75% of our data would be used for training and the remaining 25% for testing.

data_split = sample.split(data, SplitRatio = 0.75) train <- subset(data, data_split == TRUE) test <-subset(data, data_split == FALSE)

**Step 8: Now that we have divided our data into a training set and a test set.**

**We can put our linear modeling model into practice as follows:**

model <- lm(X1.1 ~ X0.00632 + X6.575 + X15.3 + X24, data = train) #DataFlair

Finally, we use the same summary() function that we had previously implemented to show the summary of our model.

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summary(model)

## Summary

We now know that OLS regression in R is possible and uses ordinary least squares. We have also learned both how to use it and how to command it.

Additionally, we studied diagnosis in R, which aids with graph display. You have mastered every command in OLS regression in R at this point.

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