# Train and Predict online with our Linear Regression Calculator

## Experience the Power of Machine Learning with Our Online Logistic Regression Calculator

Welcome to our online linear regression calculator! This powerful machine learning tool is designed to help you make accurate predictions based on your data. Whether you're new to the world of machine learning or a seasoned pro, our calculator makes it easy to train your data and generate predictions with just a few clicks.

Our calculator is completely free and accessible from anywhere with an internet connection. You can easily input your data, train your model, and generate predictions all in one place. Plus, you don't need any coding experience to use our calculator – we take care of the technical details for you.

Our tool uses the latest algorithms to perform efficient and accurate gradient descent, ensuring that your models are trained to their full potential. You can be confident in your predictions knowing that our calculator provides reliable and up-to-date results

It's worth noting that our linear regression calculator is highly sensitive to the learning rate parameter used in the gradient descent algorithm. While we've included a default value that typically works well for most datasets, it's important to experiment with different learning rates to find the optimal value that produces the best results for your specific data.

Additionally, it's important to ensure that all independent variables and the dependent variable have the same length when inputting your data. Our calculator takes care of the technical details of linear regression, allowing you to focus on the most important aspects of your analysis: training your data and making predictions

For additional information on entering data, **see the documentation**

### Linear Regression result

### Frequently Asked Questions (FAQ) About Linear Regression Calculator

In this section, we will address some of the most frequently asked questions (FAQ) related to our linear regression calculator. We will cover topics such as the significance of RMSE, SST, and SSR in linear regression, the difference between the linear regression calculator in machine learning and statistics, the use of gradient descent in linear regression, and more.

These three metrics are important in linear regression because they help us evaluate the accuracy and relevance of our model. RMSE tells us how well our model is able to predict the dependent variable, while SST and SSR help us understand the proportion of variation in the dependent variable that is explained by our independent variables. By analyzing these metrics, we can identify areas where our model needs improvement and fine-tune our machine learning algorithm to better predict the target variable.

SST stands for "Sum of Squares Total", which represents the total variation of the dependent variable. It is calculated as the sum of the squared differences between each data point and the mean of the dependent variable.

SSR stands for "Sum of Squares Regression", which represents the explained variation of the dependent variable due to the independent variables. It is calculated as the sum of the squared differences between each predicted value and the mean of the dependent variable.

RMSE stands for "Root Mean Square Error", which is a commonly used metric to evaluate the performance of a linear regression model. It measures the average distance between the predicted values and the actual values of the dependent variable. A lower RMSE value indicates better model performance.

NaN stands for "Not a Number," which usually occurs when there is missing or invalid data in your input variables or the learning rate is inappropriate for the data. In linear regression, NaN can occur when the algorithm encounters a division by zero or an infinite number. Therefore, it's essential to choose appropriate values for your input variables and learning rate to avoid NaN and get accurate results and predictions.

While both calculators perform linear regression, the machine learning calculator is limited to using gradient descent as its optimization method and is oriented towards machine learning applications. It also allows for making predictions. On the other hand, the linear regression calculator in the statistics section provides additional statistical measures such as adjusted R-squared value and F-statistic, and includes tests such as Durbin-Watson, Jarque-Bera, and Breusch-Pagan tests. It uses ordinary least squares as its optimization method.

Gradient descent is an optimization algorithm used to minimize the cost function in linear regression. The cost function measures the difference between the predicted values of the dependent variable and the actual values. Gradient descent works by calculating the gradient of the cost function with respect to the parameters of the model and updating these parameters iteratively until the cost function is minimized. By using gradient descent, we can adjust the coefficients of the independent variables in our linear regression model to better fit the data and improve the accuracy of our predictions.

The type of regression calculator (simple or multiple) depends on the data input, specifically the number of independent variables. If there is only one independent variable, then it is a simple linear regression calculator. If there are more than one independent variables, then it is a multiple linear regression calculator.