Learn how to use software tools like Excel, R, Python, and SPSS to automate regression and correlation analysis and explore the relationships between variables.

Description and examples of linear regression/multiple linear regression implementation in MATLAB, Python, and R. I used the same data set (Linear_regression_data.txt) across MATLAB, Python, and R to ...

Linear regression was invented at the beginning of the 19th century and today, after more than 200 years, it is still used extensively in practical applications for description and prediction purposes ...

Understanding the linear relationship between two numerical variables is essential for effective data analysis. Pearson’s correlation coefficient (\(r\)) measures the strength and direction of an ...

We might say that we have noticed a correlation between foggy days and attacks of wheeziness. However, in statistical terms we use correlation to denote association between two quantitative variables.

Figure 1: The results of multiple linear regression depend on the correlation of the predictors, as measured here by the Pearson correlation coefficient r (ref. 2). ( a ) Simulated values of ...

I use Python 3 and Jupyter Notebooks to generate plots and equations with linear regression on Kaggle data. I checked the correlations and built a basic machine learning model with this dataset.