Upload your data or paste it manually to perform linear regression analysis.
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Data preview will appear here after file upload
Data preview will appear here after data entry
| Statistic | Value |
|---|---|
| Correlation (r) | - |
| R-squared | - |
| Adjusted R-squared | - |
| Intercept | - |
| Slope (Coefficient) | - |
| F-statistic | - |
| Degrees of Freedom | - |
| Significance (p-value) | - |
Click on the "File Upload" tab and select your data file (CSV, XLSX, or XLS format). Once uploaded, you'll see a preview of your data. Choose which columns represent your dependent variable (what you're predicting) and independent variable (your predictor). If your first row contains column names, make sure the "First row contains column headers" checkbox is selected. Click "Analyze Data" to run the regression.
Switch to the "Manual Input" tab and paste your data into the text box. You can use commas or tabs to separate values. For example: "X,Y" on the first line, then "1,2" on the next line, and so on. Select your data separator type (comma, tab, or auto-detect), choose your dependent and independent variable columns, and click "Analyze Data" to see your results.
After analysis, you'll see a table with key statistics on the left and a scatterplot with regression line on the right. The regression equation is Y = intercept + slope × X. Focus on R-squared to see how well your line fits the data, and check the p-value to determine if your results are statistically significant (look for values less than 0.05).
Use the export buttons at the bottom to save your work. "Copy Results to Clipboard" copies all statistics as text. "Copy Data to Clipboard" saves your input data in CSV format. "Export as PNG" creates an image of the entire analysis page, while "Export as PDF" generates a formatted PDF document with your results and chart.
If your dataset has missing values or incomplete rows, the tool will automatically skip them and only analyze complete data pairs. You'll see a warning message if any data was excluded. Make sure each row has values for both your dependent and independent variables to maximize the data used in your analysis.
For best results, organize your data in columns with one variable per column. Remove any text or symbols from your data cells (except column headers). Make sure all values are numbers. If using CSV format, save your file with either comma or tab separators. Check that you have at least 10-20 data points for reliable results.
Simple linear regression is a statistical method that analyzes the relationship between two variables. It creates a straight line (Y = a + bX) that best fits your data points, where Y is what you're predicting, X is your input variable, a is the intercept, and b is the slope showing how much Y changes when X increases by one unit.
R-squared tells you how much of the variation in your data is explained by the regression line. It ranges from 0 to 1. For example, an R-squared of 0.80 means 80% of the variation in Y can be explained by X. Higher values (above 0.7) indicate a good fit, while lower values suggest other factors may be important.
The correlation coefficient (r) measures the strength and direction of the relationship between two variables, ranging from -1 to +1. Values close to +1 show a strong positive relationship (both variables increase together), values close to -1 show a strong negative relationship (one increases as the other decreases), and values near 0 indicate no linear relationship.
The p-value tells you if your results are statistically significant. A p-value less than 0.05 means there's less than a 5% chance your results happened by random chance, which is generally considered statistically significant. If your p-value is above 0.05, the relationship might not be reliable.
You can upload CSV files (.csv), Excel files (.xlsx), or older Excel files (.xls). You can also manually paste your data using commas or tabs to separate values. Make sure your data has two columns of numbers, with optional headers in the first row.
While you can technically run regression with 3 data points, you need at least 10-20 observations for basic reliability. For more accurate and trustworthy results, 30 or more data points are recommended. More data generally leads to better predictions and more reliable statistics.