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Linear regression is used to model the relationship between two variables and estimate the value of a response by using a line-of-best-fit. This calculator is built for simple linear regression, where only one predictor variable (X) and one response (Y) are used.
You can use statistical software such as Prism to calculate simple linear regression coefficients and graph the regression line it produces. For a quick simple linear regression analysis, try our free online linear regression calculator .
This calculator uses a two-sample t test, which compares two datasets to see if their means are statistically different. That is different from a one sample t test, which compares the mean of your sample to some proposed theoretical value.
Simple linear regression fits a straight line through your data to find the best-fit value of the slope and intercept. Simple logistic regression estimates the probability of obtaining a “positive” outcome (when there are only two possible outcomes, such as “positive/negative”, “success/failure”, or “alive/dead”, etc.).
If you really want to know a value for r 2, use nonlinear regression to fit your data to the equation Y=slope*X. Prism will report r 2 defined the first way (comparing regression sum-of-squares to the sum-of-squares from a horizontal line at the mean Y value).
Prism presents a full set of simple linear regression results. Learn how to interpret them.
In many experiments the relationship between X and Y is curved, making linear regression inappropriate. It rarely helps to transform the data to force the relationship to be linear. Better, use nonlinear curve fitting.
Perform linear regression analysis of a data set in Prism.
Standard deviation of the residuals: Sy.x, RMSE, RSDR. After fitting data with linear or nonlinear regression, you want to know how well the model fits the data. One way to quantify this is with R 2. Another way is to quantify the standard deviation of the residuals.
To perform simple logistic regression on this dataset, click on the simple logistic regression button in the toolbar (shown below). Alternatively, you can click on the "Analyze" button in the toolbar, then select "Simple logistic regression" from the list of available XY analyses.