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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.
1. Select category. 2. Choose calculator. 3. Enter data. 4. View results.
Confidence interval of a sum, difference, quotient or product of two means. Confidence interval of a standard deviation. Linear regression. Analyze, graph and present your scientific work easily with GraphPad Prism. No coding required.
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 .
Calculate P from t, z, r, F or chi-square, or vice-versa. View Binomial, Poisson or Gaussian distribution. Correct a P value for multiple comparisons and Bayes.
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.
This guide will walk you through the process of performing simple logistic regression with Prism. Logistic regression was added with Prism 8.3.0
Outlier calculator. Outliers make statistical analyses difficult. This calculator performs Grubbs' test, also called the ESD method (extreme studentized deviate), to determine whether the most extreme value in the list you enter is a significant outlier from the rest. Simply copy and paste your dataset into the calculator.
Nonlinear regression is used for modeling a wide array of physical, chemical, and biological processes such as: Modeling dose-response curves or answering other pharmacokinetic questions. Fitting growth or decay models for populations of interest. Modeling enzyme inhibition for drug development.
Multiple linear regression fits an equation that predicts Y based on a linear combination of X variables. This is a standard analysis that you can read about in many books. Options: • If the Y values are numbers of objects or events actually counted, Prism can do Poisson regression. If Y is a continuous variable, Prism does multiple linear ...