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Linear regression calculator. 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.
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 .
Post test following two-way (or higher) ANOVA. 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. Try for Free.
Descriptive statistics, detect outlier, t test, CI of mean / difference / ratio / SD, multiple comparisons tests, linear regression. Statistical distributions and interpreting P values. Calculate P from t, z, r, F or chi-square, or vice-versa. View Binomial, Poisson or Gaussian distribution.
Equation: Fitting a straight line on a semi-log or log-log graph. The nonlinear regression analysis fits the data, not the graph. Since Prism lets you choose logarithmic axes, some graphs with data points that form a straight line follow nonlinear relationships.
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.
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.
After entering data, click Analyze, choose nonlinear regression, choose the panel equations for lines, and choose Segmental linear regression. Model Y1 = intercept1 + slope1*X
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.). How to: Simple linear regression. Finding the best-fit slope and intercept.