Analyse-it provides single, multiple linear regression and polynomial regression
(upto 6th order) to determine fits that predict a response based on 1 or more predictor
variables.
- Fit linear or polynomial fits to your data
Describe the relationship between 1 or more predictor variables and a response variable
with simple, multiple linear regression and polynomial regression (upto 6th order).
- Identify predictors contributing to the fit
Test which predictors contribute significant to the response to decide which can
be removed, and test the fit is better than the mean fit.
- Use parameter estimates to predict future observations
Predict future values of the response using the parameter estimates of how much
each predictor contributes to the response.
- Visualise the relationship and goodness-of-fit
View the fit on a scatter plot, with confidence and prediction intervals. Assess
goodness-of-fit and spot outliers with raw and standardised residual plots.
- Test whether the fit is better than the mean
ANOVA test the fit against fitting just the mean, to determine whether the predictors
are significantly better predictors than the mean.
