Module:  Statistics on Scatter Plots (Curve Fitting)                                                      Print

Lesson Objective Learn how to add a best fit line (using a variety of curve fitting algorithms), draw a curve, add average and standard deviation lines, or add error bars to a scatter plot or trellised scatter plot
Section Description
Available Statistics, Error Bars, Curve Drawing and Line Fitting The procedure for adding a number of different statistical measures to a scatter plot.
Orthogonal Straight Line Fit Calculations can be performed on either selected records or all records, the linear equation and correlation coefficient are displayed in the legend, and calculations can be applied to data in individual trellis panels.
Other Line Fit Algorithms (Curve Fit...) Building on the previous section, other curve fitting models are available with the same features for calculating based on selected or all records and different lines for individual trellis panels.  Detailed results are available for these other curve fit options.
Straight Line Discussion of the straight line fit model, and when to use this algorithm instead of orthogonal straight line fit.
Logarithmic Discussion of the logarithmic line fit model and limitations on numerical input.
Exponential Discussion of the exponential line fit model.
Power Discussion of the power line fit model and limitations on numerical input.
Polynomial Discussion of each of the following:  2nd, 3rd, 4th, and 5th order polynomial line fit models and the expected minimum input.
View Curve Fit Result How to display details for the line equations, standard deviations for each value, and correlation coefficients.
Average and Standard Deviation Lines calculated on data across either the X or Y direction show the average and variability range.
Curve Drawing Custom lines can be drawn based on your own mathematical expressions.
Error Bars Markers can show variability, represented as separate data columns, using upper and lower error bars extending from the marker.