To assess the significance of linear regression results. To learn the relationship of each variable by the Partial Leverage Plot and Ellipse Plots.To study correlation between variables with Covariance and Correlation matrix.See Interpreting Regression Results Chapter for following information Perform a Linear Fit with Outlier Removal.To Find X from Y or Y from X The Find X/Y table allows you to obtain either a dependent variable value given an independent variable value, or an independent variable value given a dependent variable value, from the fit you performed on the data. You can also exculde the outliers from an analysis routine. Or, you can use the Q-Q plot to identify an outlier, which is introduced here. In Linear Fit, the outliers can be shown in results table by checking on the Outliers checkbox in the Linear Fit dialog. To identify the outliers in fitting process When we get the fitted curve, there may be a large difference between a few points and the fitted curve by the model, these points should be identified as Outliers. To perform multiple linear regression with boundary or constraint You can define a multiple linear regression function and set Constrain for it in the Nonlinear Curve Fit tool. To force the fitted curve go through a specific point in raw data, you can set a higher weight for the point.įor further information, please view this page.To force the fitted curve go through Origin (0,0), you can just fix the intercept to 0 for a linear or polynomial model.Here is an example of Finding the slope on the log-log plot by Apparent Fit To define the x data type for the fitted curve plot You can defined the X axis scales, fitted curves interval and range in Fitted Curve Plot: X data Types Advice and Tips To force fit curve through specified point Two methods are provided to make fitted curve go through certain points for Linear and Polynomial Regression: Apparent Fit will first transform your raw data into a new data space as specified by the graph axis type, and then fit the curve of the new data. To perform linear/polynomial fit on a graph according to current axis settings You can perform linear/polynomial fit on a graph according to current axis settings by checking on the Apparent Fit in Fit Control. Besides, the Model Degree of Freedom will be reduced due to the parameter fixed. To perform linear/polynomial fit with parameters fixed Fitting parameters can be fixed in tools above, For example, you can set the Intercept value to 0 by checking on the Fix Intercept in Fit Control dialog and set the Fix Intercept at = 0, which force the fitted line go through the origin point (0,0). However, if you want to treat errors of independent variable X as weight, the Fit Linear with X Error tool should be used, in which you can set both X and Y Errors in Input Data, which can be treated as weight. How To Perform the Fitting Fitting Control To perform linear regression on data with X/Y Error The Errors can exist for both dependent and independent values, Errors of dependent variable Y can be treated as weight in all Fitting Tools above by setting the Y Error column as Y Error in Input Data and designate the method of Error as Weight in Fit Control. After fitting, the model is evaluated using hypothesis tests and plots of residuals. The weight can be given to dependent variable in fitting to reduce the influence of the high leverage points. Parameters are estimated using a weighted least-square method. In addition, multiple linear regression can be used to study the relationship between several predictor variables and a response variable.Īnalysis: Fitting: Fit Linear with X ErrorĪnalysis: Fitting: Multiple Linear Regression Linear and polynomial regression calculate the best-fit line for one or more XY datasets. 4 Advanced: Linear fit for nonlinear model.2.2.3 To identify the outliers in fitting process.2.2.2 To perform multiple linear regression with boundary or constraint.2.2.1 To force fit curve through specified point.2.1.4 To define the x data type for the fitted curve plot.2.1.3 To perform linear/polynomial fit on a graph according to current axis settings.2.1.2 To perform linear/polynomial fit with parameters fixed.2.1.1 To perform linear regression on data with X/Y Error.
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