![]() ![]() Saves fit formulas and upper and lower confidence intervals for a single variable to the data table. Because standard least squares typically uses the Y (vertical) axis as the response axis, the Auto setting defaults to the Y axis.įor log and other axis transformations, applies computations on the transformed coordinates. Specifies the axis for the variable that is used as the response in the calculation of the linear regression line. Highlight a variable name and click an arrow to reposition it.įor an example using Variables, see Example of an Area and Line Chart. Use arrows to re-order the display if there are multiple variables in a zone. In the Variables option, select the specific color or size variable to apply to each graph. ![]() Drag a second variable to the Color or Size zone, and drop it in a corner. Tip: If you have multiple graphs, you can color or size each graph by different variables. –Ědd or remove the effect of applying the Color, Size, Shape, or Freq variable to the variable in the zone. – Show or hide the elements corresponding to a variable in a zone. Your data is linear if the pattern in its data points resembles a. Note: These options do not apply to variables in the Group X, Group Y, Wrap, or Page zones.Ĭheck boxes are followed by the zone designation and the name of the variable. A linear trendline is a best-fit straight line that is used with simple linear data sets. Shows or hides graph elements for variables, or re-orders the display of variables. You can show the root mean square error (RMSE), R-square, the equation of the regression line, and the F Test value. Both types of intervals are fixed at 95% confidence. Shows or hides confidence intervals for the predicted value (Fit) or for individual values (Prediction). Specifies the polynomial degree of the linear regression fit, which can be linear, quadratic, or cubic. See Modeling Specifications in Predictive and Specialized Modeling. The smoothing model is selected from a subset of state space smoothing models defined by Hyndman et al. Includes options for a forecast model, the number of seasonal and forecast periods, and constraining parameters. Smoothing for equally spaced X values with optimal seasonality. A linear regression assuming Cauchy distributed residuals, to de-emphasize outliers. ![]()
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