The coefficient reflects the expected change in the dependent variable for every 1 unit change in the associated explanatory variable, holding all other variables constant (for example, a 0.005 increase in residential burglary is expected for each additional person in the census block, holding all other explanatory variables constant). Coefficients are given in the same units as their associated explanatory variables (a coefficient of 0.005 associated with a variable representing population counts may be interpreted as 0.005 people). When the sign is positive, the relationship is positive (for example, the larger the population, the larger the number of residential burglaries). When the sign associated with the coefficient is negative, the relationship is negative (for example, the larger the distance from the urban core, the smaller the number of residential burglaries). The coefficient for each explanatory variable reflects both the strength and type of relationship the explanatory variable has to the dependent variable. Assess each explanatory variable in the model: Coefficient, Probability or Robust Probability, and Variance Inflation Factor (VIF).R-Squared Values Quantify Model Performance Said another way, your model tells approximately 39 percent of the residential burglary story.
An Adjusted R-Squared value of 0.39 would indicate that your model (your explanatory variables modeled using linear regression) explains approximately 39 percent of the variation in the dependent variable. Suppose you are creating a regression model of residential burglary (the number of residential burglaries associated with each census block is your dependent variable, y). Adding an additional explanatory variable to the model will likely increase the Multiple R-Squared value but may decrease the Adjusted R-Squared value. The Adjusted R-Squared value is always a bit lower than the Multiple R-Squared value, because it reflects model complexity (the number of variables) as it relates to the data and is consequently a more accurate measure of model performance.
Both the Multiple R-Squared and Adjusted R-Squared values are measures of model performance. (B) Examine the summary report using the numbered steps described below:Ĭomponents of the OLS Statistical Report Dissecting the Statistical Report
You will also need to provide a path for the Output Feature Class and, optionally, paths for the Output Report File, Coefficient Output Table, and Diagnostic Output Table.Īfter OLS runs, the first thing you will want to check is the OLS summary report, which is written as messages during tool execution and written to a report file when you provide a path for the Output Report File parameter. (A) To run the OLS tool, provide an Input Feature Class with a Unique ID Field, the Dependent Variable you want to model/explain/predict, and a list of Explanatory Variables.
Eviews output interpretation series#
Optional table of regression diagnostics OLS Model Diagnostics TableĮach of these outputs is shown and described below as a series of steps for running OLS regression and interpreting OLS results.Optional table of explanatory variable coefficients Coefficient table of OLS Model Results.Message window report of statistical results OLS Statistical Report.Output feature class Map of OLS Residuals.Output generated from the OLS Regression tool includes the following: