This project is a continuation of a previous project about the relationship between total farm income and the number and size of farms in Utah. However, the regression was only run using the number of farms as the explanatory variable in the regression and the dependent variable as the total farm income. The map displays the standard residuals which are supposed to randomly distributed with small residual values indicating and not too many outliers indicating a goodness of fit model. Standard residuals with greater residual values are seen as unreliable being there are more high value outliers.

IBM SPSS was used to run the regression analysis and find the R square value of the regression which is value between 0 and 1 which explains how much of the variability of total farm income the dependent variable can be explained by the number of farms in the state of Utah the explanatory variable. There is one R square value for the entire state in this regression as OLS regression is a global model rather than a local model meaning local geography is not accounted for. The R Square for the model was .285 meaning that 28.5 percent of the variability of total farm income in Utah in 2020 could be explained by the number of farms.