#### Overview of this book

Spatial statistics has the potential to provide insight that is not otherwise available through traditional GIS tools. This book is designed to introduce you to the use of spatial statistics so you can solve complex geographic analysis. The book begins by introducing you to the many spatial statistics tools available in ArcGIS. You will learn how to analyze patterns, map clusters, and model spatial relationships with these tools. Further on, you will explore how to extend the spatial statistics tools currently available in ArcGIS, and use the R programming language to create custom tools in ArcGIS through the ArcGIS Bridge using real-world examples. At the end of the book, you will be presented with two exciting case studies where you will be able to practically apply all your learning to analyze and gain insights into real estate data.
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Introduction to Spatial Statistics in ArcGIS and R
Measuring Geographic Distributions with ArcGIS Tools
Analyzing Patterns with ArcGIS Tools
Modeling Spatial Relationships with ArcGIS Tools
Working with the Utilities Toolset
Introduction to the R Programming Language
Creating Custom ArcGIS Tools with ArcGIS Bridge and R
Application of Spatial Statistics to Crime Analysis
Application of Spatial Statistics to Real Estate Analysis

## Linear regression with the Ordinary Least Squares (OLS) tool

The `Ordinary Least Squares` tool or OLS is a linear regression tool used to generate predictions or model a dependent variable in terms of its relationships to a set of explanatory variables. OLS is the best known regression technique and provides a good starting point for spatial regression analysis. This tool provides a global model of a variable or process you are trying to understand or predict. The result is a single regression equation that depicts a positive or negative linear relationship.

OLS is almost always an iterative process, so don't expect to simply run this tool once and be done. It's very challenging, especially in the social sciences, to find the correct explanatory variables for a dependent variable. In addition to spending many hours of research in identifying potential explanatory variables, you will, in most cases, need to run the OLS tool many times, examine the results, and perform your checks. At some point...