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4.65 out of 5
4.65
250 reviews on Udemy

Statistical Problem Solving in Geography

A college level course on how to apply statistics in geography, GIS and environmental science
Instructor:
Arthur Lembo
1,548 students enrolled
English [Auto]
perform statistical analysis with geographic data
understand descriptive and inferential statistics
correctly interpret statistical results

Do you struggle with statistics? Do you want to obtain a more quantitative background in the use of statistics in geography, environmental science, and GIS. Or, are you a student who is taking a course in statistics and geography but feel intimidated by the complexities of the subject? No worries. I created this class for you.

This class will walk you through each chapter of my textbook An Introduction to Statistical Problem Solving in Geography, along with the lecture notes I use in my course. It is designed specifically for geographers. So, the course isn’t really a math course, but an applied course in statistics for geographers.

You can also think of this course as a personal tutoring session. I will not only go over each chapter, teaching you statistics, but will also work side-by-side with you to use statistical software to recreate examples in the book so that you know how to actually perform the statistical analysis.

At the end of this course you will know how to apply statistics in the field of geography and GIS. And many of my students who were initially intimidated by statistics, find they actually love this subject, and have chosen to refocus their career on quantitative geography.

Basic Statistical Concepts in Geography

1
Course Welcome
2
Chapter 1: Introduction to Statistics and Geography
3
Chapter 1: Examples of hypotheses
4
Chapter 2: Geographic Data - Introduction

This lecture is an introduction to the terms and concepts of geographic data. You will learn about primary and secondary data sources, qualitative and quantitative data, and discreet and continuous variables.

5
Chapter 2: Data Types
6
Chapter 2: Classification
7
Chapter 2: Classification Map Examples

Descriptive Problem Solving in Geography

1
Chapter 3: Measures of Central Tendency
2
Chapter 3 - Measures of Dispersion
3
Chapter 3: Shape and Relative Position
4
Chapter 3: Considerations for Spatial Data and Descriptive Statistics
5
Chapter 4: Descriptive Spatial Statistics - Central Tendency
6
Chapter 4: Spatial Dispersion

This lecture concludes our discussion of spatial descriptive statistics by looking at measures of spatial dispersion.

The Transition to Inferential Problem Solving

1
Chapter 5: Probability - Terms and Definitions
2
Chapter 5: Probability - Probability Rules
3
Chapter 5: Probability - Binomial Distribution
4
Chapter 5: Probability - Geometric Distribution
5
Chapter 5: Probability - Poisson
6
Chapter 5: Probability - Poisson Spatial
7
Chapter 6: The Normal Distribution - Introduction
8
Chapter 6: The Normal Distribution - Calculation
9
Chapter 6: The Normal Distribution - Last Spring Frost Example
10
Chapter 8: Estimation in Sampling - Introduction
11
Chapter 8: Estimation in Sampling - Central Limit Theorem
12
Chapter 8: Estimation in Sampling - Confidence Intervals
13
Chapter 8: Estimation in Sampling - Examples

Inferential Problem Solving in Geography

1
Chapter 9: Elements of Inferential Statistics - Terms and Concepts
2
Chapter 9: Elements of Inferential Statistics - one sample difference of means
3
Chapter 10: Two Sample Difference Tests - Introduction

In this lecture you will learn how to perform two-sample difference tests. These include two-sample difference of means and proportions. You will also learn about a special case of the two sample difference test: the matched pairs test for dependent samples. Each test will include geographic examples for both the parametric and non-parametric cases.

4
Chapter 10: Two sample difference of means - calculation

In this lecture you will learn how to calculate and interpret a two-sample difference of means test. This will include both the parametric and non parametric tests.

5
Chapter 10: Difference of Proportions - calculation
6
Chapter 10: Matched Pairs Test
7
Chapter 11: ANOVA - Introduction

In this lecture you will learn how to perform a three or more sample difference test (ANOVA). The first lecture in this series will explain what ANOVA is, and what it does.

8
Chapter 11: ANOVA - Calculation

In this lecture you will learn how to calculate the ANOVA formulas. In learning the calculation methods, you will better understand how ANOVA works, and will then be ready to interpret the results of an ANOVA analysis.

9
Chapter 11: ANOVA - Examples

In this lecture, you will perform an ANOVA test and interpret the results for numerous geographical examples. You'll also learn how to use Excel to calculate and interpret an ANOVA table.

Inferential Spatial Statistics

1
Chapter 13: General Issues in Inferential Spatial Statistics

In this lecture you will learn about the unique characteristics of spatial data in statistical analysis and will be introduced to the concept of spatial autocorrelation and how to interpret variograms.

2
Chapter 14: Point Pattern Analysis - Nearest Neighbor

In this lecture you will learn a technique of point pattern analysis called nearest neighbor analysis. You'll learn what nearest neighbor analysis is, how to calculate it, and how to interpret the results. The lecture will also perform a nearest analysis on geographic data and interpret the results.

3
Chapter 14: Point Pattern Analysis - Quadrat Analysis

In this lecture you will learn a technique of point pattern analysis called quadrat analysis. You'll learn what quadrat analysis is, how to calculate it, and how to interpret the results. The lecture will also perform a quadrat analysis on geographic data and interpret the results.

4
Chapter 15: Area Pattern Analysis - Join Count

In this lecture you will learn a technique of area pattern analysis called join count analysis. You'll learn what join count analysis is, how to calculate it, and how to interpret the results. The lecture will also perform a join count analysis on geographic data and interpret the results.

5
Chapter 15: Area Pattern Analysis - Moran's I (Introduction)

In this lecture you will learn a technique of area pattern analysis called Moran's I Coefficient. This is the most common method of measuring spatial autocorrelation in a data set. You'll learn what Moran's I is, how to calculate it, and how to interpret the results. The lecture will also perform a Moran's I analysis on geographic data and interpret the results.

6
Chapter 15: Area Pattern Analysis - Moran's I (conclusion)

In this lecture you will continue to explore the concept of Moran's I analysis, by exploring a a geographic dataset. In addition, you will perform a Moran's I analysis to test for both global and local spatial autocorrelation.

Statistical Relationships Between Variables

1
Chapter 16: Correlation - Introduction

In this lecture you will be introduced to the concept of correlation. This first lecture in a series will introduce you to what correlation is, a how it is used with geographic data.

2
Chapter 16: Correlation - Pearson

In this lecture you will learn how to perform a Pearson's Correlation (the most common form of correlation) on a set of geographic data. You will learn how to calculate the Pearson Correlation component and interpret the results for a geographic data set.

3
Chapter 16: Correlation - Spearman

In this lecture you will learn how to perform a non parametric test of correlation, using the Spearman Rank Correlation coefficient. You will learn how to calculate the Spearman Correlation component and interpret the results for a geographic data set.

4
Chapter 17: Simple Linear Regression - Introduction

Now it gets interesting. In this lecture you will learn how to perform simple linear regression. Regression is the most common method of performing statistical analysis, and is the basis for statistical modeling of geographic data. You will learn what regression is, how to interpret regression results, and how to make predications based on your analysis.

5
Chapter 17: Simple Linear Regression - Calculation

This lecture will show you the nitty-gritty of how simple regression is calculated.

6
Chapter 17: Simple Linear Regression - Interpretation

In this lecture, you will analyze different geographic data sets, perform simple linear regression, interpret the results, and make predictions based on the results. When you complete this lecture, you will learn why regression is such a powerful statistical tool for any geographer.

7
Chapter 18: Multiple Regression - Introduction

I've saved the best for last. A geographer who knows how to perform multi-variate regression can command higher salaries and engage in more interesting and rewarding work. Multi-variate regression is one of the most powerful tools in a geographers toolbox. Unfortunately, most geographers do not know how to apply regression to real world scenarios. In this lecture you will conduct multivariate regression analysis on geographic data, correct for problems of multicollinearity and non significant predictors, and learn how to choose the best variables that explain a geographic phenomenon. In short, when you are done with this lecture, you are truly engaging in meaningful geographic research (not to say that everything else we've done here isn't meaningful!!).

You can view and review the lecture materials indefinitely, like an on-demand channel.
Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don't have an internet connection, some instructors also let their students download course lectures. That's up to the instructor though, so make sure you get on their good side!
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Includes

11 hours on-demand video
Certificate of Completion

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AulaGEO is a Ge-engineering specialized academy.

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