Environmental Statistics and Modeling
Expert-defined terms from the Professional Certificate in Environmental Sustainability Analytics course at London School of Planning and Management. Free to read, free to share, paired with a professional course.
ANOVA (Analysis of Variance) #
A statistical technique used to compare the means of two or more groups to determine if they are significantly different from each other. It partitions the total variance in the data into variance due to different factors and error variance.
Central Limit Theorem (CLT) #
A fundamental concept in statistics that states that the distribution of the sample mean approaches a normal distribution as the sample size increases, regardless of the shape of the population distribution. This theorem allows for the use of parametric statistical tests, such as the t-test and ANOVA, even when the data are not normally distributed.
Confidence Interval (CI) #
A range of values that estimates the true population parameter with a specified level of confidence. It is calculated by adding and subtracting a margin of error to the sample statistic.
Correlation #
A statistical relationship between two variables that measures the strength and direction of the linear relationship between them. A positive correlation indicates that as one variable increases, the other also increases, while a negative correlation indicates that as one variable increases, the other decreases.
Design of Experiments (DoE) #
A systematic approach to planning and conducting experiments to efficiently and effectively estimate the relationships among factors and responses. It involves the identification of the factors that affect the response, the selection of the levels of those factors, and the design of the experiment to estimate the effects of the factors.
Environmental Impact Assessment (EIA) #
A process of evaluating the potential environmental impacts of a proposed project or development. It involves the identification, prediction, and assessment of the impacts, as well as the development of mitigation measures to minimize or eliminate adverse impacts.
Environmental Sustainability Analytics #
The use of statistical and computational methods to analyze environmental data and inform decision-making for sustainable development. It involves the integration of data from various sources, the application of statistical models and techniques, and the communication of results to stakeholders.
Generalized Linear Model (GLM) #
A statistical model that extends the linear regression model to allow for the response variable to have a non-normal distribution. It includes linear regression, logistic regression, and Poisson regression as special cases.
Geographic Information System (GIS) #
A system for capturing, storing, analyzing, and visualizing geographic data. It allows for the integration of spatial and attribute data to support decision-making in a wide range of applications, including environmental management and planning.
Hypothesis Testing #
A statistical procedure for testing a hypothesis about a population parameter based on a sample of data. It involves the calculation of a test statistic and the comparison of the test statistic to a critical value or p-value to determine the likelihood of the hypothesis being true.
Least Squares Regression #
A statistical method for estimating the linear relationship between a response variable and one or more predictor variables. It involves the minimization of the sum of the squared residuals, which are the differences between the observed and predicted values of the response variable.
Linear Regression #
A statistical model that describes the linear relationship between a response variable and one or more predictor variables. It is a special case of the generalized linear model.
Logistic Regression #
A statistical model used to analyze the relationship between a binary response variable and one or more predictor variables. It is a special case of the generalized linear model.
Multivariate Analysis #
A set of statistical techniques used to analyze data with multiple variables. It includes methods for reducing the dimensionality of the data, identifying patterns and relationships among the variables, and visualizing the results.
Normal Distribution #
A continuous probability distribution that is symmetrical and bell-shaped. It is often used to model real-world data, such as heights, weights, and test scores.
Non #
parametric Statistics: A class of statistical methods that do not assume a specific distribution for the data. These methods are often used when the data are not normally distributed or when the assumptions of parametric methods are not met.
Poisson Regression #
A statistical model used to analyze the relationship between a count response variable and one or more predictor variables. It is a special case of the generalized linear model.
Principal Component Analysis (PCA) #
A statistical method for reducing the dimensionality of data by identifying the principal components, which are linear combinations of the original variables that explain the most variance in the data.
Probability Density Function (PDF) #
A function that describes the probability distribution of a continuous random variable. It gives the probability that the random variable takes on a value within a given interval.
Probability Distribution #
A function that describes the possible values of a random variable and the probabilities associated with those values.
Random Variable #
A variable that takes on different values according to some underlying probability distribution.
Regression Analysis #
A statistical method for estimating the relationship between a response variable and one or more predictor variables. It includes linear regression, logistic regression, and Poisson regression as special cases.
Residual Analysis #
A statistical technique used to evaluate the goodness-of-fit of a regression model. It involves the calculation and analysis of the residuals, which are the differences between the observed and predicted values of the response variable.
Sample Mean #
The average of a sample of data. It is calculated by adding up all the values in the sample and dividing by the sample size.
Sample Standard Deviation #
A measure of the spread of a sample of data. It is calculated as the square root of the variance.
Sample Variance #
A measure of the spread of a sample of data. It is calculated by taking the average of the squared deviations of the sample values from the sample mean.
Statistical Inference #
The process of drawing conclusions about a population based on a sample of data. It involves the use of statistical methods to estimate population parameters and test hypotheses.
Student's t #
distribution: A probability distribution used to make inferences about the population mean when the sample size is small and the population standard deviation is unknown. It is a bell-shaped distribution that is similar to the normal distribution, but has heavier tails.
t #
test: A statistical test used to compare the means of two groups to determine if they are significantly different from each other. It is a special case of the generalized linear model.
Variability #
A measure of the spread of a set of data. It can be quantified using measures such as the range, variance, and standard deviation.
Variance #
A measure of the spread of a set of data. It is calculated as the average of the squared deviations of the data values from the mean.
Visualization #
The process of representing data in a graphical or visual format to facilitate understanding and communication. It includes methods such as bar charts, line graphs, scatter plots, and heat maps.
These glossary terms cover a wide range of concepts and techniques in environmen… #
Understanding these terms is essential for anyone working in the field of environmental sustainability analytics, as they provide the foundation for data analysis, modeling, and decision-making. By applying these techniques to environmental data, practitioners can gain insights into the complex systems that affect our planet and develop strategies for promoting sustainability.