Alan Anderson

Alan Anderson, PhD is a teacher of finance, economics, statistics, and math at Fordham and Fairfield universities as well as at Manhattanville and Purchase colleges. Outside of the academic environment he has many years of experience working as an economist, risk manager, and fixed income analyst. Alan received his PhD in economics from Fordham University, and an M.S. in financial engineering from Polytechnic University.

Articles & Books From Alan Anderson

Cheat Sheet / Updated 03-10-2022
Summary statistical measures represent the key properties of a sample or population as a single numerical value. This has the advantage of providing important information in a very compact form. It also simplifies comparing multiple samples or populations. Summary statistical measures can be divided into three types: measures of central tendency, measures of central dispersion, and measures of association.
Cheat Sheet / Updated 12-21-2023
Statistics make it possible to analyze real-world business problems with actual data so that you can determine if a marketing strategy is really working, how much a company should charge for its products, or any of a million other practical questions. The science of statistics uses regression analysis, hypothesis testing, sampling distributions, and more to ensure accurate data analysis.
Article / Updated 03-26-2016
Many different techniques have been designed to forecast the future value of a variable. Two of these are time series regression models and simulation models. Time series regression models A time series regression model is used to estimate the trend followed by a variable over time, using regression techniques.
Article / Updated 03-26-2016
For a dataset that consists of observations taken at different points in time (that is, time series data), it's important to determine whether or not the observations are correlated with each other. This is because many techniques for modeling time series data are based on the assumption that the data is uncorrelated with each other (independent).
Article / Updated 03-26-2016
Before you apply statistical techniques to a dataset, it's important to examine the data to understand its basic properties. You can use a series of techniques that are collectively known as Exploratory Data Analysis (EDA) to analyze a dataset. EDA helps ensure that you choose the correct statistical techniques to analyze and forecast the data.
Article / Updated 03-26-2016
A statistic is said to be robust if it isn’t strongly influenced by the presence of outliers. For example, the mean is not robust because it can be strongly affected by the presence of outliers. On the other hand, the median is robust — it isn’t affected by outliers. For example, suppose the following data represents a sample of household incomes in a small town (measured in thousands of dollars per year): 32, 47, 20, 25, 56 You compute the sample mean as the sum of the five observations divided by five: The sample mean is $36,000 per year.
Article / Updated 03-26-2016
Regression analysis is a statistical tool used for the investigation of relationships between variables. Usually, the investigator seeks to ascertain the causal effect of one variable upon another — the effect of a price increase upon demand, for example, or the effect of changes in the money supply upon the inflation rate.
Article / Updated 03-26-2016
You can use the adjusted coefficient of determination to determine how well a multiple regression equation "fits" the sample data. The adjusted coefficient of determination is closely related to the coefficient of determination (also known as R2) that you use to test the results of a simple regression equation.
Article / Updated 03-26-2016
You can determine the relationship between two variables with two measures of association: covariance and correlation. For example, if an investor wants to understand the risk of a portfolio of stocks, then he can use these measures to properly determine how closely the returns on the stocks track each other. Covariance is used to measure the tendency for two variables to rise above their means or fall below their means at the same time.
Article / Updated 03-26-2016
When testing a hypothesis for a small sample where you have to find the appropriate critical left-tail value, this value depends on certain criteria. In addition to being negative, the value also depends on the sample size and whether or not the population standard deviation is known. A left-tailed test is a test to determine if the actual value of the population mean is less than the hypothesized value.