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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 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 07-10-2023
You can use the Central Limit Theorem to convert a sampling distribution to a standard normal random variable. Based on the Central Limit Theorem, if you draw samples from a population that is greater than or equal to 30, then the sample mean is a normally distributed random variable. To determine probabilities for the sample meanthe standard normal tables requires you to convertto a standard normal random variable.
Article / Updated 05-03-2023
After you estimate the population regression line, you can check whether the regression equation makes sense by using the coefficient of determination, also known as R2 (R squared). This is used as a measure of how well the regression equation actually describes the relationship between the dependent variable (Y) and the independent variable (X).
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.
Article / Updated 03-26-2016
Compared with other types of hypothesis tests, constructing the test statistic for ANOVA is quite complex. The first step in finding the test statistic is to calculate the error sum of squares (SSE).
Calculating the SSE enables you to calculate the treatment sum of squares (SSTR) and total sum of squares (SST).
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
When comparing data samples from different populations, two of the most popular measures of association are covariance and correlation. Covariance and correlation show that variables can have a positive relationship, a negative relationship, or no relationship at all.
A sample is a randomly chosen selection of elements from an underlying population.
Article / Updated 03-26-2016
When a data set contains a large number of repeated values, you can simplify the process of computing the mean by using weights — the frequencies of a value in a sample or a population. You can then compute the geometric mean as a weighted average.
You can calculate the weighted geometric mean in the same way for both samples and populations.
Article / Updated 03-26-2016
Quartiles split up a data set into four equal parts, each consisting of 25 percent of the sorted values in the data set. Quartiles are related to percentiles like so:
First quartile (Q1) = 25th percentile
Second quartile (Q2) = 50th percentile
Third quartile (Q3) = 75th percentile
Because the second quartile is the 50th percentile, it's also the median of a data set.
Article / Updated 03-26-2016
An unconditional, or marginal, probability is one where the events (possible outcomes) are independent of each other. When you create a joint probability table, the unconditional probability of an event appears as a row total or a column total.
For example, say that you create a joint probability table representing the distribution of students in a business school; you classify them according to major and whether they're working on a bachelor's degree or a master's degree.
