Course Spotlight: The Text Analytics Sequence

Text analytics or text mining is the natural extension of predictive analytics, and Statistics.com's text analytics program starts Feb. 6. Text analytics is now ubiquitous and yields insight in: Marketing: Voice of the customer, social media analysis, churn analysis, market research, survey analysis Business: Competitive…

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Course Spotlight: Constrained Optimization

Say you operate a tank farm (to store and sell fuel). How much of each fuel grade should you buy? You have specified flow and storage capacities, constraints on what types of fuels can be stored in which tanks, prior contractual obligations about minimum monthly…

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Week # 52 – Quasi-experiment

In social science research, particularly in the qualitative literature on program evaluation, the term "quasi-experiment" refers to studies that do not involve the application of treatments via random assignment of subjects.

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Week #51 – Curb-stoning

In survey research, curb-stoning refers to the deliberate fabrication of survey interview data by the interviewer.

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College Credit Recommendation

Statistics.com Receives College Recommendation from the American Council on Education (ACE) College Credit Recommendation for Online Data Science Courses from The Institute for Statistics Education at Statistics.com LLC The American Council on Education's College Credit Recommendation Service (ACE CREDIT) has evaluated and recommended college credit…

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Week #49 – Stemming

In language processing, stemming is the process of taking multiple forms of the same word and reducing them to the same basic core form.

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Week #48 – Structured vs. unstructured data

Structured data is data that is in a form that can be used to develop statistical or machine learning models (typically a matrix where rows are records and columns are variables or features).

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Week #47 – Feature engineering

In predictive modeling, a key step is to turn available data (which may come from varied sources and be messy) into an orderly matrix of rows (records to be predicted) and columns (predictor variables or features).

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Week #46 – Naive bayes classifier

A full Bayesian classifier is a supervised learning technique that assigns a class to a record by finding other records  with attributes just like it has, and finding the most prevalent class among them.

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Week #45 – MapReduce

In computer science, MapReduce is a procedure that prepares data for parallel processing on multiple computers. 

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Week #44 – Likert scales

Likert scales are categorical ordinal scales used in social sciences to measure attitude.  A typical example is a set of response options ranging from "strongly agree" to "strongly disagree."

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Week #43 – Node

A node is an entity in a network.  In a social network, it would be a person.  In a digital network, it would be a computer or device.

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Week #41 – K-nearest neighbor

K-nearest-neighbor (K-NN) is a machine learning predictive algorithm that relies on calculation of distances between pairs of records.

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Word #40 – Kappa Statistic

The kappa statistic measures the extent to which different raters or examiners differ when looking at the same data and assigning categories.

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Word #39 – Censoring

Censoring in time-series data occurs when some event causes subjects to cease producing data for reasons beyond the control of the investigator, or for reasons external to the issue being studied.

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Word #37 – Joint Probability Distribution

The probability distribution for X is the possible values of X and their associated probabilities. With two separate discrete random variables, X and Y, the joint probability distribution is the function f(x,y) 

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Word #36 – The Jackknife

With a sample of size N, the jackknife involves calculating N values of the estimator, with each value calculated on the basis of the entire sample less one observation. 

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Word #35 – Interim Monitoring

In the interim monitoring of clinical trials, multiple looks are taken at the accruing patient results - say, response to a medication.

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Industry Spotlight: SAS is back

The big news from the SAS world this summer was the release, on May 28, of the SAS University Edition, which brings the effective price for a single user edition of SAS down from around $10,000 to $0. It does most of the things that…

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Twitter Sentiment vs. Survey Methods

Nobody expects Twitter feed sentiment analysis to give you unbiased results the way a well-designed survey will. A Pew Research study found that Twitter political opinion was, at times, much more liberal than that revealed by public opinion polls, while it was more conservative at…

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Internet of Things

Boston, August 3 2014: Bill Ruh, GE Software Center, says that the Internet of Things, 30 billion machines talking to one another, will dwarf the impact of the consumer internet. Speaking at the Joint Statistical Meetings today, Ruh predicted that the marriage of the IoT…

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Work #32 – Predictive modeling

Predictive modeling is the process of using a statistical or machine learning model to predict the value of a target variable (e.g. default or no-default) on the basis of a series of predictor variables (e.g. income, house value, outstanding debt, etc.).

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Word #31 – Hold-out sample

A hold-out sample is a random sample from a data set that is withheld and not used in the model fitting process.  After the model...

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Week #29 – Goodness-of-fit

Goodness-of-fit measures the difference between an observed frequency distribution and a theoretical probability distribution which

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Week #28 – Geometric Mean

The geometric mean of n values is determined by multiplying all n values together, then taking the nth root of the product. It is useful in taking averages of ratios.

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Week #27 – Hierarchical Linear Models

Hierarchical linear modeling is an approach to analysis of hierarchical (nested) data - i.e. data represented by categories, sub-categories, ..., individual units (e.g. school -> classroom -> student).

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Week #21 – Error

Error is the deviation of an estimated quantity from its true value, or, more precisely,

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Week #19 – Regularization

Regularization refers to a wide variety of techniques used to bring structure to statistical models in the face of data size, complexity and sparseness.

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Week #18 – SQL

SQL stands for structured query language, a high level language for querying relational databases, extracting information.

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Week #16 – MapReduce

MapReduce is a programming framework to distribute the computing load of very large data and problems to multiple computers.

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Week #15 – Hadoop

As data processing requirements grew beyond the capacities of even large computers, distributed computing systems were developed to spread the load to multiple computers.

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Week #13 – Data Product

A data product is a product or service whose value is derived from using algorithmic methods on data, and which in turn produces data to be used in the same product, or tangential data products.

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Week #12 – Dependent and Independent Variables

 

 

Statistical models normally specify how one set of variables, called dependent variables, functionally depend on another set of variables, called independent variables.

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Week #11 – Distance

Statistical distance is a measure calculated between two records that are typically part of a larger dataset, where rows are records and columns are variables.  To calculate...

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Week #10 – Decile Lift

In predictive modeling, the goal is to make predictions about outcomes on a case-by-case basis:  an insurance claim will be fraudulent or not, a tax return will be correct or in error, a subscriber...

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Week #9 – Decision Trees

In the machine learning community, a decision tree is a branching set of rules used to classify a record, or predict a continuous value for a record.  For example

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Week #8 – Feature Selection

In predictive modeling, feature selection, also called variable selection, is the process (usually automated) of sorting through variables to retain variables that are likely...

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Week #7 – Bagging

In predictive modeling, bagging is an ensemble method that uses bootstrap replicates of the original training data to fit predictive models.

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Week #6 – Boosting

In predictive modeling, boosting is an iterative ensemble method that starts out by applying a classification algorithm and generating classifications.

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Week #5 – Ensemble Methods

In predictive modeling, ensemble methods refer to the practice of taking multiple models and averaging their predictions.

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Dialects

When talking to several people, do you address them as "you guys"? "Y'all"? Just "you"? And is the carbonated soft drink "soda" or "pop?" Maps based on survey responses to questions like this were published in the Harvard Dialect Survey in 2003. Josh Katz took…

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Needle in a Haystack

What's the probability that the NSA examined the metadata for your phone number in 2013? According to John Inglis, Deputy Director at the NSA, it's about 0.00001, or 1 in 100,000. A surprisingly small number, given what we've all been reading in the media about…

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Week #3 – Exact Tests

Exact tests are hypothesis tests that are guaranteed to produce Type-I error at or below the nominal alpha level of the test when conducted on samples drawn from a null model.

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Week #2 – Error

In statistical models, error or residual is the deviation of the estimated quantity from its true value: the greater the deviation, the greater the error.

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Week #1 – Endogenous variable

Endogenous variables in causal modeling are the variables with causal links (arrows) leading to them from other variables in the model.

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Week #53 – Effect size

In a study or experiment with two groups (usually control and treatment), the investigator typically has in mind the magnitude of the difference between the two groups that he or she wants to be able to detect in a hypothesis test.

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Week #51 – Type 1 error

In a test of significance (also called a hypothesis test), Type I error is the error of rejecting the null hypothesis when it is true -- of saying an effect or event is statistically significant when it is not.

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Personality regions

There are Red States and Blue States. The three blue states of the Pacific coast constitute the Left Coast. For Colin Woodward, Yankeedom comprises both New England and the Great Lakes. If you're into accessories, there's the Bible Belt, the Rust Belt, and the Stroke…

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Week #49 – Data partitioning

Data partitioning in data mining is the division of the whole data available into two or three non-overlapping sets: the training set (used to fit the model), the validation set (used to compared models), and the test set (used to predict performance on new data).

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Week #46 – Cluster Analysis

In multivariate analysis, cluster analysis refers to methods used to divide up objects into similar groups, or, more precisely, groups whose members are all close to one another on various dimensions being measured.

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Week #45 – Construct validity

In psychology, a construct is a phenomenon or a variable in a model that is not directly observable or measurable  - intelligence is a classic example.

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Terrorist Clusters

The "righteous vengeance gun attack" is just one of 10 types of terrorism identified by Chenoweth and Lowham via statistical clustering techniques. Another cluster is "bombings of a public population where a liberation group takes responsibility." You can read about the 10 clusters, and the…

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Statistics.com Partners With CrowdANALYTIX to Offer New Online Course With Crowdsource Contest As Project

Crowdsourcing, using the power of the crowd to solve problems, has been used for many functions and tasks, including predictive modeling (like the 2009 Netflix Contest). Typically, problems are broadcast to an unknown group of statistical modelers on the Internet, and solutions are sought. Every…

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Week #42 – Cross-sectional data

Cross-sectional data refer to observations of many different individuals (subjects, objects) at a given time, each observation belonging to a different individual.  A simple...

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Week #41 – Tokenization

Tokenization is an initial step in natural language processing.  It involves breaking down a text into a series of basic units, typically words. For example...

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Week #40 – Natural Language

A natural language is what most people outside the field of computer science think of as just a language (Spanish, English, etc.). The term...

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Week # 39 – White Hat Bias

White Hat Bias is bias leading to distortion in, or selective presentation of, data that is considered by investigators or reviewers to be acceptable because it is in the service of righteous goals.

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Week #35 – Continuous vs. Discrete Distributions

A discrete distribution is one in which the data can only take on certain values, for example integers.  A continuous distribution is one in which data can take on any value within a specified range (which may be infinite).

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Week # 34 – Central Limit Theorem

The central limit theorem states that the sampling distribution of the mean approaches Normality as the sample size increases, regardless of the probability distribution of the population from which the sample is drawn.

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Week #32 – CHAID

CHAID stands for Chi-squared Automatic Interaction Detector. It is a method for building classification trees and regression trees from a training sample comprising already-classified objects.

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Week # 31 – Census

In a census survey , all units from the population of interest are analyzed. A related concept is the sample survey, in which only a subset of the population is taken.

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Week #30 – Discriminant analysis

Discriminant analysis is a method of distinguishing between classes of objects.  The objects are typically represented as rows in a matrix.

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Week # 29 – Training data

Also called the training sample, training set, calibration sample.  The context is predictive modeling (also called supervised data mining) -  where you have data with multiple predictor variables and a single known outcome or target variable.

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Mutual Attraction

Mutual attraction is a dominant force in the universe. Gravity binds the moon to the earth, the earth to the sun, the sun to the galaxy, and one galaxy to another. And yet the universe is expanding; the result is a larger universe comprised of…

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Week #28 – Bias

A general statistical term meaning a systematic (not random) deviation of an estimate from the true value.

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