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Related Concept Videos

Prediction Intervals01:03

Prediction Intervals

The interval estimate of any variable is known as the prediction interval. It helps decide if a point estimate is dependable.
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y. 
The...
Determination of Expected Frequency01:08

Determination of Expected Frequency

Suppose one wants to test independence between the two variables of a contingency table. The values in the table constitute the observed frequencies of the dataset. But how does one determine the expected frequency of the dataset? One of the important assumptions is that the two variables are independent, which means the variables do not influence each other. For independent variables, the statistical probability of any event involving both variables is calculated by multiplying the individual...
Residuals and Least-Squares Property01:11

Residuals and Least-Squares Property

The vertical distance between the actual value of y and the estimated value of y. In other words, it measures the vertical distance between the actual data point and the predicted point on the line
If the observed data point lies above the line, the residual is positive, and the line underestimates the actual data value for y. If the observed data point lies below the line, the residual is negative, and the line overestimates the actual data value for y.
The process of fitting the best-fit...
Probability Histograms01:17

Probability Histograms

A probability histogram is a visual representation of a probability distribution. Similar a typical histogram, the probability histogram consists of contiguous (adjoining) boxes. It has both a horizontal axis and a vertical axis. The horizontal axis is labeled with what the data represents. The vertical axis is labeled with probability. Each rectangular bar in the histogram is 1 unit wide, which suggests that the area under each bar equals the probability, P(x), where x is 1, 2, 3, and so on.
Expected Frequencies in Goodness-of-Fit Tests01:19

Expected Frequencies in Goodness-of-Fit Tests

A goodness-of-fit test is conducted to determine whether the observed frequency values are statistically similar to the frequencies expected for the dataset. Suppose the expected frequencies for a dataset are equal such as when predicting the frequency of any number appearing when casting a die. In that case, the expected frequency is the ratio of the total number of observations (n) to the number of categories (k).
Noncompartmental Analysis: Mean Residence Time01:05

Noncompartmental Analysis: Mean Residence Time

According to statistical moment theory, mean residence time (MRT) is an important measure in pharmacokinetics. MRT can be defined as the expected mean of a probability density function distribution. It provides valuable insights into drug disposition in the body.
After the administration of a drug through intravenous bolus injection, the drug molecules are distributed throughout the body and remain there for varying periods. The MRT represents the average time these drug molecules stay in the...

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Related Experiment Video

Updated: May 12, 2026

Using a Virtual Store As a Research Tool to Investigate Consumer In-store Behavior
09:17

Using a Virtual Store As a Research Tool to Investigate Consumer In-store Behavior

Published on: July 24, 2017

The predictability of consumer visitation patterns.

Coco Krumme1, Alejandro Llorente, Manuel Cebrian

  • 1Media Laboratory, Massachusetts Institute of Technology, Cambridge, MA 02139, USA. coco@media.mit.edu

Scientific Reports
|April 20, 2013
PubMed
Summary
This summary is machine-generated.

Consumer shopping behavior is surprisingly predictable over the long term, despite individual preferences. While short-term choices have random elements, long-term patterns reveal consistent merchant visitation regularities across populations.

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Last Updated: May 12, 2026

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Published on: July 24, 2017

Trajectory Data Analyses for Pedestrian Space-time Activity Study
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Published on: May 24, 2019

Area of Science:

  • Consumer behavior analysis
  • Economic transaction patterns
  • Predictive modeling in economics

Background:

  • Understanding consumer behavior is crucial for economic modeling.
  • Individual shopping choices appear elective but may follow underlying patterns.
  • Previous research often focused on aggregate trends, overlooking individual predictability.

Purpose of the Study:

  • To investigate the predictability of consumer merchant visitation patterns.
  • To identify regularities in individual and aggregate shopping behavior over time.
  • To assess the accuracy of predictive models, including Markov models, for consumer movements.

Main Methods:

  • Analysis of hundreds of thousands of individual economic transactions.
  • Application of Markov models to predict next location based on visitation history.
  • Incorporation of population-level transition probabilities into predictive models.

Main Results:

  • Consumer merchant visitation patterns exhibit significant predictability in the long run.
  • Individual preferences vary, but shoppers share common regularities in visiting merchants.
  • Aggregate behavior is predictable, but short-term shopping event interleaving introduces stochasticity.
  • Markov models, especially with population-level data, can improve prediction accuracy.

Conclusions:

  • Long-term consumer activity, despite seeming elective, is highly predictable.
  • While precise short-term predictions are elusive, population-level regularities exist at larger time scales.
  • The study provides insights into the theoretical upper bounds of predictability in consumer behavior.