In this paper we discuss the basic procedures for the implementation of multivariate statistical process control via control charting. Furthermore, we review multivariate extensions for all kinds of univariate control charts, such as multivariate Shewhart-type control charts, multivariate CUSUM control charts and multivariate EWMA control charts. In addition, we review unique procedures for the construction of multivariate control charts, based on multivariate statistical techniques such as principal components analysis (PCA) and partial lest squares (PLS). Finally, we describe the most significant methods for the interpretation of an out-of-control signal.

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Multivariate statistical process control charts: an overview

Multivariate Statistical Process Control Charts: An Overview

Significant measurement error often exists in quality control applications. Measurement error is known to result in reduced power to detect a given change in the mean or variance of a quality chara...

Effect of Measurement Error on Shewhart Control Charts

Understanding tablet computer usage among primary school students in underdeveloped areas

The inference about the statistical properties of quality control methodologies is based on the assumptions of normality and independence. In real industrial environments though process data is often correlated or exhibits some serial dependence affecting the efficiency of Statistical Process Control (SPC) methodologies. New technology gives managers the option of using more sophisticated SPC models which more accurately reflect the process being monitored, by relaxing some of the assumptions. The aim of this paper is to present, to apply and to evaluate control charts that are designed to account for autocorrelation.

SPC Procedures for Monitoring Autocorrelated Processes

A model demonstrates that the performance of multivariate control charting methods based on measured covariates is not directionally invariant to shifts in the mean vector of the underlying process variables. This is the case even if it is directionally..

The Performance of Multivariate Control Charts in the Presence of Measurement Error

Online instructional ratings are taken by many with a grain of salt. This study analyzes the ability of said ratings to estimate the official (university-administered) instructional ratings of the same respective university instructors. Given self-selection among raters, we further test whether more online ratings of instructors lead to better prediction of official ratings in terms of both R-squared value and root mean squared error. We lastly test and correct for heteroskedastic error terms in the regression analysis to allow for the first robust estimations on the topic. Despite having a starkly different distribution of values, online ratings explain much of the variation in official ratings. This conclusion strengthens, and root mean squared error typically falls, as one considers regression subsets over which instructors have a larger number of online ratings. Though (public) online ratings do not mimic the results of (semi-private) official ratings, they provide a reliable source of information for predicting official ratings. There is strong evidence that this reliability increases in online rating usage.

/pdf/do-more-online-instructional-ratings-lead-to-better-3weayuz6ji.pdf

Do more online instructional ratings lead to better prediction of instructor quality

Clock rule changes were introduced in the 2006 season with the goal of reducing the average duration of the game; these changes were reversed in 2007. In addition, in 2007 the kickoff rule was changed to create more excitement and potentially more scoring. We examine what happened to actual and expected scoring during these National Collegiate Athletic Association (NCAA) football seasons. The clock rule change in 2006 led to lower scoring which was not fully encompassed in the betting market, leading to significant returns to betting the under. Multiple rule changes in 2007 led to volatility in the betting market that subsided by season's end.

https://www.econstor.eu/bitstream/10419/103573/1/789994895.pdf

The Effects of the Clock and Kickoff Rule Changes on Actual and Market-Based Expected Scoring in NCAA Football

/pdf/improving-shewhart-control-chart-performance-in-the-presence-12o6n3mzyw.pdf

Kenneth W. Linna

Papers

Effect of Measurement Error on Shewhart Control Charts

The Performance of Multivariate Control Charts in the Presence of Measurement Error

Do more online instructional ratings lead to better prediction of instructor quality

The Effects of the Clock and Kickoff Rule Changes on Actual and Market-Based Expected Scoring in NCAA Football

Improving Shewhart Control Chart Performance in the Presence of Measurement Error Using Multiple Measurements and Two-Stage Sampling