This paper reports on the Failure To Attend (FTA) rate of appointments as well as patients following the implementation of SMS reminders in a public dental outpatient service.  Given the ineffectiveness of the intervention and a highly representative patient’s profile, this paper identifies the demographic characteristics of patients who miss all of their appointments.  Data on appointment attendance, patient demographics and dental service type was collected over a time period of 46 consecutive months.  Using descriptive and inferential statistics (chi-square, two sample tests and Marascuilo procedure) we found the SMS intervention was ineffective in reducing the FTA rates. Further, patients associated with high rates of non-attendance exhibited one or more of the following characteristics: male; age 26 – 44; non-concession card holders; a person of Indigenous, local, Asian or African descent, and of refugee status, persons living in low socio-economic areas; and appointments in General Care and Student Clinics. Whilst the literature overwhelmingly attributes SMS reminders to improving the attendance rate of patients in outpatient clinics, our contradictory findings suggest a more targeted approach in settings whose patients exhibit strong characteristics associated with non-attendance.

This paper presents a new multivariate process capability index (MPCI) which is based on the principal component analysis (PCA) and is dependent on a parameter β which can take on any real number. This MPCI generalises some existing multivariate indices based on PCA proposed by several authors when β = 0 or 1. One of the key contributions of this paper is to show that there is a direct correspondence between this MPCI and process yield for a unique value of β. This result is used to establish a relationship between the capability status of the process and to show that under some mild conditions, the estimators of this MPCI is consistent and converge to a normal distribution. This is then applied to perform tests of statistical hypotheses and in determining sample sizes. Several numerical examples are presented with the objective of illustrating the procedures and demonstrating how they can be applied to determine the viability and capacity of different manufacturing processes.

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In nonparametric statistics the functional form of the relationship between the response variable and its associated predictor variables is unspecified but it is assumed to be a smooth function. We develop a procedure for constructing a fixed width confidence interval for the predicted value at a specified point of the independent variable. The optimal sample size for constructing this interval is obtained using a two stage sequential procedure which relies on some asymptotic properties of the Nadaraya-Watson and local linear estimators. Finally, a large scale simulation study demonstrates the applicability of the developed procedure for small and moderate sample sizes. The procedure developed here should find wide applicability since many practical problems which arise in industry involve estimating an unknown function.

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