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Related Concept Videos

Multiple Regression01:25

Multiple Regression

Multiple regression assesses a linear relationship between one response or dependent variable and two or more independent variables. It has many practical applications.
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Analysis of population pharmacokinetic data involves studying the behavior of drugs within diverse populations to understand their pharmacokinetic parameters. Traditional pharmacokinetic methods typically involve collecting samples from a few individuals and estimating these parameters. While these methods are commonly used, they have limitations in capturing the variability in drug response among individuals or heterogeneous populations. Population pharmacokinetics is employed to address these...
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Regression toward the mean (“RTM”) is a phenomenon in which extremely high or low values—for example, and individual’s blood pressure at a particular moment—appear closer to a group’s average upon remeasuring. Although this statistical peculiarity is the result of random error and chance, it has been problematic across various medical, scientific, financial and psychological applications. In particular, RTM, if not taken into account, can interfere when researchers try to extrapolate results...
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Related Experiment Video

Updated: May 16, 2026

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
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Using Regression Analysis to Find Predictors of Performance in a Pharmacotherapy Course.

Beth Janetski1, Casey E Gallimore1

  • 1University of Wisconsin-Madison, School of Pharmacy, Madison, WI, USA.

American Journal of Pharmaceutical Education
|May 14, 2026
PubMed
Summary
This summary is machine-generated.

Student success in pharmacy programs depends on both in-class performance and external factors. A study found that a sense of belonging and prior GPA positively impact grades, while early intervention policies and office hour visits negatively correlate with final course outcomes.

Keywords:
Academic performanceCourse-level outcomesEarly warning and interventionPharmacy educationStudent success

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A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
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12:18

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

Area of Science:

  • Pharmacy Education
  • Academic Performance Analysis

Background:

  • Understanding factors influencing student success is crucial in Doctor of Pharmacy (PharmD) programs.
  • Identifying both internal and external variables can inform pedagogical strategies and student support.

Purpose of the Study:

  • To examine the relationship between classroom-related and external variables and final course outcomes in a PharmD program.
  • To utilize exploratory linear regression analysis to identify significant predictors of academic success.

Main Methods:

  • Data were collected from students in a required Pharmacotherapy I course.
  • An exploratory linear regression model incorporated eight variables from admissions data, learning analytics, and student surveys.
  • A dataset from 57 out of 98 students (58%) was analyzed.

Main Results:

  • Final course grade positively correlated with sense of belonging (r=.544), matriculation GPA (r=.537), co-curricular involvement (r=.421), and practice quiz scores (r=.214).
  • Final course grade negatively correlated with triggering an Early Warning and Intervention (EWI) policy (r=-.652), progression committee attendance (r=-.553), attending office hours for exam study (r=-.401), and using recorded lectures (r=-.377).
  • The regression model was statistically significant (F(8, 48)=16.763, p<.001).

Conclusions:

  • A combination of internal and external factors significantly influences final course grades in pharmacy education.
  • Sense of belonging, prior academic achievement, and engagement levels are key positive predictors.
  • Intervention policies and specific study habits may indicate challenges impacting course outcomes.