Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Steps in Outbreak Investigation01:18

Steps in Outbreak Investigation

651
In the ever-evolving field of public health, statistical analysis serves as a cornerstone for understanding and managing disease outbreaks. By leveraging various statistical tools, health professionals can predict potential outbreaks, analyze ongoing situations, and devise effective responses to mitigate impact. For that to happen, there are a few possible stages of the analysis:
651
Overview of Microsoft Excel as a Data Analysis Tool01:13

Overview of Microsoft Excel as a Data Analysis Tool

1.8K
Microsoft Excel is a cornerstone tool for data analysis and statistical operations, offering a wide array of functionalities to manage, analyze, and visualize data efficiently. Recognized for its versatility, Excel facilitates the performance of basic to complex statistical operations, serving as an indispensable asset for analysts, researchers, and students alike. Excel's significance in data analysis emanates from its spreadsheet environment, where data can be organized in rows and...
1.8K
Microsoft Excel: Regression Analysis01:18

Microsoft Excel: Regression Analysis

1.6K
Regression analysis in Microsoft Excel is a powerful statistical method for examining the relationship between a dependent variable and one or more independent variables. It's used extensively in fields such as economics, biology, and business to predict outcomes, understand relationships, and make data-driven decisions. The most common type is linear regression, which attempts to fit a straight line through the data points to model the relationship between variables.
To perform regression...
1.6K
Microsoft Excel: Median, Quartile range, and Box Plots01:29

Microsoft Excel: Median, Quartile range, and Box Plots

3.7K
In Microsoft Excel, calculating the median, interquartile range, and creating box plots can help understand the distribution of your data.
Median and Quartile Range: The median is calculated using the formula `=MEDIAN(range)', which provides the middle value of your data set. Quartiles divide your data into four equal parts. To find the first and third quartiles, use ‘=QUARTILE(range, 1)' and ‘=QUARTILE(range, 3)', respectively. The interquartile range (IQR), which...
3.7K
Microsoft Excel: Plotting Mean, SD, and SE01:18

Microsoft Excel: Plotting Mean, SD, and SE

1.4K
In Microsoft Excel, plotting the mean along with standard deviation (SD) and standard error (SE) helps visualize data variability and reliability. To plot these values, follow these steps:
First, calculate the mean, SD, and SE of your data. The mean is obtained using the formula `=AVERAGE(range)`, while SD can be calculated with `=STDEV.P(range)` for a population or `=STDEV.S(range)` for a sample. SE is calculated as `=SD/SQRT(n)`, where `n` is the sample size.
To plot these values, use a bar...
1.4K
Performing a Simple Data Analysis using MS-Excel Function01:17

Performing a Simple Data Analysis using MS-Excel Function

1.1K
Microsoft Excel offers a suite of functions and tools ideal for statistical analysis, making it accessible to students and researchers. This article outlines fundamental Excel functions pivotal for data analysis.
SUM: This function calculates the total sum of a range of values. It's the foundation for aggregating data, essential for determining overall trends and totals in datasets.
AVERAGE: It computes the mean value of a given set of numbers, providing a quick insight into the central...
1.1K

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Getting It Right the Second Time: How Can we Optimize First-Generation Cephalosporin Dosing for Skin and Soft Tissue Infections in the 21st Century?

Pharmacotherapy·2026
Same author

Toward the Future: A Race-Agnostic, Bayesian Approach to Defining Glomerular Filtration for Personalized Drug Dosing.

Clinical pharmacology and therapeutics·2026
Same author

Defining Optimal Sampling Strategies for Cefepime Model-Informed Precision Dosing.

Therapeutic drug monitoring·2026
Same author

Cefepime pharmacokinetics in critically ill children with multiple organ dysfunction syndrome using volumetric absorptive microsampling.

Antimicrobial agents and chemotherapy·2026
Same author

Population pharmacokinetics of polymyxin B: an individual participant data meta-analysis.

Clinical microbiology and infection : the official publication of the European Society of Clinical Microbiology and Infectious Diseases·2026
Same author

Bayesian Dosing Simulator (BDS): A Pharmacokinetic Modeling Tool for Optimized Antibiotic Therapy.

Pharmacotherapy·2026

Related Experiment Video

Updated: Mar 3, 2026

A Data-Driven Approach to Quantifying Immune States in Sepsis
07:42

A Data-Driven Approach to Quantifying Immune States in Sepsis

Published on: February 7, 2025

559

A Simple Microsoft Excel Method to Predict Antibiotic Outbreaks and Underutilization.

Cristina Miglis1, Nathaniel J Rhodes1, Sean N Avedissian1

  • 11Department of Pharmacy Practice,Midwestern University Chicago College of Pharmacy,Downers Grove,Illinois.

Infection Control and Hospital Epidemiology
|May 4, 2017
PubMed
Summary
This summary is machine-generated.

This study introduces a simple Excel-based method for antibiotic stewardship. It helps hospitals track antibiotic use over time to identify and address overuse or underuse.

More Related Videos

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data
10:46

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data

Published on: December 9, 2015

11.2K
Application of the Intelligent High-Throughput Antimicrobial Sensitivity Testing/Phage Screening System and Lar Index of Antimicrobial Resistance
09:59

Application of the Intelligent High-Throughput Antimicrobial Sensitivity Testing/Phage Screening System and Lar Index of Antimicrobial Resistance

Published on: July 21, 2023

1.9K

Related Experiment Videos

Last Updated: Mar 3, 2026

A Data-Driven Approach to Quantifying Immune States in Sepsis
07:42

A Data-Driven Approach to Quantifying Immune States in Sepsis

Published on: February 7, 2025

559
A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data
10:46

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data

Published on: December 9, 2015

11.2K
Application of the Intelligent High-Throughput Antimicrobial Sensitivity Testing/Phage Screening System and Lar Index of Antimicrobial Resistance
09:59

Application of the Intelligent High-Throughput Antimicrobial Sensitivity Testing/Phage Screening System and Lar Index of Antimicrobial Resistance

Published on: July 21, 2023

1.9K

Area of Science:

  • Healthcare Management
  • Infectious Disease Epidemiology
  • Antimicrobial Stewardship

Background:

  • Appropriate antibiotic use is crucial for combating antimicrobial resistance.
  • Effective benchmarking strategies are needed to guide antibiotic stewardship programs.
  • Current methods for tracking antibiotic consumption can be complex.

Purpose of the Study:

  • To present an easily implementable, regressive method for benchmarking antibiotic consumption.
  • To develop predictive indices for monitoring antibiotic use at the hospital level.
  • To identify temporal patterns of antibiotic overuse and underuse.

Main Methods:

  • Adaptation of a simple regressive analysis technique.
  • Utilizing Microsoft Excel for data analysis and visualization.
  • Trending antibiotic consumption data over specific time periods.

Main Results:

  • The developed method effectively creates predictive indices for antibiotic use.
  • The approach successfully trends consumption patterns over time.
  • Periods of potential antibiotic over- and underuse were identified at the hospital level.

Conclusions:

  • This Excel-based regressive method offers a practical tool for antibiotic stewardship.
  • The strategy facilitates the identification of deviations in antibiotic consumption.
  • Implementation can support targeted interventions to optimize antibiotic prescribing practices.