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Degrees of Freedom

Degrees of Freedom

The degree of freedom for a particular statistical calculation is the number of values that are free to vary. As a result, the minimum number of independent numbers can specify a particular statistic. The degrees of freedom differ greatly depending on known and uncalculated statistical components.
For example, suppose there are three unknown numbers whose mean is 10; although we can freely assign values to the first and second numbers, the value of the last number can not be arbitrarily...

Degrees of Freedom

Degrees of Freedom

The degree of freedom for a particular statistical calculation is the number of values that are free to vary. Thus, the minimum number of independent numbers can specify a particular statistic. The degrees of freedom differ greatly depending on known and uncalculated statistical components.
For example, suppose there are three unknown numbers whose mean is 10; although we can freely assign values to the first and second numbers, the value of the last number can not be arbitrarily assigned.

Degree of Unsaturation

Degree of Unsaturation

The degree of unsaturation (U), or index of hydrogen deficiency (IHD), is defined as the difference in the number of pairs of hydrogen atoms between the compound and the acyclic alkane with the same number of carbon atoms. Each double bond or ring costs two hydrogen atoms compared to a saturated analog and results in one degree of unsaturation.
The degree of unsaturation for hydrocarbons is U = (2C + 2 − H) / 2, where C is the number of carbon atoms and H is the number of hydrogen atoms.

One-Degree-of-Freedom System

One-Degree-of-Freedom System

In mechanical engineering, one-degree-of-freedom systems form the basis of a wide range of electrical and mechanical components. Using these models, engineers can predict the behavior of various parts in a larger system, which gives them insight into how different forces interact with each other.
A one-degree-of-freedom system is defined by an independent variable that determines its state and behavior. One example of a one-degree-of-freedom system is a simple harmonic oscillator, such as a...

Student t Distribution

Student t Distribution

The population standard deviation is rarely known in many day-to-day examples of statistics. When the sample sizes are large, it is easy to estimate the population standard deviation using a confidence interval, which provides results close enough to the original value. However, statisticians ran into problems when the sample size was small. A small sample size caused inaccuracies in the confidence interval.
The Student t distribution was developed by William S. Goset (1876–1937) of the...

Microsoft Excel: Student's t-Test

Microsoft Excel: Student's t-Test

Student's t-test in Microsoft Excel is a statistical method used to compare the means of two groups to determine if they are significantly different from each other. It's commonly used to evaluate hypotheses, such as testing whether a treatment has an effect compared to a control group. Excel provides built-in functions to perform t-tests, making it accessible for users needing to conduct basic statistical analysis.
To conduct a t-test in Excel, use the T.TEST function or the "Data...

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Updated: Jan 27, 2026

Using Learning Outcome Measures to assess Doctoral Nursing Education

Using Learning Outcome Measures to assess Doctoral Nursing Education

Published on: June 21, 2010

Variables Impacting Program Completion of Readmitted Associate Degree Nursing Students.

Kelly J Dries¹

¹Author Affiliation: Dean, School of Health Sciences, Milwaukee Area Technical College, Milwaukee, WI.

|March 14, 2019

Summary

This summary is machine-generated.

Readmitted nursing students who achieve a B+ or higher in fundamentals are more likely to complete their program. Older students and those with lower grades face higher attrition risks.

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Published on: September 11, 2021

Area of Science:

Nursing Education
Higher Education Studies
Student Retention

Background:

Projected Registered Nurse (RN) shortages necessitate identifying factors influencing nursing student program completion.
Limited research exists on variables affecting the attrition and completion rates of readmitted nursing students.

Purpose of the Study:

To investigate academic and nonacademic factors influencing the attrition and program completion of readmitted associate degree nursing students.

Main Methods:

Utilized survey data and record reviews to collect information on student characteristics, support systems, effort, and outcomes.
Analyzed data to identify predictors of attrition and program completion.

Main Results:

Achieving a final grade of B+ or higher in the nursing fundamentals course was associated with successful program completion.
Readmitted students aged 33 and older experienced a 1.44% decrease in program completion odds for each additional year of age.
Identified significant nonacademic variables contributing to student attrition.

Conclusions:

The study provides insights into improving program completion rates for readmitted nursing students.
Recommendations for interventions to support at-risk readmitted students are suggested.