Extreme value analysis of the number of student absences in Jiangsu, China: Based on extreme value theory
These videos have been matched automatically. Contact us if they are not relevant.
These videos have been matched automatically. Contact us if they are not relevant.
View abstract on PubMed
Summary
This summary is machine-generated.Extreme Value Theory (EVT) can predict student absences, offering early warnings for public health crises. This helps school health professionals implement preventative measures against excessive student absenteeism.
Area Of Science
- Public Health
- Educational Psychology
- Biostatistics
Background
- Student absences significantly impact physical, mental health, and academic achievement.
- Excessive absenteeism can signal emerging public health crises, like infectious disease outbreaks.
- Identifying and mitigating causes of student absences is crucial for student well-being.
Purpose Of The Study
- To predict extreme instances of student absences using statistical modeling.
- To apply Extreme Value Theory (EVT) to analyze high-value absence data.
- To provide school health professionals with predictive tools for intervention.
Main Methods
- Application of Extreme Value Theory (EVT) to analyze student absence data.
- Modeling extreme values in absence data using five distinct statistical distributions.
- Statistical analysis focused on characterizing the distribution of extreme absence events.
Main Results
- Extreme Value Theory (EVT) demonstrates utility in predicting extreme student absences.
- The study successfully characterized extreme absence values using selected statistical distributions.
- Predicted results can inform preventative strategies for public health.
Conclusions
- EVT is a valuable tool for forecasting extreme student absenteeism.
- Predictive insights from EVT can support proactive public health interventions in schools.
- Minimizing excessive absences through early detection aids student health and academic success.
These videos have been matched automatically. Contact us if they are not relevant.
These videos have been matched automatically. Contact us if they are not relevant.
Related Concept Videos
An outlier is an observation of data that does not fit the rest of the data. It is sometimes called an extreme value. When you graph an outlier, it will appear not to fit the pattern of the graph. Some outliers are due to mistakes (for example, writing down 50 instead of 500), while others may indicate that something unusual is happening. Outliers are present far from the least squares line in the vertical direction. They have large "errors," where the "error" or residual is the...
Outliers are observed data points that are far from the least squares line. They have unusual values and need to be examined carefully. Though an outlier may result from erroneous data, at other times, it may hold valuable information about the population under study and should be included in the data. Hence, it is crucial to examine what causes a data point to be an outlier.
The z score is used to find outliers or unusual values. It should be noted that any values beyond -2 and +2 are...
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...
Unusual results are those that have a very low chance of occurring. Unusual results can be identified using probabilities and the range rule of thumb. In problems involving probability, unusual results can be observed in 2 instances – an unusually high number of successes or an unusually low number of successes.
According to the range rule of thumb, any value above or below two standard deviations, 2σ from the mean, μ is considered unusual.
Maximum unusual value =...
When one or more data points appear far from the rest of the data, there is a need to determine whether they are outliers and whether they should be eliminated from the data set to ensure an accurate representation of the measured value. In many cases, outliers arise from gross errors (or human errors) and do not accurately reflect the underlying phenomenon. In some cases, however, these apparent outliers reflect true phenomenological differences. In these cases, we can use statistical methods...
Sometimes, a data set can have a recorded numerical observation that greatly deviates from the rest of the data. Assuming that the data is normally distributed, a statistical method called the Grubbs test can be used to determine whether the observation is truly an outlier. To perform a two-tailed Grubbs test, first, calculate the absolute difference between the outlier and the mean. Then, calculate the ratio between this difference and the standard deviation of the sample. This...