Related Experiment Video

Updated: Aug 14, 2026

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

Robustness of 5qx estimators under nonstationarity

D P Smith

    Demography
    |November 1, 1984
    PubMed

    More Related Videos

    Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach04:35

    Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

    Published on: July 3, 2020

    The Innovation Arena: A Method for Comparing Innovative Problem-Solving Across Groups14:14

    The Innovation Arena: A Method for Comparing Innovative Problem-Solving Across Groups

    Published on: May 13, 2022

    Related Concept Videos

    Estimating Population Mean with Known Standard Deviation01:16

    Estimating Population Mean with Known Standard Deviation

    To construct a confidence interval for a single unknown population mean μ, where the population standard deviation is known, we need sample mean as an estimate for μ and we need the margin of error. Here, the margin of error (EBM) is called the error bound for a population mean (abbreviated EBM). The sample mean is the point estimate of the unknown population mean μ.
    The confidence interval estimate will have the form as follows:
    (point estimate - error bound, point estimate + error bound)
    The...
    Distributions to Estimate Population Parameter01:26

    Distributions to Estimate Population Parameter

    The accurate values of population parameters such as population proportion, population mean, and population standard deviation (or variance) are usually unknown. These are fixed values that can only be estimated from the data collected from the samples. The estimates of each of these parameters are sample proportion, the sample mean, and sample standard deviation (or variance). To obtain the values of these sample statistics, data are required that have particular distribution and central...
    Estimating Population Standard Deviation01:26

    Estimating Population Standard Deviation

    When the population standard deviation is unknown and the sample size is large, the sample standard deviation s is commonly used as a point estimate of σ. However, it can sometimes under or overestimate the population standard deviation. To overcome this drawback, confidence intervals are determined to estimate population parameters and eliminate any calculation bias accurately. However, this only applies to random samples from normally distributed populations. Knowing the sample mean and...
    Expected Frequencies in Goodness-of-Fit Tests01:19

    Expected Frequencies in Goodness-of-Fit Tests

    A goodness-of-fit test is conducted to determine whether the observed frequency values are statistically similar to the frequencies expected for the dataset. Suppose the expected frequencies for a dataset are equal such as when predicting the frequency of any number appearing when casting a die. In that case, the expected frequency is the ratio of the total number of observations (n) to the number of categories (k).
    Estimating Population Mean with Unknown Standard Deviation01:22

    Estimating Population Mean with Unknown Standard Deviation

    In practice, we rarely know the population standard deviation. In the past, when the sample size was large, this did not present a problem to statisticians. They used the sample standard deviation s as an estimate for σ and proceeded as before to calculate a confidence interval with close enough results. However, statisticians ran into problems when the sample size was small. A small sample size caused inaccuracies in the confidence interval.
    William S. Gosset (1876–1937) of the Guinness...
    Parametric Survival Analysis: Weibull and Exponential Methods01:14

    Parametric Survival Analysis: Weibull and Exponential Methods

    Parametric survival analysis models survival data by assuming a specific probability distribution for the time until an event occurs. The Weibull and exponential distributions are two of the most commonly used methods in this context, due to their versatility and relatively straightforward application.
    Weibull Distribution
    The Weibull distribution is a flexible model used in parametric survival analysis. It can handle both increasing and decreasing hazard rates, depending on its shape parameter...