Related Experiment Video
Updated: Sep 29, 2026
04:35
Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
Published on: July 3, 2020
[A further test of a multivariate forecasting model for fertility]
Maandstatistiek Van De Bevolking (Hague, Netherlands : 1982)
|June 1, 1987
Abstract:
A model developed by J. Nelissen and A. Vossen, for making short-term population projections, is first described. In this model, the fertility rate is related to the unemployment rate and the fraction of the labor force employed in agriculture. The model is then applied to Dutch data for the period 1980-1985. "The main conclusions are that the results depend heavily on the choice of the forecasting period and that the ex-post forecasts of Nelissen and Vossen's model are out-performed by univariate ARIMA-projections." (SUMMARY IN ENG)
Keywords:
Agricultural WorkersBirth RateComparative StudiesCritiqueDemographic FactorsDeveloped CountriesEconomic FactorsEmploymentEstimation TechnicsEuropeFertilityFertility MeasurementsFertility RateHuman ResourcesLabor ForceMacroeconomic FactorsModels, TheoreticalNetherlandsOccupationsPopulationPopulation DynamicsPopulation ForecastPopulation ProjectionResearch MethodologyUnemploymentWestern Europe
Related Concept Videos
01:25Multiple Regression
Multiple regression assesses a linear relationship between one response or dependent variable and two or more independent variables. It has many practical applications.
Farmers can use multiple regression to determine the crop yield based on more than one factor, such as water availability, fertilizer, soil properties, etc. Here, the crop yield is the response or dependent variable as it depends on the other independent variables. The analysis requires the construction of a scatter plot...
Farmers can use multiple regression to determine the crop yield based on more than one factor, such as water availability, fertilizer, soil properties, etc. Here, the crop yield is the response or dependent variable as it depends on the other independent variables. The analysis requires the construction of a scatter plot...
01:08Fisher's Exact Test
Fisher's exact test is a statistical significance test widely used to analyze 2x2 contingency tables, particularly in situations where sample sizes are small. Unlike the chi-squared test, which approximates P-values and assumes minimum expected frequencies of at least five in each cell, Fisher's exact test calculates the exact probability (P-value) of observing the data or more extreme results under the null hypothesis. This feature makes it especially valuable when the assumptions of the...
01:19Expected 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).
01:52Regression Toward the Mean
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...
01:16Goodness-of-Fit Test
The goodness-of-fit test is a type of hypothesis test which determines whether the data "fits" a particular distribution. For example, one may suspect that some anonymous data may fit a binomial distribution. A chi-square test (meaning the distribution for the hypothesis test is chi-square) can be used to determine if there is a fit. The null and alternative hypotheses may be written in sentences or stated as equations or inequalities. The test statistic for a goodness-of-fit test is given as...
01:11Regression Analysis
Regression analysis is a statistical tool that describes a mathematical relationship between a dependent variable and one or more independent variables.
In regression analysis, a regression equation is determined based on the line of best fit– a line that best fits the data points plotted in a graph. This line is also called the regression line. The algebraic equation for the regression line is called the regression equation. It is represented as:
In regression analysis, a regression equation is determined based on the line of best fit– a line that best fits the data points plotted in a graph. This line is also called the regression line. The algebraic equation for the regression line is called the regression equation. It is represented as: