このページは機械翻訳されています。他のページは英語で表示される場合があります。 View in English

変数rを用いた組織人口学的指標のポイント推定

  • 0Population Studies Center, University of Pennsylvania, 3718 Locust Walk, Philadelphia, PA, USA.

|

|

まとめ

この要約は機械生成です。

2つの調査と雇用データを用いて雇用期間を推定する. このアプローチは,労働市場分析に不可欠な,期待される職務期間と職務分離の洞察を提供します.

科学分野

  • 労働経済学
  • 人口統計
  • 社会学

背景

  • 人口統計学,経済学,社会学において 雇用形態の分布は不可欠です
  • 雇用期間は人口研究における年齢と同等である.
  • 人口統計学者は生活表モデルに 変数rのような間接的な方法を用いる.

研究 の 目的

  • 職歴表のパラメータを推定するための変数-r方法を適応させる.
  • 予想される雇用期間と関連する労働市場対策
  • 職種分離のダイナミクスを分析する.

主な方法

  • 従業員の任期と調査間雇用の2つの遡及調査を使用しています.
  • 期間表のパラメータの見積もりには変数-rの方法を使用します.
  • 多重減期と原因削除の任期表を用いて,職種分離を分析する.

主要な成果

  • 変数rの方法は,合理的なパラメータの見積もりをもたらしました.
  • 雇用期間の予測は2.48年 (2002年から2004年までの割合) と見積もられている.
  • マルチプル・デクリメント・メソッドは 転職とその影響を定量化します

結論

  • 変数rの方法は,就職期間分析に有効です.
  • 職分別表は,職分別化の原因と結果についての洞察を提供します.
  • この研究は,公式の人口統計とビジネス統計を統合しています.

関連する概念動画

Variability: Analysis 01:11

189

Measures of variability are statistical metrics that reveal the dispersion pattern within a dataset. They are pivotal in biostatistics, providing insights into the heterogeneity within health and biological data. Variability signifies the degree to which data points diverge from one another, helping researchers understand the potential range of values and associated uncertainty within the data.
The range is a simple measure of variability, indicating the difference between the highest and...

Friedman Two-way Analysis of Variance by Ranks 01:21

295

Friedman's Two-Way Analysis of Variance by Ranks is a nonparametric test designed to identify differences across multiple test attempts when traditional assumptions of normality and equal variances do not apply. Unlike conventional ANOVA, which requires normally distributed data with equal variances, Friedman's test is ideal for ordinal or non-normally distributed data, making it particularly useful for analyzing dependent samples, such as matched subjects over time or repeated measures...

Estimating Population Standard Deviation 01:26

3.1K

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...

Estimating Population Mean with Unknown Standard Deviation 01:22

8.3K

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...

Estimating Population Mean with Known Standard Deviation 01:16

8.9K

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 +...

Mechanistic Models: Compartment Models in Individual and Population Analysis 01:23

85

Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least...