印度男性成年人BMI的变化:一个定量回归分析
Archana Agnihotri1, Brinda Viswanathan1
1Madras School of Economics, Kotturpuram, Chennai600025, TN, India.
Journal of biosocial science
|October 10, 2023
概括
印度面临着营养不良的双重负担,随着超重和肥胖的增加以及持续的体重不足问题,特别是影响男性. 手工劳动,教育和饮食多样性是保持健康体重指数 (BMI) 的关键.
科学领域:
- 公共卫生 公共卫生
- 营养流行病学 营养流行病学
- 人体生理学 人体生理学
背景情况:
- 印度表现出双重营养不良负担:高比例的体重不足的成年人,以及超过三十年的超重/肥胖显著增加.
- 现有的研究主要集中在女性营养不良的双重负担上,对男性的关注较少.
研究的目的:
- 分析与20-54岁的印度男性的低和高体重指数 (BMI) 相关的因素.
- 对男性和他们的配偶之间与BMI相关的共同变量进行性别比较分析.
主要方法:
- 使用量子回归建模来检查分布中的BMI关联.
- 在2015-2016年期间,分析了印度男性 (20-54岁) 和他们的配偶的数据.
主要成果:
- 手工劳动职业,高等教育,饮食多样性和减少久坐行为与男性的正常BMI有关.
- 在男性和女性的BMI与共变量之间的关联中,观察到显著的性别特异性差异.
结论:
- 结果为个人行为改变和公共卫生干预提供了洞察力,以打击印度营养不良的双重负担.
- 解决体重不足,超重和肥胖问题需要根据社会经济和生活方式因素制定适合性别的战略.
相关概念视频
Variation: Normal Distribution, Range, and Standard Deviation
22.3K
In the field of psychology, there are several ways to organize measurements of a trait, feature, or characteristic (i.e., variables). Qualitative data, such as ethnicity, can be tabulated into a frequency count to provide information about the proportion, as well as the variety of groups in a sample or population. On the other hand, researchers can perform a wider set of calculations on quantitative data. The mean, mode, and median, for instance, are central tendency measures to identify a...
22.3K
Quartile
4.3K
Quartiles are numbers that separate the data into quarters. Quartiles may or may not be part of the data. To find the quartiles, first, find the median or second quartile. The first quartile, Q1, is the middle value of the lower half of the data, and the third quartile, Q3, is the middle value, or median, of the upper half of the data. To get the idea, consider the same data set:
1; 1; 2; 2; 4; 6; 6.8; 7.2; 8; 8.3; 9; 10; 10; 11.5
The median or second quartile is seven. The lower half of the...
1; 1; 2; 2; 4; 6; 6.8; 7.2; 8; 8.3; 9; 10; 10; 11.5
The median or second quartile is seven. The lower half of the...
4.3K
Regression Toward the Mean
6.3K
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...
6.3K
Applications of Normal Distribution
5.1K
The normal distribution is a useful statistical tool. One of its practical applications is determining the door height after considering the normal distribution of heights of persons, such that many can pass through it easily without striking their heads. The normal distribution can also determine the probability of a person having a height less than a specific height.
The heights of 15 to 18-year-old males from Chile from 1984 to 1985 followed a normal distribution. The mean height is 172.36...
The heights of 15 to 18-year-old males from Chile from 1984 to 1985 followed a normal distribution. The mean height is 172.36...
5.1K
Central Limit Theorem
15.1K
The central limit theorem, abbreviated as clt, is one of the most powerful and useful ideas in all of statistics. The central limit theorem for sample means says that if you repeatedly draw samples of a given size and calculate their means, and create a histogram of those means, then the resulting histogram will tend to have an approximate normal bell shape. In other words, as sample sizes increase, the distribution of means follows the normal distribution more closely.
The sample size, n, that...
The sample size, n, that...
15.1K
One-Way ANOVA: Unequal Sample Sizes
5.8K
One-way ANOVA can be performed on three or more samples of unequal sizes. However, calculations get complicated when sample sizes are not always the same. So, while performing ANOVA with unequal samples size, the following equation is used:
5.8K


