UGC-NET (NTA) POPULATION STUDIES, December-2023

The main functions of a Geographical Information System (GIS) typically include capturing, storing, querying, analyzing, and displaying geographic data. Among the options provided, "Argument" is not typically considered a main function of GIS. GIS serves as a powerful tool for capturing, storing, querying, analyzing, and displaying geographic information, which can be utilized in various fields such as urban planning, environmental management, natural resource exploration, disaster response, and more. While GIS can facilitate discussions and debates about spatial issues, it's not primarily focused on engaging in arguments; rather, its main functions revolve around managing and analyzing spatial data.

Type-I error occurs in hypothesis testing when we mistakenly reject the null hypothesis, concluding that there is a significant effect or relationship when there isn't one in reality. It represents a false positive outcome, which can lead to incorrect conclusions and potentially undesirable consequences, such as approving ineffective treatments or interventions. Controlling the significance level helps minimize the risk of Type-I error, emphasizing the importance of careful interpretation of statistical results to avoid drawing erroneous conclusions.

In statistics, the relationship between the mean, median, and mode for a moderately skewed distribution is captured by the formula:
Mode = 3 Median - 2 Mean.
This empirical relationship, known as the empirical rule, provides an approximation for the mode based on the mean and median. It is useful because the mean, median, and mode tend to be close to each other in a normal distribution, but as the distribution becomes skewed, this formula helps estimate the mode more accurately. This relationship highlights how the skewness of the distribution affects these three measures of central tendency.

In statistical estimation, bias refers to the difference between the expected value of an estimator and the true value of the parameter being estimated. If an estimator is unbiased, its expected value equals the true parameter value. However, if there is a systematic deviation, the estimator is biased. Bias measures the accuracy of an estimator: a high bias indicates that the estimator consistently overestimates or underestimates the parameter, while a low bias suggests more accurate estimation. Unlike standard error or sampling error, which quantify variability, bias specifically addresses the systematic error in the estimation process.

Variance measures the average squared deviations from the mean, providing a sense of how spread out the data points are. The standard deviation (SD) is the square root of the variance and gives a measure of spread in the same units as the data, making it easier to interpret.
Given that the variance is 121, the standard deviation is:
So, the correct answer is: 11

Neonatal mortality refers to the number of deaths that occur among live-born infants within the first 28 days of life. This period is critical because it represents a highly vulnerable time for newborns, who are at a higher risk of dying due to complications during birth, congenital anomalies, infections, or prematurity. Neonatal mortality is a crucial indicator of a region's healthcare quality and maternal health services.

The Medical Certification of Cause of Death (MCCD) scheme was first introduced in Pune, India in 1951. This scheme aims to systematically record and classify the causes of death based on medical certifications. The implementation of MCCD helps in generating reliable mortality statistics, which are essential for public health planning, policy-making, and epidemiological research. By accurately identifying and documenting causes of death, health authorities can better understand disease patterns, allocate resources more effectively, and develop targeted health interventions to reduce mortality rates and improve population health outcomes.

In a Life Table, Lx represents the number of person-years lived within each age interval, while Tx represents the total number of person-years lived after each age interval. Lx provides information about the number of individuals surviving up to a particular age, whereas Tx accumulates these individual years of life lived up to each age interval. Together, Lx and Tx allow for the calculation of various demographic measures such as life expectancy and survivorship curves, providing valuable insights into the mortality patterns and life expectancies of a population across different age groups.