Estimation Methods

There are several techniques that can be used to generate estimates:

• Method of Moments
• Maximum Likelihood Estimation (MLE)

1. Method of Moments

This method compares the moments (central tendency and spread) that are computed from the sample data to the corresponding moments in the population. The population parameters can be estimated by working out the resulting equations.

• The approach of moments can estimate the mean age of a large population by analyzing age distribution. Moments are calculated for a subset to determine group mean and age distribution, which is then used to estimate the larger population. Trained estimates can be made by comparing moments of smaller and larger groups, allowing inference of population characteristics based on smaller sample data. This method provides accurate estimates for larger populations using smaller sample data.

2. Maximum Likelihood Estimation (MLE)

Maximum likelihood estimation (MLE) aims to find parameter values that give the highest chance of observing the data in a statistical model. It involves identifying values that maximize the likelihood of the observed data.

• Maximum likelihood estimation (MLE) is a method used to find the most probable values of variables based on given data. It involves starting with an initial estimate for a parameter and iteratively adjusting it to maximize the likelihood of observing the data. By comparing different estimates to the dataset, the MLE process helps identify the parameter values that best fit the data. This statistical method is valuable in accurately estimating unknown variables by increasing the probability of occurrence in the dataset through adjusting parameter values in a model.

Estimation is a technique for calculating information about a bigger group from a smaller sample, and statistics are crucial to analyzing data. For instance, the average age of a city’s population may be obtained by taking the age of a sample of 1,000 residents. While estimates aren’t perfect, they are typically trustworthy enough to be of value.

In this article, we examine the significance of statistics, their function in the interpretation of data, and how efficient data analysis leads to the making of decisions based on accurate information.