Parameter

What Parameter is

Parameter in statistics is a numerical characteristic of a population or a sample, such as the mean or standard deviation. Parameters are estimated from sample data, and are used to describe the population from which the sample was drawn. The following are the steps for parameter estimation:

  1. Define the population of interest: The first step is to identify the population of interest, i.e. the members of the population that you would like to study.

  2. Choose a sample: The second step is to select a sample from the population of interest. The sample should be representative of the population, i.e. it should contain members that are similar to the members of the population.

  3. Collect data: Once a sample has been selected, the third step is to collect data from the sample. The data should be relevant to the parameter that is being estimated.

  4. Estimate the parameter: The fourth step is to estimate the parameter from the sample data. This is done by calculating the sample mean, sample standard deviation, sample median, etc.

  5. Interpret the result: The fifth step is to interpret the result of the parameter estimation. This is done by comparing the estimated parameter to the population parameter and interpreting the difference.

Examples

  1. Parameters are used to calculate the mean, median and mode of a dataset.
  2. Parameters are used to calculate the variance and standard deviation of a dataset.
  3. Parameters are used to measure the skewness of a distribution.
  4. Parameters are used to measure the degree of correlation between two variables.
  5. Parameters are used to estimate the coefficients of a linear regression model.

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