Parametric Methods
Parametric methods are statistical models that assume a specific distribution for the data being analyzed. These models have a fixed number of parameters that are estimated from the data. The most common example of a parametric method is linear regression, where the data is assumed to follow a normal distribution and the model estimates the slope and intercept of the line that best fits the data. Other examples of parametric methods include logistic regression, ANOVA, and time series analysis. Parametric methods are useful when the underlying distribution of the data is known or can be reasonably assumed, and when the number of parameters is small relative to the sample size. They are also useful for making predictions and estimating confidence intervals. However, if the assumptions of the model are violated, the results may be biased or misleading.
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