📚 Learning Guide
Parametrized Predictors
easy

In the context of parametrized predictors, which estimation technique is commonly used to determine the parameters of a regression model?

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Choose the Best Answer

A

Maximum Likelihood Estimation

B

Simple Random Sampling

C

Stratified Sampling

D

Cross-Validation

Understanding the Answer

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Answer

In regression with parametrized predictors, the most common way to find the model parameters is ordinary least squares (OLS). OLS works by choosing the values of the coefficients that make the sum of squared differences between the observed outcomes and the predictions as small as possible. This is done by solving a simple set of linear equations derived from setting the derivative of that sum to zero. For example, if you have a model \(y = \beta_0 + \beta_1 x\), OLS will give you the \(\beta_0\) and \(\beta_1\) that best fit the data points in a least‑squares sense.

Detailed Explanation

MLE picks the parameter values that make the observed data most likely. Other options are incorrect because Simple random sampling is about choosing a representative sample; Stratified sampling divides the population into groups before sampling.

Key Concepts

estimation techniques
Topic

Parametrized Predictors

Difficulty

easy level question

Cognitive Level

understand

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