Learning Path
Question & Answer1
Understand Question2
Review Options3
Learn Explanation4
Explore TopicChoose the Best Answer
A
Maximum Likelihood Estimation
B
Simple Random Sampling
C
Stratified Sampling
D
Cross-Validation
Understanding the Answer
Let's break down why this is correct
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
Practice Similar Questions
Test your understanding with related questions
1
Question 1In a logistic regression model, which of the following best describes the role of a parametrized predictor?
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Question 2In the context of parametrized predictors, which combination of estimation techniques and regularization methods can lead to improved model evaluation by reducing overfitting?
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Question 3In the context of logistic regression, which estimation technique is primarily used to maximize predictive accuracy while ensuring that the model effectively predicts binary outcomes?
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4
Question 4Which of the following scenarios best exemplifies the use of a parametrized predictor?
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Question 5In the context of parametrized predictors, which statement best describes the role of parameters in the predictive model?
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Question 6Which of the following statements about parametrized predictors are true? Select all that apply.
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Question 7In the context of parametrized predictors, which aspect most directly influences the model's capacity to generalize to unseen data?
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