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Questions and Answers
In error minimization, what does the term 'sum of squares' refer to?
In error minimization, what does the term 'sum of squares' refer to?
- The sum of the individual distances of the predicted values from the actual data points.
- The total distance as the sum of squares of the differences of the values predicted by the linear model and the actual data. (correct)
- The square root of the sum of the squared errors.
- The sum of the squares of the parameter estimates.
What is the aim of error minimization with adjustment?
What is the aim of error minimization with adjustment?
- Balancing the predictive errors.
- Maximizing the predictive errors.
- Minimizing the predictive errors. (correct)
- Ignoring the predictive errors.
What is the role of the value 𝛽0 in the error minimization method?
What is the role of the value 𝛽0 in the error minimization method?
- Maximizing the fit of the model to the population.
- Finding the value that leads to predictions of Y that minimize the distance between all the data points and the predicted value. (correct)
- Minimizing the sum of squares of the errors.
- Determining the number of parameters in the model.
What characterizes good models in the context of model comparison?
What characterizes good models in the context of model comparison?
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What does 'I' represent in the context of the participants?
What does 'I' represent in the context of the participants?
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