APPLICATION OF SUPERVISED LEARNING
LINEAR REGRESSION
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Question
[CLICK ON ANY CHOICE TO KNOW THE RIGHT ANSWER]
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Which of the following assumptions do we make while deriving linear regression parameters?The true relationship between dependent y and predictor x is linear The model errors are statistically independent The errors are normally distributed with a 0 mean and constant standard deviation The predictor x is non-stochastic and is measured error-free
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1, 2 and 3
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1, 3 and 4
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1 and 3
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All of the above
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Explanation:
Detailed explanation-1: -Assumption 1 – Linearity: The relationship between X and the mean of Y is linear. Assumption 2-Homoscedasticity: The variance of residual is the same for any value of X. Assumption 3 – Independence: Observations are independent of each other.
Detailed explanation-2: -The errors are normally distributed with a 0 mean and constant standard deviation.
Detailed explanation-3: -Answer and Explanation: c. The errors are independent. The errors in the linear regression model are assumed to take values that are basically independent to each other.
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