APPLICATION OF SUPERVISED LEARNING
LINEAR REGRESSION
Question
[CLICK ON ANY CHOICE TO KNOW THE RIGHT ANSWER]

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 nonstochastic and is measured errorfree

1, 2 and 3


1, 3 and 4


1 and 3


All of the above

Explanation:
Detailed explanation1: Assumption 1 – Linearity: The relationship between X and the mean of Y is linear. Assumption 2Homoscedasticity: The variance of residual is the same for any value of X. Assumption 3 – Independence: Observations are independent of each other.
Detailed explanation2: The errors are normally distributed with a 0 mean and constant standard deviation.
Detailed explanation3: 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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