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The following sample, X = {x, y}, was drawn from a large data set. Assume that the original data set satisfies the condition of normality, homoscedasticity, and independence between observations. Answer the following questions to make a linear regression model, h, to predict y using the gradient descent sequential learning. i x1 x2 y 10.25 2 2.4 2 0.1 1.3 1.2 30.31 2.9 3.3 40.49 4.5 5.1 Suppose a linearity of features, x, to the target and between features is tested. Which of the following are your expectations? Select all applies. X1 and X2 have a weak negative correlation value. X2 is linear to y, but x₁ is not. X1 and X2 have a strong negative correlation value. X2 and x1 are linear to y. X1 and X2 have a weak positive correlation value. Ox1 is linear to y, but x2 is not. X1 and X2 have a strong positive correlation value.

The following sample, X = {x, y}, was drawn from a large data set. Assume that the original data set satisfies the condition of normality, homoscedasticity, and independence between observations. Answer the following questions to make a linear regression model, h, to predict y using the gradient descent sequential learning. i x1 x2 y 10.25 2 2.4 2 0.1 1.3 1.2 30.31 2.9 3.3 40.49 4.5 5.1 Suppose a linearity of features, x, to the target and between features is tested. Which of the following are your expectations? Select all applies. X1 and X2 have a weak negative correlation value. X2 is linear to y, but x₁ is not. X1 and X2 have a strong negative correlation value. X2 and x1 are linear to y. X1 and X2 have a weak positive correlation value. Ox1 is linear to y, but x2 is not. X1 and X2 have a strong positive correlation value.

Calculus For The Life Sciences
Calculus For The Life Sciences
2nd Edition
ISBN:9780321964038
Author:GREENWELL, Raymond N., RITCHEY, Nathan P., Lial, Margaret L.
Publisher:GREENWELL, Raymond N., RITCHEY, Nathan P., Lial, Margaret L.
Chapter1: Functions
Section1.2: The Least Square Line
Problem 4E
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The following sample, X = {x, y}, was drawn from a large data set. Assume that the original data set satisfies the condition of normality, homoscedasticity, and independence between observations. Answer the following questions to make a linear regression model, h, to predict y using the gradient descent sequential learning. i x1 x2 y 1 0.25 2 2.4 2 0.1 1.3 1.2 3 0.31 2.9 3.3 4 0.49 4.5 5.1 Suppose a linearity of features, x, to the target and between features is tested. Which of the following are your expectations? Select all applies. X1 and X2 have a weak negative correlation value. X2 is linear to y, but x1 is not. X1 and X2 have a strong negative correlation value. X2 and X1 are linear to y. X1 and X2 have a weak positive correlation value. X1 is linear to y, but X2 is not. X1 and X2 have a strong positive correlation value.
Transcribed Image Text:The following sample, X = {x, y}, was drawn from a large data set. Assume that the original data set satisfies the condition of normality, homoscedasticity, and independence between observations. Answer the following questions to make a linear regression model, h, to predict y using the gradient descent sequential learning. i x1 x2 y 1 0.25 2 2.4 2 0.1 1.3 1.2 3 0.31 2.9 3.3 4 0.49 4.5 5.1 Suppose a linearity of features, x, to the target and between features is tested. Which of the following are your expectations? Select all applies. X1 and X2 have a weak negative correlation value. X2 is linear to y, but x1 is not. X1 and X2 have a strong negative correlation value. X2 and X1 are linear to y. X1 and X2 have a weak positive correlation value. X1 is linear to y, but X2 is not. X1 and X2 have a strong positive correlation value.
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