# An eccentric professor believes that a child with IQ 95 should have reading score 70. What is the equation of the professor's regression line for predicting reading score from IQ?

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She also believes that the reading score should increase by 2 point for every additional point of IQ.

She also believes that the reading score should increase by 2 point for every additional point of IQ.

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The equation of the regression line for predicting reading score from IQ, based on the professor's belief, can be expressed in the form of a linear regression equation:

[ \text{Reading Score} = \text{Intercept} + \text{Slope} \times \text{IQ} ]

Given that the professor believes a child with an IQ of 95 should have a reading score of 70, this point can be used to determine the intercept and slope of the regression line.

Let ( \text{Reading Score} = y ) and ( \text{IQ} = x ). Then, using the point (95, 70), the equation becomes:

[ 70 = \text{Intercept} + \text{Slope} \times 95 ]

This equation, along with the professor's belief, forms a system of equations to solve for the intercept and slope. Once the intercept and slope are found, they can be plugged into the regression equation to obtain the professor's regression line for predicting reading score from IQ.

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When evaluating a one-sided limit, you need to be careful when a quantity is approaching zero since its sign is different depending on which way it is approaching zero from. Let us look at some examples.

When evaluating a one-sided limit, you need to be careful when a quantity is approaching zero since its sign is different depending on which way it is approaching zero from. Let us look at some examples.

When evaluating a one-sided limit, you need to be careful when a quantity is approaching zero since its sign is different depending on which way it is approaching zero from. Let us look at some examples.

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