When you add a fitting this new model, the estimated coefficient for height must be Different, but still positive.
<h3>What is the SLR model about?</h3>
If there is a positive correlation but negative regression coefficient it means that the influence or the effect of the intercorrelations that exist between the independent variables may be negative when there is a positive correlation coefficient that exist between the variable and the dependent variable.
Therefore When you add a fitting this new model, the estimated coefficient for height must be Different, but still positive.
See full question below
Suppose you have an SLR model for predicting IQ from height. The estimated coefficient predictor for age to create an MLR model. After for height is positive. Now, we add a fitting this new model, the estimated coefficient for height must be:
- Exactly the same as the SLR model
- Different, but still positive
- Zero Negative
- None of the above
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Answer:
(1.21875, 23.765625)
Step-by-step explanation:
-b/2a gives you the x value of the maximum point, with b=39 and a = -16
-39/2(-16) = 1.21875 = x
Plug that value back in and get the y value
would be the y value.
I included all the digits because you didn't tell me how many significant figures.
Answer: Third Option
yes; k = 4 and y = 4x
Step-by-step explanation:
Observe in the values of x and y given that when x decreases the variable-y also decreases.
Then the variation is direct.
Then, if between any two points of the function, the rate of variation k remains the same then the variation is constant.
We can test whether these conditions are met by using the given points.
(-2, -8) and (-4, -16)
The rate of variation k for these points is:
Now we calculate the variation rate for the points
(-4, -16) and (-6, -24)
The rate of variation is constant and equal to 4.
Then the answer is yes; k = 4 and y = 4x
The answer to this equation is x=-2.5