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.
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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:
x=2.4
Step-by-step explanation:
5·x=5x
5x=12
12/5=2.4
x=2.4
Monday = - 2.1 Celcius
Tuesday = 15.2
Wednesday = 5
Thursday = - 4.6
Friday = 0
What was the average temperature for the first week in December?
The average temperature = (- 2.1 + 15.2 + 5 - 4.6 + 0)/5 = (20.2 - 6.7)/5 = 13.5/5 = 2.7°C
Answer:
Step-by-step explanation: If I is intial height (y=I-6x) because Height = intial height + numbrt of knots -6 becuase as shown in the table every knot takes away 6 cm.
Answer:
You can actually draw both points in a graph to find out as it makes it easier to understand. Or you could think, if C has moved from (0,0) to (-2,3). It means it moves to the left of the x axis by 2, then moves up by 3 on the y axis.
So point P should move the same way. (-3, 4) move by -2 on the x axis would make it -5, and for y you go up 3 steps so 4+3=7 making P's final location (-5,7)