Which characteristic of a data set makes a linear regression model unreasonable?
Answer: A correlation coefficient close to zero makes a linear regression model unreasonable.
If the correlation between the two variable is close to zero, we can not expect one variable explaining the variation in other variable. For a linear regression model to be reasonable, the most important check is to see whether the two variables are correlated. If there is correlation between the two variable, we can think of regression analysis and if there is no correlation between the two variable, it does not make sense to apply regression analysis.
Therefore, if the correlation coefficient is close to zero, the linear regression model would be unreasonable.
Answer:
∛-27 or -3
Step-by-step explanation:
I don't think you can do anything further to this problem unless we have to solve for something.
If it is 3 square root of the whole thing, then the answer is -3 because
-3 x -3 x -3 = -27
-3 x -3 = negative x negative = positive = 9
9 x -3 = positive x negative = negative = -27
Hope this helps!
P= 2.5m + 35 (replace p with 115)
115= 2.5m + 35 (subract 35 from each side)
115 - 35 = 2.5m
80 = 2.5m (divide 2.5 from each side)
80/2.5= 2.5m/2.5
32 = m
The price of the materials is $32
<span>Linear regression is a method of finding the linear equation that comes closest to fitting a collection of data points.
</span>The better the choice of line, the closer the predicted values will be to the observed values.
The differences between the data pints (observed values) and the estimated (pedicted) regression line is called the <span>residue.
</span>Residue = Observed Value -<span> Predicted Value</span>
