Answer:
Squared differences between actual and predicted y
Step-by-step explanation:
The least squares regression method used in predictive modeling for linear regression models produces a best fit line which will minimize the square of the mean difference between the actual and projected or predicted values of the dependent, y variable. Hence, the when the sum of the squared value of the difference between the actual and predicted values (residual) are taken, the fit which gives the minimum sum of squared value is the best fit line upon which the estimated regression equation is based.
It does not vary directly with x
look at ur points on the table....put in y/x form
32/40 = 0.8
16/28 = 0.57
12/16 = 0.75
these are all different...this means that y does not vary directly with x
Answer:
<em>Value</em><em> </em><em>of</em><em> </em><em>the</em><em> </em><em>expression</em><em> </em><em>is</em><em> </em><em> </em><em>-35</em>
Step-by-step explanation:
<em>HAVE A NICE DAY</em><em>!</em>
<em>THANKS FOR GIVING ME THE OPPORTUNITY</em><em> </em><em>TO ANSWER YOUR QUESTION</em><em>. </em>
Answer:
42 slices per hour
Step-by-step explanation:
Answer: 45 Pennies and 15 Nickels
Step-by-step explanation:
0.05 x 15 = 0.75
0.01 x 45 = 0.45
0.45 + 0.75 = 1.20
