Answer:
d) Squared differences between actual and predicted Y values.
Step-by-step explanation:
Regression is called "least squares" regression line. The line takes the form = a + b*X where a and b are both constants. Value of Y and X is specific value of independent variable.Such formula could be used to generate values of given value X.
For example,
suppose a = 10 and b = 7. If X is 10, then predicted value for Y of 45 (from 10 + 5*7). It turns out that with any two variables X and Y. In other words, there exists one formula that will produce the best, or most accurate predictions for Y given X. Any other equation would not fit as well and would predict Y with more error. That equation is called the least squares regression equation.
It minimize the squared difference between actual and predicted value.
Answer:
7/10
51/100
473/1000
27/100
537/1000
1649/10000
Step-by-step explanation:
im curious about what the dots are in .16.49 and .5.37, why are there two dots?
Answer:
i think the answer is 76cm im pretty sure it is
Step-by-step explanation:
if so mark this as brainliest answer!!!
I think the answer is -7/4.