<h3><u>Candidate score 40%</u></h3>
I believe the answer to this diagram should be <u><em>Diagram B</em></u>
Answer:
6
Step-by-step explanation:
10, 10, 10, 10, 10, 10. Those are 6 tens
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:
53
Step-by-step explanation:
To evaluate 6x + 11, we must substitute the value of x.
Since we know that x = 7, it is easier to evaluate the expression;
6x + 11
6(7) + 11
Since 6 is outside the parenthesis, we must multiply everything inside the parenthesis by 6;
6(7) + 11
42 + 11
When you add the two numbers you get:
53
