Answer:
1. -3/8 2. 1/2 3. -3/2
Step-by-step explanation:
You can use Y2-Y1/X2-X1 to find the answers
Ask me if you want it more in detail
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:
y=-3x+6
Step-by-step explanation:
that equation gives the answer for all the 'y'
Answer:
μ−2σ = 1,089.26
μ+2σ = 1,097.62
Step-by-step explanation:
The standard deviation of a sample of size 'n' and proportion 'p' is:
If n=1139 and p =0.96, the standard deviation is:
The minimum and maximum usual values are:
