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:
<u>8 home theaters are sold for each $ 500 spent on advertising </u>
Step-by-step explanation:
1. Let's review the information given to us to answer the question correctly:
Advertising ($) 1,000 - $ 2,000 - $ 3,000
Home Theaters 16 - 32 - 48
2. How many home theaters does the company sell for each $500 spent on advertising?
As we can see in the graph:
16 home theaters are sold when the amount on advertising is $ 1,000
32 home theaters are sold when the amount on advertising is $ 2,000
48 home theaters are sold when the amount on advertising is $ 3,000
Therefore, we can use this ratio:
x = Number of home theaters that are sold when the amount on advertising is $ 500
16/1,000 = x/500
1,000x = 500 *16
1,000x = 8,000
x = 8,000/1,000
<u>x = 8</u>
Answer:
f(n) = 2 + 5n. This is an arithmetic sequence.
Step-by-step explanation:
f(1) = 7
f(2) = 7 + 5
f(3) = 7 + 5 + 5 = 7 +10
f(4) = 7 + 5 + 5 + 5 = 7 + 15
In general,
f(n) = 7 + 5(n -1 )
= 7 + 5n - 5
= 2 + 5n
We have the sequence 7, 12, 17, 22, 27 …
This is an arithmetic sequence, because it is a sequence of numbers in which the <em>common difference</em> between consecutive terms is 5.
