Part A: Option e: 
Option f: 
Part B: Option c: 
Option d: 
Part C: Option a: 
Option b: 
Explanation:
Part A: The equation is 
Simplifying, we have,

Taking the term 5 common out, we have,

Thus, the above two expressions are equivalent to the equation
.
Hence, Option e and Option f are the correct answers.
Part B: The equation is 
Taking the term 15 common out, we have,

Also, the equation can be rewritten as,

Thus, the above two expressions are equivalent to the equation 
Hence, Option c and Option d are the correct answers.
Part C: The equation is 
Multiplying, we have,

The above expression can be rewritten as,

Thus, the above two expressions are equivalent to the equation 
Hence, Option a and Option b are the correct answers.
It is B. Since each angle is 60 degrees, if you rotate B counterclockwise 6 times, it means that you rotated B 360 degrees. Therefore, the image of B would be B.
The answer is 549 divided by 9 = 61 vehicles per acre
5353 minutes = 89.22 hours
Maybe the figures should be 53 and 20??
Let's let Billy = A
The formula to use for work problems is
Time = (A*B) / (A+B)
53 = ( (A +20)* A) / (A +20 + A) )
53 = ( A^2 + 20A ) / (2A + 20)
A^2 + 20A -106A -1060
A^2 -86A -1060
A = 96.935
Bobby Mows in 116.935 minutes
Correct question
Sale Price :160 | 180 | 200 | 220 | 240 | 260 | 280
New home : 126 | 103 | 82 | 75 | 82 | 40 | 20
A.) state the linear regression function that estimates the number of new homes available at a specific price.
B.) state the correlation Coefficient of the data, and explain what it means in the context of the problem
Answer:
Y = -0.79X + 249.86
R = -0.9543
Step-by-step explanation:
Sale Price :160 | 180 | 200 | 220 | 240 | 260 | 280
New home : 126 | 103 | 82 | 75 | 82 | 40 | 20
Calculate the Linear regression equation :
Using the linear regression calculator :
The linear regression equation is :
Y = -0.79X + 249.86
The correlation Coefficient 'R' measures the strength of statistical relationship between the relative movement of two variables. The The value of R is -0.9543 in the question above.
This is a strong negative correlation, which means that high sales price of homes scores correlates with low number of new homes scores (and vice versa). Homes with high sales price have fewer number of new homes.