This is the question:
A
bicycle manufacturing company makes a particular type of bike.
Each
child bike requires 4 hours to build and 4 hours to test.
Each
adult bike requires 6 hours to build and 4 hours to test.
With
the number of workers, the company is able to have up to 120 hours of building
time and
100 hours of testing time for a week.
If
c represents child bikes and a represents adult bikes,
determine
which system of inequality best explains whether the company can build 10 child
bikes and 12 adult bikes in the week
Now you
can state the system of inequalities from the statements
1) First inequality based on the hours availble
to buiding
Each
child bike requires 4 hours, e<span>ach
adult bike requires 6 hours to build and </span>the company is able to have up to 120 hours of building =>
4c + 6a ≤ 120
2) Second inequality based of the hours available to testing.
Each
child bike requires 4 hours to test, each
adult bike 4 hours to test and the company is able to have up 100 hours of testing time for a week =>
4c + 4a ≤ 100
Then the two inequalities are:
4c + 6a ≤ 1204c + 4a ≤ 100<span>
The answer is Yes, because the bike order meets the restrictions of 4c + 6a ≤ 120 and 4c + 4a ≤ 100Which you can verify by replacing in both equations 10 for c and 12 for a. Look:
1) 4(10) + 6(12) = 40 + 72 = 112 ≤ 1202) 4(10) + 4(12) = 40 + 48 = 88 ≤ 100</span>
Answer:
Boris will have $250 in 10 months.
Step-by-step explanation:
$25 = one month.
$250= ?months
To find ?, the months we divide 250 by 25 to get this:
250 ÷ 25 = 10
10 months is the amount of time Boris will have to wait until he saves up $250.
Hope this helps you! Good luck with your quiz! :)
11 times the sun of a number and 15
11(y+15)
the answer is d
have great day and I hope you make a good grade
Answer-
The exponential model best fits the data set.
Solution-
x = input variable = number of practice throws
y = output variable = number of free throws
Using Excel, Linear, Quadratic and Exponential regression model were generated.
The best fit equation and co-efficient of determination R² are as follows,
Linear Regression
Quadratic Regression
Exponential Regression
The value of co-efficient of determination R² ranges from 0 to 1, the more closer its value to 1 the better the regression model is.
Now,
Therefore, the Exponential Regression model must be followed.
