Answer: 321 adult tickets and 227 children tickets were sold.
Step-by-step explanation:
Let x represent the number of adult tickets that were sold.
Let y represent the number of children tickets that were sold.
The total number of tickets that the theatre sold is 548. This means that
x + y = 548
Adult tickets sell for $6.50 each, and children's tickets sell for $3.50 each. The total ticket sales was $2881. This means that
6.5x + 3.5y = 2881 - - - - - - - - - - -1
Substituting x = 548 - y into equation 1, it becomes
6.5(548 - y) + 3.5y = 2881
3562 - 6.5y + 3.5y = 2881
- 6.5y + 3.5y = 2881 - 3562
- 3y = - 681
y = - 681/ -3
y = 227
x = 548 - y = 548 - 227
x = 321
Answer:
8x+$0.75x.
Step-by-step explanation:
8x is the hours you babysit and $0.75x the miles you drive.
Multiply 4.93m by 8.5m to get 41.905m to the second power.
You do this because there are 2 identical triangles on the top. And if you put those two triangles together, you get a rectangle. The length of the rectangle is 8.5m while the width would be 4.93. Multiplying the length and width gives you the area.
Then multiply 10.2m by 8.5m to get 86.7m to the second power.
You do this because there are 2 identical triangles on the bottom. And if you put those two triangles together, you get another rectangle. The length of the rectangle is 8.5m while the width would be 10.2m. Multiplying those together gives you the area.
You then add the two areas, 41.905m to the second power and 86.7m to the second power, to get the area of the entire figure.
After adding, you get 128.605 m to the second power. That's the answer
Answer:
The least squares method results in values of the y-intercept and the slope, that minimizes the sum of the squared deviations between the observed (actual) value and the fitted value.
Step-by-step explanation:
The method of least squares works under these assumptions
- The best fit for a data collection is a function (sometimes called curve).
- This function, is such that allows the minimal sum of difference between each observation and the expected value.
- The expected values are calculated using the fitting function.
- The difference between the observation, and the expecte value is know as least square error.
