Answer:
Step-by-step explanation:
Steve works:
- 60 hour per week = 12 hours per day (60/5)
- first job: 3 hours per day = 15 hours per week
- x = second job hours per day
equitation:
1st option to calculate:
12 (total hours per day) = 3 (hours per day on first job) + X
9 = X
2nd option to calculate:
60(total hours per week) = 15 (3 hours per day x 5) + x
45 = x
you need to divide 45 / 5 to get the daily hours which is 9
Answer:
Part A) The area total of the design is 
Part B) The total area of the four large shaded triangles is 
Part C) The cost to purchase the material to make the triangles for the quilt will be 
Step-by-step explanation:
Part A) Find the area total of the design
The area total of the design is a square
so
To find the length side of the square design multiply the number of squares by 3 in
Part B) Find the total area of the four large shaded triangles
the area of the four large triangles is equal to
![A=4[\frac{1}{2}(b)(h)]](https://tex.z-dn.net/?f=A%3D4%5B%5Cfrac%7B1%7D%7B2%7D%28b%29%28h%29%5D)
we have that
substitute
![A=4[\frac{1}{2}(6)(6)]=72\ in^{2}](https://tex.z-dn.net/?f=A%3D4%5B%5Cfrac%7B1%7D%7B2%7D%286%29%286%29%5D%3D72%5C%20in%5E%7B2%7D)
Part C) How much will it cost to purchase the material to make the triangles for the quilt ?
To find the cost multiply the area of each design of four large triangles by 30 and then multiply by $0.08 per square inch
The cost is equal to
28/8 or 7/2 is the answer to that
Answer:
x= 8
Step-by-step explanation:
5.5(x-3)=17+10.5
5.5x - 16.5= 27.5
5.5x= 44
x= 8
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.
