Answer:
Step-by-step explanation:
By using regression line calculator,
Equation of the line of best fit,
c). y = 0.74x + 0.03
Here, x = Number of pints
y = Weight in pounds
d). We have to find the weight of 10 pints of the blueberries,
By substituting x = 10 in the equation,
y = 0.74(10) + 0.03
y = 7.4 + 0.03
y = 7.43 pounds
e). If per pound cost of the blueberries = $2.25
By substituting y = 2.25 in the equation,
2.25 = 0.74x + 0.03
x = 
x = $3.00 per pound
Therefore, cost of 10 pounds blueberries = 3 × 10
= $30
Answer:
x = 4
Step-by-step explanation:





The answer would be x ≠ 0
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
First start with the left side, doing distributive property
So... -2(x) = -2x and -2(5) = -10 Therefore on the left side you now have -2x -10
Next do the same on the right side
-2(x) = - 2x and -2(-2) = 4 so you have -2x + 4 + 5 and you add 4 and 5, leaving you with -2x + 9
Now that you have simplified both sides the problem now looks like this:
-2x - 10 = -2x + 9
Because you have equal terms on both sides (-2) those cancel out so you have -10 = 9
Just from looking at this we know that the statement is false because -1o does not equal 9
*The symbol, "≠" means not equal to"
55 g x 25 days = 1375 grams
1gram = 0.001 kg
1375 x 0.001 = 1.375 kg
round the answer as needed
A system of equations with infinitely many solutions is a system where the two equations are identical. The lines coincide. Anything that is equal to

will work. You could try multiply the entire equation by some number, or moving terms around, or adding terms to both sides, or any combination of operations that you apply to the entire equation.
You could multiply the whole thing by 4.5 to get

. If you want, you could mix things up and write it in slope-intercept form:

. The point is, anything that is equivalent to the original equation will give infinitely many solutions x and y. You can test this by plugging in values x and y and seeing the answers!
The attached graph shows that four different equations are really the same.