Answer:
6(3d+2)
Step-by-step explanation:
Answer:The set fee would be $15
Explanation:The set fee is the starting value. This means that it is the value of the y at x = 0 (y-intercept).
To get the set fee, we would first need to get the equation of the line.
Equation of the linear line has the following general formula:
y = mx + c
where m is the slope and c is the y-intercept
1- getting the slope:we are given two points which are:
(20,25) and (50,40)
the slope =

The equation now is:
y = 0.5x + c
2- getting the value of the y-intercept:To get the value of the c, we will use any of the given points, substitute in the equation and solve for c.
I will choose the point (20,25)
y = 0.5x + c
25 = 0.5(20) + c
25 = 10 + c
c = 15
The equation of the line representing the scenario is:y = 0.5x + 15
Now, we know that the value of the c is the y-intercept which is the initial value of the function at x=0.
In our situation, this represents the set fee.
Hope this helps :)
When you multiply it is just 5*23=115
115*2= 230
After solving the equation (work shown in image) your answer would be x > -2. I drew an image of how the number line should look at least similar to, using the solution.
*whenever you divide or multiply with a negative number reverse/flip the sign
< or > signs will always have an open circle
≥ or ≤ signs will have a closed/filled in circle
TIP: To know which way the arrow should point, look at the sign and think of it as an arrow.
< would have the arrow on the number point
this way: <-
or > which would have the arrow on the number line point this way: ->
Answer:
Pvalue = 0.1505
y = 0.550x1 + 3.600x2 + 7.300
Step-by-step explanation:
Given the data :
Study Hours GPA ACT Score
5 4 27
5 2 18
5 3 18
1 3 20
2 4 21
Using technology, the Pvalue obtained using the Fratio :
F = MSregression / MSresidual = 30.228571/ 8.190476 = 3.69
The Pvalue for the regression equation is:
Using the Pvalue from Fratio calculator :
F(1, 3), 3.69 = 0.1505
Using the Pvalue approach :
At α = 0.01
Pvalue > α ; Hence, we fail to reject H0 and conclude that ; There is not enough evidence to show that the relationship is statistically significant.
The regression equation :
y = A1x1 + A2x2 +... AnXn
y = 0.550x1 + 3.600x2 + 7.300
x1 and x2 are the predictor variables ;
y = predicted variable