Ok so
cost=setupfee+costused
costused=(number of months) times (cost per month)
if we represent number of months as x
we know cost per month is 18
18x is costused
and in 2 m onts, he paid 81 so
81=startup fee +18x
2 monts so x=2
81=startupfeee+36
subtract 36
45=startup fee
5 monts is x=5
45+18(5)=45+90=135
A. cost=18x+45
B. $45
C. $135
"Alaina’s sugar cookie recipe calls for 2 1/4
cups of flour per batch. If she wants to make 2/3
a batch of cookies, how much flour should she use?"
1 1/2 Cups, if she wants to make less than the original recipe, she would need less flour, you have to divide.
57 is the answer cuz im good at math math is my thang
Answer:
1st side=44 cm, 2nd side= 55cm
Step-by-step explanation:
let a = '4x' , b = '5x'
h= 12cm
Area of trapezium=1/2*(a+b)h
area=594=1/2*9x*12
594*2/12=9x
594/6=9x
99=9x
99/9=11=x
therefore, 4x=4*11=44cm
&5x=5*11=55cm
Answer:
The correlation coeffcient for this case was provided:
r =0.934
And this coefficient is very near to 1 the maximum possible value, so then we can interpret that the relationship between the entrace exam score and the grade point average are strongly linearly correlated .
We can also find the
who represent the determination coefficient and we got:

And the interpretation for this is that a linear model explains appproximately 87.2% of the variability between the two variables
Step-by-step explanation:
Previous concepts
The correlation coefficient is a "statistical measure that calculates the strength of the relationship between the relative movements of two variables". It's denoted by r and its always between -1 and 1.
And in order to calculate the correlation coefficient we can use this formula:
The correlation coeffcient for this case was provided:
r =0.934
And this coefficient is very near to 1 the maximum possible value, so then we can interpret that the relationship between the entrace exam score and the grade point average are strongly linearly correlated .
We can also find the
who represent the determination coefficient and we got:

And the interpretation for this is that a linear model explains appproximately 87.2% of the variability between the two variables