Answer:
some information is missing, so I looked for similar questions and found the attached image:
since each recipe requires ¹/₂ cup of butter, you are able to make 12 ÷ ¹/₂ = 24 recipes with one container of butter (assuming you have all the other ingredients).
One-half, ¹/₂, is a fraction that can also be written in decimal form as 0.5. Two times ¹/₂ is equal to 1, and similarly, twenty four times ¹/₂ is equal to 12.
Answer:
Step-by-step explanation:
because -3 is a zero, so x=-3
(k-1)(-3)²+k(-3)+1=0
9(k-1)-3k+1=0
9k-9-3k+1=0
6k=8
3k=4
k=4/3
(B)
∵ x=-1 is a zero of p(x)=kx²-4x+k
so k(-1)²-4(-1)+(-1)=0
k+4-1=0
k=-3
Answer:
1.81,2.54,3.77
Total
8.12
Step-by-step explanation:
To find the tax, multiply the original amount by the tax rate
tax
25 * 7.25%
25 *.0725
Rounding to the nearest cent
1.81
35 * 7.25%
35 *.0725
Rounding to the nearest cent
2.54
52 * 7.25%
52 *.0725
Rounding to the nearest cent
3.77
Total for all three
1.81+2.54+3.77=8.12
P=1560000
APR=5.6%
monthly interest, i=5.6%/12=7/1500 [fractions keep exact values]
R=1+i=1+7/1500
# of periods, n=30 years = 360 periods
monthly payment, A
A=PR^n(i)/(R^n-1)
=1560000*(1+7/1500)^360*(7/1500)/((1+7/1500)^360-1)
=$8955.632
At the end of eight years,
number of periods, n1 = 8*12 = 96
If paid off at the end of 8 years, value of loan then
future value of principal
F1=PR^n1=1560000*(1+7/1500)^96=2439135.635
future value of payments
F2=A(R^n1-1)/i=8955.632*(1+7/1500)^96-1)/(7/1500)=1081485.620
Therefore the balloon payment
= future value of principal (owing) - future value of payments (paid)
=F1-F2
=2439135.635-1081485.620
=1357650.0152
Round to two places after decimal to get final answer.
Answer:
a. the line that passes through the most data points.
Step-by-step explanation:
Regression analysis, is used to draw the line of‘ best fit’ through co-ordinates on a graph. The techniques used enable a mathematical equation of the straight line form y=mx+c to be deduced for a given set of co-ordinate values, the line being such that the sum of the deviations of the co-ordinate values from the line is a minimum, i.e.
The least-squares regression lines is the line of best fit
