ABC is a right triangle, so by Pythagoras' theorem,

Then by the law of sines,


Part a)
for extraneous solution
<span>
1 ⋅ sqrt(x+2) + 3 = 0</span>
for non extraneous solution
1⋅ sqrt(x+2) + 3 = 6
part b) solve each equation
1⋅sqrt(x+2)+3=0
x+sqrt(x+2)=−3
square both sides
(sqrt(<span>x+2)</span>)^ 2 = (−3)^2
x+2=9
x=9−2
x=7
do you see why its extraneous
Answer:
You can model a data using a linear function when the dependent variable is a multiple of the independent formula plus another constant by the y-intercept. The constant multiple is represented by the slope. In real life problems, linear function is applied when you want to determine the cost given with a slope which is represented by cost per unit time. For example, the cost of wifi connection is $10/month plus $2 inclusive for phone charges. The linear function would be:
C = 10t + 2
where C is the cost and t is time in months
Step-by-step explanation:
Answer:Fee as a function of miles:
f(m) = 16.95 + 0.92m
140.23 = 16.95 + 0.92m
0.92m = 140.23 - 16.95
0.92m = 123.28
m = 123.28/0.92
m = 134 miles
Step-by-step explanation:
The table is missing in the question. The table is attached below.
Solution :
Let X = appraised value
Y = area (square feet)
The regression line is given by :







The regression line is :

To estimate the error variance, we have:
Error variance, 

