Answer:
Step-by-step explanation:
we know that
In an <u><em>Arithmetic Sequence</em></u> the difference between one term and the next is a constant, and this constant is called the common difference
we have
Let
we have that
so
The common difference is equal to 9
We can write an Arithmetic Sequence as a rule:
where
a_n is the nth term
a_1 is the first term
d is the common difference
n is the number of terms
Find the 38th term of the arithmetic sequence
we have
substitute the values
Answer:
<h2>
B. 4 StartRoot 2 EndRoot i
</h2>
Step-by-step explanation:
Given the surd function √-2 and √-18, we are to fund the sum of both values.
Taking the sum:
= √-2 + √-18
= (√2 * √-1)+ (√18 *√-1)
from complex numbers, √-1) = i
The expression becomes
= √2 i+ √18 i
= √2 i+ √9*2 i
= √2 i+ 3√2 i
= 4 √2 i
= √-2 + √-18 = 4 √2 i
The result is 4 StartRoot 2 EndRoot i
5280ft = 1mile
13,000ft = 2.46miles 8,000ft = 1.52 miles
2.46 x 1.52 = 3.7 miles^2
Answer:
(1,0)
Step-by-step explanation:
The given functions are:
f(x) = log₂x
and
g(x) = log₁₀x
We know that logarithm of 1 is always zero.
This means that irrespective of the base, the y-values of both functions will be equal to 0 at x=1
Therefore the point the graphs of f and g have in common is (1,0)
Correct question
Sale Price :160 | 180 | 200 | 220 | 240 | 260 | 280
New home : 126 | 103 | 82 | 75 | 82 | 40 | 20
A.) state the linear regression function that estimates the number of new homes available at a specific price.
B.) state the correlation Coefficient of the data, and explain what it means in the context of the problem
Answer:
Y = -0.79X + 249.86
R = -0.9543
Step-by-step explanation:
Sale Price :160 | 180 | 200 | 220 | 240 | 260 | 280
New home : 126 | 103 | 82 | 75 | 82 | 40 | 20
Calculate the Linear regression equation :
Using the linear regression calculator :
The linear regression equation is :
Y = -0.79X + 249.86
The correlation Coefficient 'R' measures the strength of statistical relationship between the relative movement of two variables. The The value of R is -0.9543 in the question above.
This is a strong negative correlation, which means that high sales price of homes scores correlates with low number of new homes scores (and vice versa). Homes with high sales price have fewer number of new homes.
