Here we go again... can someone explain how these problems work?
1 answer:
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We are given the first term and the common ratio, this means they belong to a geometric series.
For the given series:
Each term of the geometric series is obtained by multiplying the previous term by common ratio.
So the next terms will be:
-4.5, -6.75, -10.125, -15.1875, -22.78125
The general formula for the G.P would be:
On plotting the series, the result will be like this:
For the given series:
Each term of the geometric series is obtained by multiplying the previous term by common ratio.
So the next terms will be:
-4.5, -6.75, -10.125, -15.1875, -22.78125
The general formula for the G.P would be:
On plotting the series, the result will be like this:
Step-by-step explanation:
The first step is to find the (b − a) / n factor in the limit of the Riemann sum. This is the Δx.
The next step is to identify the function and the argument, f(xᵢ). The argument xᵢ looks like a + i (b − a) / n.
From there, you can write two equations:
Δx = (b − a) / n
xᵢ = a + i (b − a) / n = a + i Δx
Since you know xᵢ and Δx, you can identify a. And when you know a, you can plug that into Δx to find b. So now you have the limits of the definite integral.
Finally, write the integral using the limits and the function.
Here's an example. Suppose you have the limit:
lim(n→∞) ∑ᵢ₌₁ⁿ √(1 + 3i/n) (3/n)
First, we notice that Δx = (b − a)/n = 3/n. So b − a = 3.
Next, we identify that xᵢ = 1 + 3i/n = a + i (3/n), so a = 1. Therefore, b = 4.
Now, we identify the function, f(xᵢ) = √xᵢ.
Finally, we plug it all into a definite integral:
∫₁⁴ √x dx
Answer:
a)
Price=150.414-16.626 Age
b)
$34.032
c)
We can't estimate the price of 18 year old car using the above regression equation because the price of 18 year old car falls outside the scope of the model
Step-by-step explanation:
Age(X) Price(Y)
8 18
3 94
6 50
9 21
2 145
5 42
6 36
3 99
a)
The regression equation can be written as
y=a+bx
a=ybar-bxbar
xbar=sumx/n
xbar=(8+3+6+9+2+5+6+3)/8=5.25
ybar=sumy/n
ybar=(18+94+50+21+145+42+36+99)/8=63.125
x y x-xbar y-ybar (x-xbar)^2 (x-xbar)(y-ybar)
8 18 2.75 -45.125 7.5625 -124.09375
3 94 -2.25 30.875 5.0625 -69.46875
6 50 0.75 -13.125 0.5625 -9.84375
9 21 3.75 -42.125 14.0625 -157.96875
2 145 -3.25 81.875 10.5625 -266.09375
5 42 -0.25 -21.125 0.0625 5.28125
6 36 0.75 -27.125 0.5625 -20.34375
3 99 -2.25 35.875 5.0625 -80.71875
Total 43.5 -723.25
b=-723.25/43.5
b=-16.626
a=ybar-bxbar
a=63.125-(16.626)*5.25
a=150.414
Thus, the required regression equation is
y=150.414-16.626 x
Price=150.414-16.626 Age
b)
For predicting price of 7 years old car we put x=7 in the estimated regression equation
y=150.414-16.626 x
y=150.414-16.626(7)
y=150.414-116.382
y=34.032
The predicted price of a 7 year old car is $34.032.
c)
We can't estimate the price of 18 year old car using the above regression equation because the price of 18 year old car falls outside the scope of the model. The estimate of price is only valid for 2 to 9 year old car.