Answer:

The selling price of house is approximately 270.4264 thousand dollars.
Step-by-step explanation:
We are given the following in the question:
A linear model gives the relation between natural logarithm of price, in thousands of dollars, and size, in 100 square feet.

Let p be the price and s be the size.

We have to approximate the selling price for a house with a size of 3,200 square feet.
Thus, we put s = 32

Thus, the selling price of house is approximately 270.4264 thousand dollars.
The spinner is divided into 4 equal sections number 1 to 4.
So, for spinner, total sections = 4
Favorable sections = 2 (i.e sections with even numbers)
So, probability of getting even number on the spinner = 2/4 = 1/2
Total number of outcome when a dice is rolled = 6
Favorable outcomes= 3 (i.e outcomes with 2,4 and 6)
So, probability of getting an even number = 3/6 = 1/2
Since both events are independent, we can write:
The probability of getting an even number in both events = 1/2 x 1/2 = 1/4
Answer:

Step-by-step explanation:
First of all, we calculate v and a as:


after that, we compute the cross product and we replace in the formula for k
a(t) X v(t) = (0,0,2)
| a(t) X v(t) | = 2

Hence we have

I hope this is useful for you
regards
Answer:
<em>Find the probability of success in a single trial and then think about the nature of the problem (when do we stop). </em>
Step-by-step explanation:
Observe that in the single trial, we have (8 4) possibilities of choosing our set of balls. If we have chosen two white balls and two black balls, the probability of doing that is simply
p=(4 2)*(4 2)/(8 4)
This is well know Hyper geometric distribution. Now, define random variable X that marks the number of trials that have been needed to obtain the right combination (two white and two black balls). From the nature of the problem, observe that X has Geometric distribution with parameter p that has been calculated above. Hence
P(X = n) = (1— p)^n-1 *( p )
<em>Find the probability of success in a single trial and then think about the nature of the problem (when do we stop). </em>