i put it in coments sence it says it is inapropreait
First we need slope


Put D co-ordinates on y=mx+b




Now
slope intercept form.

100,000 equals 10 to the power of 5, hope helps.
Answer:
84 sq meters
Step-by-step explanation:
1. Approach
In order to solve this problem, one will have to divide the figure up into simple shapes. A picture is attached showing how the shape is divided up for this answer. Find the area of each region, then add up the results to find the total area.
2. Area of Region 1
As one can see, the length of (Region 1), as given is (6), the width is (3). To find the area multiply the length by the width.
Length * width
6 * 3
= 18
3. Area of Region 2
In (Region 2), the length is given, (12). However, one must find the width, this would be the size of the total side, minus the width of (Region 1). Multiply the length by the side to find the area.
Length * width
= 12 * (8 - 3)
= 12 * 5
= 60
4. Area of Region 3
In (Region 3), the length of the figure is (2), the width is (3). To find the area, multiply the length by the width.
Length * width
= 2 * 3
= 6
5. Total area
Now add up the area of each region to find the total rea,
(Region 1) + (Region 2) + ( Region 3)
= 18 + 60 + 6
= 84
Answer:
The probability table is shown below.
A Poisson distribution can be used to approximate the model of the number of hurricanes each season.
Step-by-step explanation:
(a)
The formula to compute the probability of an event <em>E</em> is:

Use this formula to compute the probabilities of 0 - 8 hurricanes each season.
The table for the probabilities is shown below.
(b)
Compute the mean number of hurricanes per season as follows:

If the variable <em>X</em> follows a Poisson distribution with parameter <em>λ</em> = 7.56 then the probability function is:

Compute the probability of <em>X</em> = 0 as follows:

Compute the probability of <em>X</em> = 1 as follows:

Compute the probabilities for the rest of the values of <em>X</em> in the similar way.
The probabilities are shown in the table.
On comparing the two probability tables, it can be seen that the Poisson distribution can be used to approximate the distribution of the number of hurricanes each season. This is because for every value of <em>X</em> the Poisson probability is approximately equal to the empirical probability.