<h3>
Answer: Point D</h3>
Explanation:
The origin is the center of the grid. This is where the x and y axis meet. The location of this point is (0,0).
Start at the origin and move 5 places to the right. Note how the x coordinate is 5 which tells us how to move left/right. Positive x values mean we go right.
Then we go down 2 spots to arrive at point D. We move down because the y coordinate is negative.
You could also start at (0,0) and go down 2 first, then to the right 5 to also arrive at point D. Convention usually has x going first as (x,y) has x listed first.
Answer:
9, 32 and 36
Step-by-step explanation:
The perimeter of a triangle is the sum of all the sides. Since you know the perimeter and the expressions for each of the sides, you can set up an equation to solve for the variable and find the values of the other sides:
one side: '4 times the shortest side' = 4s
shortest side: 's'
third side: '23 more than the shortest side' = s + 23
perimeter = 77
Equation: 4s + s + (s + 23) = 77
Combine like terms: 6s + 23 = 77
Subtract 23 from both sides: 6s + 23 - 23 = 77 - 23 or 6s = 54
Divide by 6: 6s/6 = 54/6 or s = 9
one side: 4s = 4(9) = 36
shortest side: s = 9
third side: s + 23 = 9 + 23 = 32
Answer:
m1 = 44 m2 = 46
Step-by-step explanation:
On the opposite side of the 1, there is a 44
The 1 and 44 seem like the same angle so m1 is 44
Rectangles have four right angles which measure as 90°
90-44 = 46
<u>Please </u><u>give </u><u>brainliest </u><u>if </u><u>I </u><u>helped!</u><u> </u><u>:</u><u>)</u>
Answer:
It is a good Estimator of the Population Mean because the distribution of the sample midrange is just same as the distribution of the random variable.
Step-by-step explanation: from the table,
Minimum value = 34
maximum values = 1084
The sample mid-range can be computed as:
(Min.value + max.value)/2
(34 + 1084)/2
Sample mid-range = 55
The sample midrange uses only a small portion of the data, but can be heavily affected by outliers.
It provides information about the skewness and heavy-tailedness of the distribution which is just same as the distribution of the random variable.
The nature of this distribution is not intuitive but the Central Limit in which it will approach a normal distribution for large sample size.