Answer:
Step-by-step explanation:
Given a triangle has points:
(-5,1),(2,1), (2,-1)
Let us label the points:
A(2,1),
B(-5,1) and
C(2,-1)
To find:
Distance between (−5, 1) and (2, −1) i.e. BC.
Horizontal leg AB and
Vertical leg, AC.
Solution:
Please refer to the attached diagram for the labeling of the points on xy coordinate plane.
We can simply use Distance formula here, to find the distance between two coordinates.
Distance formula :
For BC:
Horizontal leg, AC:
Vertical Leg, AB:
You will need 3.333.... cups of rice to make 10 servings of casserole.
<u><em>Explanation</em></u>
The recipe which makes 6 servings of casserole, needs 2 cups of rice.
Suppose, you need cups of rice for making 10 servings of casserole.
Now, the equation according to <u>the ratio of "cups of rice" to the "servings of casserole" </u>will be.....
So, you will need 3.333.... cups of rice to make 10 servings of casserole.
Answer:
35.2% probability that the sample mean will be 246 pages or more
Step-by-step explanation:
To solve this question, we need to understand the normal probability distribution and the central limit theorem.
Normal probability distribution
Problems of normally distributed samples are solved using the z-score formula.
In a set with mean and standard deviation
, the zscore of a measure X is given by:
The Z-score measures how many standard deviations the measure is from the mean. After finding the Z-score, we look at the z-score table and find the p-value associated with this z-score. This p-value is the probability that the value of the measure is smaller than X, that is, the percentile of X. Subtracting 1 by the pvalue, we get the probability that the value of the measure is greater than X.
Central Limit Theorem
The Central Limit Theorem estabilishes that, for a normally distributed random variable X, with mean and standard deviation
, the sampling distribution of the sample means with size n can be approximated to a normal distribution with mean
and standard deviation
.
For a skewed variable, the Central Limit Theorem can also be applied, as long as n is at least 30.
In this question, we have that:
What the probability that the sample mean will be 246 pages or more?
This is 1 subtracted by the pvalue of Z when X = 246. So
By the Central Limit Theorem
has a pvalue of 0.6480.
1 - 0.6480 = 0.3520
35.2% probability that the sample mean will be 246 pages or more