Answer:
The second option will cost her less than the first one.
Step-by-step explanation:
In order to solve this problem we will create two functions to represent the cost of the car in function of the miles drove by her.
For the first option we have:
For the second option we have:
Since she intends to drive it for 10,000 miles per year for 6 years, then the total mileage she intends to drive her car is 60,000 miles. Applying this to the formula of each car and we have:
The second option will cost her less than the first one.
This is a question that can be solved with Pythagorean Theorem since it is a right triangle. The longest side, 29, is the hypotenuse and the sides 21 and x are the legs. So, the Pythagorean Theorem states,
Where,
c is the Hypotenuse, a and b are the legs. Putting the respective values gives us,
So, 20 is the side length of x.
ANSWER: 20
If the value of the z-score is 1. Then the probability that a cat will weigh less than 11 pounds will be 0.84134.
<h3>What is the z-score?</h3>
The z-score is a statistical evaluation of a value's correlation to the mean of a collection of values, expressed in terms of standard deviation.
The z-score is given as
z = (x - μ) / σ
Where μ is the mean, σ is the standard deviation, and x is the sample.
The weight of a cat is normally distributed with a mean of 9 pounds and a standard deviation of 2 pounds.
Then the probability that a cat will weigh less than 11 pounds will be
The value of z-score will be
z = (11 – 9) / 2
z = 1
Then the probability will be
P(x < 11) = P(z < 1)
P(x < 11) = 0.84134
Thus, the probability that a cat will weigh less than 11 pounds will be 0.84134.
More about the z-score link is given below.
brainly.com/question/15016913
#SPJ1
Answer:
Answer = d. Chi-Square Goodness of Fit
Step-by-step explanation:
A decision maker may need to understand whether an actual sample distribution matches with a known theoretical probability distribution such as Normal distribution and so on. The Goodness-of-fit Test is a type of Chi-Square test that can be used to determine if a data set follows a Normal distribution and how well it fits the distribution. The Chi-Square test for Goodness-of-fit enables us to determine the extent to which theoretical probability distributions coincide with empirical sample distribution. To apply the test, a particular theoretical distribution is first hypothesized for a given population and then the test is carried out to determine whether or not the sample data could have come from the population of interest with hypothesized theoretical distribution. The observed frequencies or values come from the sample and the expected frequencies or values come from the theoretical hypothesized probability distribution. The Goodness-of-fit now focuses on the differences between the observed values and the expected values. Large differences between the two distributions throw doubt on the assumption that the hypothesized theoretical distribution is correct and small differences between the two distributions may be assumed to be resulting from sampling error.
Ok this is the answer to this question 2(x+1)
