The equation of a line is given below.

Mathematics

1 answer:

Alex_Xolod [135]3 years ago

4 0

Step-by-step explanation:

Given equation:

- 6x - 2y = 2

Simplify and convert into slope-intercept form (y = mx + b):

- 6x - 2y = 2
-3x - y = 1
y = - 3x - 1

The slope is m = -3, the y-intercept is b = -1

See the graph below

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Last year, a person wrote 126 checks. Let the random variable x represent the number of checks he wrote in one day, and assume

Arisa [49]

Answer: The mean number of checks written per day $=0.3452$

Standard deviation $=0.5875$

Variance $=0.3452$

Step-by-step explanation:

Given : The total number of checks wrote by person in a year = 126

Assume that the year is not a leap year.

Then 1 year = 365 days

Let the random variable x represent the number of checks he wrote in one day.

Then , the mean number of checks wrote by person each days id=s given by :-

$\lambda=\dfrac{126}{365}\approx0.3452$

Since , the distribution is Poisson distribution , then the variance must equal to the mean value i.e. $\sigma^2=\lambda=0.3452$

Standard deviation : $\sigma=\sqrt{0.3452}=0.5875372328\approx0.5875$

6 0

4 years ago

The mean rent of a 3-bedroom apartment in Orlando is $1300. You randomly select 10 apartments around town. The rents are normall

lana [24]

Answer:

96.49% probability that the mean rent is more than $1100

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 $\mu$ and standard deviation $\sigma$ , the zscore of a measure X is given by:

$Z = \frac{X - \mu}{\sigma}$

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 $\mu$ and standard deviation $\sigma$ , the sampling distribution of the sample means with size n can be approximated to a normal distribution with mean $\mu$ and standard deviation $s = \frac{\sigma}{\sqrt{n}}$ .