Can anyone please help me out?? 3rd time posting this question and I really need help!!!!

If B = 0 in a linear equation of the form Ax + By = C, then which statement is also true? A. The graph of this equation is a hor

Vika [28.1K]

Answer:

<h3><u>A) Its horizontal line </u></h3>

Step-by-step explanation:

Ax + By = C

so Ax + (0)y = C

a= c /x

its horizontal

5 0

3 years ago

X+2y=8 in slope intercept form

jeka94

This question is super easy, all you need to do is isolate the y variable. When you want to isolate it, you have to get rid of other numbers by doing the opposite sign.
x+2y=8 Subtract the x.
2y=8-x Divide by two.
y=4-1/2x

3 0

3 years ago

The head of maintenance at XYZ Rent-A-Car believes that the mean number of miles between services is 3639 3639 miles, with a var

Andrei [34K]

Answer:

96.6% probability that the mean of a sample would differ from the population mean by less than 126 miles

Step-by-step explanation:

To solve this question, we need to understand the normal probability distribution and the central limit theorem.

A reminder is that the standard deviation is the square root of the variance.

Normal probability distribution

When the distribution is normal, we use 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 $s = \frac{\sigma}{\sqrt{n}}$ .