Is each line parallel, perpendicular, or neither parallel nor perpendicular to a line whose slope is -3/4?

Mathematics

2 answers:

taurus [48]3 years ago

8 0

You need to draw out the slope on a piece of graph paper and u can figure it out

brilliants [131]3 years ago

4 0

In order for a line to be parallel it must have the same slope of -3/4
in order for a line to be perpendicular it must have the inverse of the slope which would be 4/3
if it is neither of the cases listed above it is neither parallel nor perpendicular

You might be interested in

The relationship between antioxidant food consumption per day in cups and the BMI of an individual is modeled by the following l

Anuta_ua [19.1K]

Y is bmi
x is amount of antioxidant in cups

so when x=0 that is when 0 cups of antioxydants are consumed, that is y intercept, that means a person who consumes 0 cups starts at 26 bmi

slope is the amount that the bmi decreases for every cup of antioxydants consumed

7 0

2 years ago

The mean number of words per minute (WPM) read by sixth graders is 97 with a standard deviation of 19 WPM. If 75 sixth graders a

andrey2020 [161]

Answer:

0.0174 = 1.74% probability that the sample mean would be greater than 101.63 WPM

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 $s = \frac{\sigma}{\sqrt{n}}$ ;