A phone is originally priced a $40, is discounted to$30.What is the percent of discount?

These two matrices are unable to be added due to dimensional mismatch? True, false, both, unable to answer with the info given

Hoochie [10]

Answer:

false

Step-by-step explanation:

$\left[\begin{array}{c}-4\end{array}\right] +\left[\begin{array}{c}7\end{array}\right] =\left[\begin{array}{c}3\end{array}\right]$

Both of the matrices are 1 x 1 ("one by one"), so they can be added to produce a 1 x 1 matrix.

To add (or subtract) two matrices, they must be the same size.

(m x n) + (m x n) = (m x n)

m x n means a matrix has m rows and n columns. Dimensions are always named in that order: rows, then columns.

6 0

3 years ago

Which is different? Find "both answers.

Basile [38]

Answer:

1) 11

2)275

3)11

Step-by-step explanation:

0.20 x 55= 11

55 x 5=275

0.2*55=11

4 0

3 years ago

HELP ME!!! I BEGG , HELP

julia-pushkina [17]

1.) 9.1 kb
2.) 12.32 cm
3.) 5.2 lb were lime flavor
4.) 132. 53 oz
5.) 10.1 kg
6.) 37.40 calories
7.) 42.70 gigabytes
8.) 13.54 inches

Just simple adding and subtracting. I hope this helps hun!

6 0

4 years ago

Public health officials claim that people living in low income neighborhoods have different Physical Activity Levels (PAL) than

Naddika [18.5K]

Answer:

The z-score for this data is Z = -0.26.

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 normal distributions can be solved using the z-score formula.

In a set with mean $\mu$ and standard deviation $\sigma$ , the z-score 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 p-value, we get the probability that the value of the measure is greater than X.

Central Limit Theorem

The Central Limit Theorem establishes 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 $s = \frac{\sigma}{\sqrt{n}}$ .

For a skewed variable, the Central Limit Theorem can also be applied, as long as n is at least 30.

For a proportion p in a sample of size n, the sampling distribution of the sample proportion will be approximately normal with mean $\mu = p$ and standard deviation $s = \sqrt{\frac{p(1-p)}{n}}$