0.1 x 1.6 decimals xd

What is the value of x in this equation?

andre [41]

The value of x is
-4x + 8 = 42
-4x = 42 -8
-4x = 34
x = -8,5

8 0

3 years ago

Assume that thermometer readings are normally distributed with a mean of degrees and a standard deviation of 1.00degrees C. A th

Andrew [12]

Answer:

0.2684 is the probability that the temperature reading is between 0.50 and 1.75.

Step-by-step explanation:

We are given the following information in the question:

Mean, μ = 0 degrees

Standard Deviation, σ = 1 degrees

We are given that the distribution of thermometer readings is a bell shaped distribution that is a normal distribution.

Formula:

$z_{score} = \displaystyle\frac{x-\mu}{\sigma}$

P(Between 0.50 degrees and 1.75 degrees)

$P(0.50 \leq x \leq 1.75)\\\\ = P(\displaystyle\frac{0.50 - 0}{1} \leq z \leq \displaystyle\frac{1.75-0}{1})\\\\ = P(0.50 \leq z \leq 1.75)\\= P(z \leq 1.75) - P(z < 0.50)\\= 0.9599 - 0.6915 = 0.2684 = 26.84\%$

0.2684 is the probability that the temperature reading is between 0.50 and 1.75.

7 0

4 years ago

60in in feet Pls help fast !

PolarNik [594]

Answer:

5 feet

Step-by-step explanation:

Divide 60 inches by 12

Your answer is 5 feet.

Hope this helped :)

6 0

3 years ago

A manufacturer knows that their items have a normally distributed length, with a mean of 15.4 inches, and standard deviation of

Masteriza [31]

Answer:

0.9452 = 94.52% probability that their mean length is less than 16.8 inches.

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