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pentagon [3]
3 years ago
7

Question is attached.Step by step explanation only!​

Mathematics
2 answers:
Arada [10]3 years ago
6 0

LHS

\\ \sf\longmapsto \dfrac{cos(90-\Theta)}{sin\Theta}+\dfrac{sin\Theta}{cos(90-\Theta)}

\boxed{\sf cos(90-A)=sinA}

\\ \sf\longmapsto \dfrac{sin\Theta}{sin\Theta}+\dfrac{sin\Theta}{sin\Theta}

\\ \sf\longmapsto 1+1

\\ \sf\longmapsto 2

Hence proved

Anna11 [10]3 years ago
4 0

Answer:

Attached is your answer.

Step-by-step explanation:

  • hence Proved.

Hope it helps you!!

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After a special medicine is introduced into a petri dish full of bacteria, the number of bacteria remaining in the dish decrease
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The amount of coffee that a filling machine puts into an 8-ounce jar is normally distributed with a mean of 8.2 ounces and a sta
nordsb [41]

Answer:

73.30% probability that the sampling error made in estimating the mean amount of coffee for all 8-ounce jars by the mean of a random sample of 100 jars will be at most 0.02 ounce

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}}.

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

In this problem, we have that:

\mu = 8.2, \sigma = 0.18, n = 100, s = \frac{0.18}{\sqrt{100}} = 0.018

What is the probability that the sampling error made in estimating the mean amount of coffee for all 8-ounce jars by the mean of a random sample of 100 jars will be at most 0.02 ounce?

This is the pvalue of Z when X = 8.2 + 0.02 = 8.22 subtracted by the pvalue of Z when X = 8.2 - 0.02 = 8.18. So

X = 8.22

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

By the Central Limit Theorem

Z = \frac{X - \mu}{s}

Z = \frac{8.22 - 8.2}{0.018}

Z = 1.11

Z = 1.11 has a pvalue of 0.8665

X = 8.18

Z = \frac{X - \mu}{s}

Z = \frac{8.18 - 8.2}{0.018}

Z = -1.11

Z = -1.11 has a pvalue of 0.1335

0.8665 - 0.1335 = 0.7330

73.30% probability that the sampling error made in estimating the mean amount of coffee for all 8-ounce jars by the mean of a random sample of 100 jars will be at most 0.02 ounce

8 0
3 years ago
By what percent will the product of two numbers change, if the first number increases by 70%, while the second number decreases
FinnZ [79.3K]

Answer:

2%

Step-by-step explanation:

Let x be the first number

It increases by 70 %

The new number is

m = x+ .70x

   = 1.7x

Let y be the second number

It decreases by 40 %

The new number is

n =y - .40 y

  = .6y


The product of the original numbers is xy

The product of the new number is

mn = (1.7x * .6y) = 1.02xy

The new number is larger than the old number so it is an increase.

Percent increase is  new - original divided by original  times 100%

Percent increase = (1.02 xy - xy)

                                -----------------  * 100%

                                 xy  

                           = .02 xy

                                ------- * 100 %

                                 xy  

                            = .02 * 100 %

                             = 2%



5 0
3 years ago
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