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3 years ago

What is n in 3/5n + 15 =10+2/5n

2 answers:

8 0

Simplify both sides of the equation.

3/5n + 15 = 2/5n + 10

Subtract 2/5n from both sides.

3/5n + 15 - 2/5n = 2/5n + 10 - 2/5n15n + 15 = 10

Subtract 15 from both sides.

1/5n + 15 - 15 = 10 - 151/5n = -5

Multiply both sides by 5.

5 × (1/5n) = (5) × (−5)n = -25

Answer: n is -25.

den301095 [7]3 years ago

7 0

The answer is n is -25

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Help I don’t understand the concept

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Answer:

C. No, because one x-value corresponds to two different y-values

Step-by-step explanation:

An x-value cannot have two y-values. Hope this helps!

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2 years ago

A sample of size 6 will be drawn from a normal population with mean 61 and standard deviation 14. Use the TI-84 Plus calculator.

AVprozaik [17]

Answer:

$X \sim N(61,14)$

Where $\mu=61$ and $\sigma=14$

Since the distribution for X is normal then the distribution for the sample mean is also normal and given by:

$\bar X \sim N(\mu, \frac{\sigma}{\sqrt{n}})$

$\mu_{\bar X} = 61$

$\sigma_{\bar X}= \frac{14}{\sqrt{6}}= 5.715$

So then is appropiate use the normal distribution to find the probabilities for $\bar X$

Step-by-step explanation:

Previous concepts

Normal distribution, is a "probability distribution that is symmetric about the mean, showing that data near the mean are more frequent in occurrence than data far from the mean".

The Z-score is "a numerical measurement used in statistics of a value's relationship to the mean (average) of a group of values, measured in terms of standard deviations from the mean". The letter $\phi(b)$ is used to denote the cumulative area for a b quantile on the normal standard distribution, or in other words: $\phi(b)=P(z$

Solution to the problem

Let X the random variable that represent the variable of interest of a population, and for this case we know the distribution for X is given by:

$X \sim N(61,14)$

Where $\mu=61$ and $\sigma=14$

Since the distribution for X is normal then the distribution for the sample mean $\bar X$ is also normal and given by:

$\bar X \sim N(\mu, \frac{\sigma}{\sqrt{n}})$

$\mu_{\bar X} = 61$

$\sigma_{\bar X}= \frac{14}{\sqrt{6}}= 5.715$

So then is appropiate use the normal distribution to find the probabilities for $\bar X$

8 0

3 years ago

The variance can never bea.zerob.larger than the standard deviationc.negatived.smaller than the standard deviation

MrRa [10]

Answer:

c.negative

True, by definition since the variance take in count the differences around the mean squared, then we can't have a negative value for a sum of square values.

Step-by-step explanation:

We need to remember that the variance is a measure of dispersion for a dataset respect to a measure of central tendency called the mean. The mean is defined as:

$\bar x = \frac{\sum_{i=1}^n X_i}{n}$

And the population mean is given by:

$\mu= \frac{\sum_{i=1}^n X_i}{N}$

The population variance is defined by:

$\sigma^2 = \frac{\sum_{i=1}^n (X_i -\mu)^2}{N}$

And the sample variance is defined as:

$s^2= \frac{\sum_{i=1}^n (X_i -\bar x)}{n-1}$

Now if we analyze the options we have this:

a.zero

False, if all the values are the same then the mean would be the same to the values and the difference between each value and the mean would 0 and indeed the variance would be 0.

b.larger than the standard deviation

False, if the population standard deviation is $\sigma=2$ then the variance would be $\sigma^2 = 4$ and as we can see is higher than the standard deviation

c.negative

True, by definition since the variance take in count the differences around the mean squared, then we can't have a negative value for a sum of square values.

d.smaller than the standard deviation

False, if the population standard deviation is $\sigma=0.1$ then the variance would be $\sigma^2 = 0.01$ and as we can see is lower than the standard deviation