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3 years ago

Inferential statistics involves using population data to make inferences about a sample. Group of answer choices True False

Mathematics

1 answer:

zhuklara [117]3 years ago

8 0

Answer:

False. Is the inverse process.

See explanation below.

Step-by-step explanation:

We need to remember two important concepts:

A parameter, is a quantity or value who describe a population desired, for example the population mean $\mu$ or the population standard deviation $\sigma$

A statistic, is a quantity or value who represent the information of the sample data, for example the sample mean $\bar X$ or the sample deviation $s$

Based on this we can analyze the statement:

"Inferential statistics involves using population data to make inferences about a sample"

False. Is the inverse process.

If we know the population data then we indeed have parameters and we don't need to do any type of inference in order to estimate these parameters with the statistics.

What we do generally is use the information from the sample in order to obtain statistics representative of the population with the aim to estimate the parameters unknown of the population

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A park started with 12 oak trees, and the number of oak trees increased at a rate of 25% each year. Write the function that mode

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Answer:

The function which models the situation is as The number of oak trees increase after n year = 12 × $(1+\frac{25}{100})^{n}$

Step-by-step explanation:

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3 years ago

A roulette wheel contains the integers 1 through 36, 0, and 00. suppose that you spin the wheel 6 times and that each time you b

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Each indivitual probability of not winning the is:

(number of not winning outcomes) / (number of possible outcomes) = 37 / 38.

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3 years ago

Suppose it is known that the distribution of purchase amounts by customers entering a popular retail store is approximately norm

Ket [755]

Answer:

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Step-by-step explanation:

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.

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