Select Parameter or Statistic to classify each statement.

Mathematics

2 answers:

Firdavs [7]3 years ago

8 0

Answer:

Statistic

Parameter

Statistic

Parameter

Step-by-step explanation:

i took the test :)

Annette [7]3 years ago

7 0

Answer:

1) Statistic

2) Parameter

3) Statistic

4) Parameter.

Step-by-step explanation:

Parameter: is a number that describes the whole population.

Statistic: is a number that describes a sample.

The difference is:

50% of the cats are nice (parameter, because talks about all the cats)

out of 100 cats surveyed, 39 are nice (statistic, because talks about a sample of 100 cats)

Then:

1) 85% of those surveyed were in favor of bike lanes.

This only refers to the surveyed people, so this only refers to a sample (and not the population that this sample represents) then this is a statistic statement.

2) Each member of the soccer team has an average of 2 jerseys.

In this case, we are talking about the whole population (the soccer team) then this is a parameter

3) Of the 100 swim teams surveyed across America, 23 practice 10 hours a week.

Here the information is about the sample (only 100 swim teams). So here the correct option is statistic.

4) The golf team's average score is 79

Here the information is about the average score for the entire golf team, so this information is about all the population in the golf team, then in this case the correct option is parameter.

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The average breaking strength of a certain brand of steel cable is 2000 pounds, with a standard deviation of 100 pounds. A sampl

Westkost [7]

Answer:

1963.2 pounds (lbs.)

Step-by-step explanation:

Things to understand before solving:

- <u>Normal Probability Distribution</u>

The z-score formula can be used to solve normal distribution problems. In a set with mean ц and standard deviation б, the z-score of a measure X is given by: $Z=\frac{X-u}{a}$

The Z-score reflects how far the measure deviates from the mean. After determining the Z-score, we examine the z-score table to determine the p-value associated with this z-score. This p-value represents the likelihood that the measure's value is less than X, or the percentile of X. Subtracting 1 from the p-value yields the likelihood that the measure's value is larger than X.

- <u>Central Limit Theorem</u>

The Central Limit Theorem establishes that, for a normally distributed random variable X, with mean ц and standard deviation б , the sampling distribution of the sample means with size n can be approximated to a normal distribution with mean ц and standard deviation $s=\frac{a}{\sqrt{n} }$

As long as n is more than 30, the Central Limit Theorem may be applied to a skewed variable. A specific kind of steel cable has an average breaking strength of 2000 pounds, with a standard variation of 100 pounds.

This means, ц = 2000 and б = 100.

A random sample of 20 cables is chosen and tested.

This means that n = 20, $s=\frac{100}{\sqrt{120} } =22.361$