Applying descriptive statistics and data visualization to the training set to understand the data and assist in the selection of
1 answer:
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Answer:
C. Those who have a car tend to have a cell phone.
Step-by-step explanation:
The relative frequency for a data set n is calculated by dividing each frequency by n.
We have a table that relates the use of cars and cell phones.
First, we take from the table the population that has a car.
Then, of the students who have a car, 12 have a cell phone and 6 do not have a cell phone.
Then we calculate the relative frequencies for those who have a cell phone and those who do not.
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Relative frequency Students with car n = 18.
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C<em>ell phone No cell phone Total n</em>
12/18 6/18 18
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Relative frequency Students with car n = 18.
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C<em>ell phone No cell phone Total n</em>
0.666 0.333 18
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Most students who have a car also have a cell phone.
Now we calculate the relative frequency for students who do not have a car.
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Relative frequency Students without a car n = 7
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<em>Cell phone No cell phone Total n</em>
2/7 5/7 7
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Relative frequency Students without a car n = 7
-------------------------------------------------- --------------------------------------
<em>Cell phone</em> <em> No</em> <em>cell phone Total n</em>
0.286 0.714 7
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Most students who do not have a car do not have a cell phone either.
<em>Then these data suggest that. Those who have a car tend to have a cell phone.</em>
Option C
Answer:
It is a good Estimator of the Population Mean because the distribution of the sample midrange is just same as the distribution of the random variable.
Step-by-step explanation: from the table,
Minimum value = 34
maximum values = 1084
The sample mid-range can be computed as:
(Min.value + max.value)/2
(34 + 1084)/2
Sample mid-range = 55
The sample midrange uses only a small portion of the data, but can be heavily affected by outliers.
It provides information about the skewness and heavy-tailedness of the distribution which is just same as the distribution of the random variable.
The nature of this distribution is not intuitive but the Central Limit in which it will approach a normal distribution for large sample size.
