Select two symbols that represent a statistic.
1 answer:
<h3>Answers: Choice B and Choice C</h3>
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Explanation:
The symbol or mu represents the population mean, which is a parameter.
The symbol or sigma represents the population standard deviation, which is also a parameter. The same goes for the symbol p, without any hat on top, as it is the population proportion.
If something represents a population data item, then it's automatically a parameter. It might help to think both words "population" and "parameter" start with the letter P.
Based on those previous paragraphs, we can rule out choices A, D and E. Those three items are parameters. This leaves <u>choices B and C</u> as our final answers.
or xbar is a sample statistic, and it represents an estimate of the population mean. Similarly,
or p-hat is the sample proportion and it represents an estimate of the population proportion. These are considered unbiased estimators.
Another example of an unbiased estimator is the variable s which represents the sample standard deviation and it estimates the value of sigma. All of these estimators have one thing in common: they are based on a sample, which in turn tries to predict what the corresponding population value is. In other words, the statistic's job is to estimate the parameter.
Below is a reference table for all of the items mentioned.
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The statement that is a correct interpretation of the word strong in the association is B. the actual time required to deliver all the items to the baggage claim area based on the number of models that are unloaded will be very close to the time predicted by a least square model.
- A linear relationship helps in showing the relationship that exists between two variables.
- A scatter plot is vital as it is a graph that helps in showing the relationship for two numerical values. Apart from the relationship between the variables, it also helps in plotting the data points.
- Since the scatterplot of the two variables indicated a strong, positive linear association, it means the changes that occur to the variables are almost the same. For example, if one of the variables increases by two, then the other variable increases by two also.
In conclusion, the actual time required to deliver all the items to the baggage claim area will be identical to the time predicted by a least square model.
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