Q: A batch contains 100,000 light bulbs. You inspect 10,000 and find 11% are defective. Which formula would you use to estimate the population proportion of defective bulbs?

$$ \hat{p} = \frac{\text{number of defective bulbs in sample}}{\text{sample size}} $$

Question 1

Which of the following best describes the difference between a parameter and a statistic?

Accepted Answer

A parameter is a numerical characteristic of a population, while a statistic is a numerical characteristic of a sample.

Question 2

A researcher wants to estimate the average weight of all newborn babies in Canada. She randomly selects 200 newborns and records their weights. Which of the following is the population?

Accepted Answer

All newborn babies in Canada.

Question 3

Which of the following variables is an example of a discrete quantitative variable?

Accepted Answer

Number of defective items in a batch.

Question 4

In the context of statistics, what is an experimental unit?

Accepted Answer

An object about which data are collected.

Question 5

A taste tester ranks four brands of soda as 'best', 'worst', etc. for a panel of 12 testers. What type of data is this?

Accepted Answer

Categorical, ordinal

Question 6

Which of the following is NOT one of the Five W’s used to provide context for data?

Accepted Answer

Which

Question 7

A sample of 100 medical school applicants is scored on the MCAT. Which of the following best describes the MCAT score variable?

Accepted Answer

Quantitative, discrete

Question 8

Suppose a TV executive samples 500 viewers to estimate the average age of all CTV News viewers. Which of the following is the statistic?

Accepted Answer

The average age of the 500 sampled viewers.

Question 9

Which of the following statements about categorical data is TRUE?

Accepted Answer

Categorical data can be either nominal or ordinal.

Question 10

A researcher records the pH of a chemical substance, which can be any value from 0 to 14. What type of variable is pH?

Accepted Answer

Quantitative, continuous

Question 11

Which of the following is an example of a measure of reliability in statistics?

Accepted Answer

A statement about the degree of uncertainty associated with a statistical inference.

Question 12

In a Consumer Reports article about tablets, the variable 'manufacturer' is best classified as:

Accepted Answer

Categorical, nominal

Question 13

Which of the following best describes the process of statistical inference?

Accepted Answer

Making estimates or predictions about a population based on information contained in a sample.

Question 14

A variable is measured as 'income level' with categories: 'less than 50K', '50K-100K', 'over 100K'. What type of variable is this?

Accepted Answer

Categorical, ordinal

Question 15

Which of the following is a correct example of transforming a quantitative variable into a categorical variable?

Accepted Answer

Recording age in years as 'Child', 'Teen', 'Adult', or 'Senior'.

Question 16

Which of the following is NOT an element of a descriptive statistical problem?

Accepted Answer

A measure of reliability of the inference.

Question 17

A batch contains 100,000 light bulbs. You inspect 10,000 and find 11% are defective. Which formula would you use to estimate the population proportion of defective bulbs?

Accepted Answer

$$ \hat{p} = \frac{	ext{number of defective bulbs in sample}}{	ext{sample size}} $$

Question 18

Which of the following best describes a continuous quantitative variable?

Accepted Answer

A variable whose possible values consist of an entire interval on the real number line.

Question 19

In a medical study, researchers report the mean and standard deviation for the weights of 20 experimental rats. Which formula would you use to calculate the sample mean?

Accepted Answer

$$ \bar{y} = \frac{1}{n} \sum_{i=1}^n y_i $$

Question 20

Which of the following is a key reason why the method of data collection ('How') is important in statistics?

Accepted Answer

It determines whether the data provide insight or nonsense.

Introduction to Data and Fundamental Statistical Concepts

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