Categorical vs Quantitative Variables Practice

Does the visualization represent a categorical​ variable or a quantitative​ variable? What are the data​ points?

• 
  Quantitative​ variable; each data point is a film that was nominated for a prize.

• Categorical​ variable; each data point is a category of film.

• Quantitative​ variable; each data point is the number of films in a category.

• Categorical​ variable; each data point is a film that was nominated for a prize.

Tags

CCSS.HSS.ID.B.5

Does the visualization represent a categorical​ variable or a quantitative​ variable? What are the data​ points?

• Quantitative​ variable; each data point is a type of fuel for a power plant.

• 
  Categorical​ variable; each data point represents a power plant by the amount of capacity it has.

• Quantitative​ variable; each data point represents a power plant by the amount of capacity it has.

• Categorical​ variable; each data point is a type of fuel for a power plant.

Tags

CCSS.HSS.ID.A.1

CCSS.HSS.ID.B.5

Does the visualization represent a categorical​ variable or a quantitative​ variable? What are the data​ points?

• Categorical​ variable; each data point is a unit of energy generated.
  

• 
  Quantitative​ variable; each data point is the amount of energy generated​ (in terawatt​ hours).

• Categorical​ variable; each data point is the amount of energy generated​ (in terawatt​ hours).

• Quantitative​ variable; each data point is a unit of energy generated.

Tags

CCSS.HSS.ID.A.1

Identify the appropriate data for the situation:

A fitness trainer wants to determine a target race time for her trainee to set as a goal.

• race times for the same length race

• race times for all races run that week

• the weather when each race takes place

• lengths of the race for the same race times

Identify if the data should be measured by a categorical variable or a quantitative variable:

A fitness trainer wants to determine a target race time for her trainee to set as a goal.

• categorical

• quantitative

Tags

CCSS.HSS.ID.B.5

Is the total useful when analyzing a categorical​ variable, a quantitative​ variable, or​ both? Explain.

• Quantitative; only quantitative data can have a​ total, which is the total sum.

• 
  ​Both; quantitative data can be analyzed as the total sum of all of the​ data, and categorical data can be analyzed by the total number of data points.

• Quantitative; the total measures values and categorical data does not have values.

• Categorical; only categorical data will have a total number of data points.

For the type of​ data name one categorical variable:

Weekly box office results

• Amount of tickets sold each week

• Snacks purchased by each moviegoer

• Movie attended by each moviegoer

• 
  Total number of sales each week

Tags

CCSS.HSS.ID.B.5

For the type of​ data name one quantitative variable:

Weekly box office results

• Amount of tickets sold each week

• Movie attended by each moviegoer

• Number of movies played each week

• 
  Type of movie attended by each moviegoer

Tags

CCSS.HSS.MD.A.1

In the​ scenario, a data point belongs to multiple categories or none of the expected categories. Make an argument for whether the data should be counted multiple​ times, counted in a separate​ category, or ignored:

A pollster conducts a survey to predict the winner of an election. A respondent plans to write in a candidate not on the ballot.

• 
  It should be counted multiple times. The​ write-in vote can include all of the candidates on the​ ballot, so it will fit into more than one category.

• 
  It should be counted in a separate category. If there are more​ write-in votes than votes for any one candidate on the​ ballot, it will be important to distinguish one​ write-in candidate from another.

• 
  It should be ignored. If they are not voting for a candidate on the​ ballot, it will not be needed to predict the winner because only a candidate can win.

• 
  It should be counted in a separate category. If there are more​ write-in votes than votes for any one candidate on the​ ballot, it will be important to distinguish the total number of​ write-in votes.

In the​ scenario, a data point belongs to multiple categories or none of the expected categories. Make an argument for whether the data should be counted multiple​ times, counted in a separate​ category, or ignored.

A scientist conducts a survey to identify the heritability of eye color. One respondent has a green left eye and a blue right eye.

• 
  It should be counted in a separate category. The person should be counted with the others that have green eyes.

• 
  It should be counted in a separate category. The person should be counted with the others that have blue eyes.

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  It should be counted multiple times. The person has traits that fit two different categories.

• 
  It should be ignored. The person does not fit into any of the existing​ categories, so they should not be counted in the survey.