STA2010 Week 1 Quiz Answers
Help with question 1 (Definition apply question)
Explanatory Variable (Independent Variable): This is a variable in a
statistical analysis that is manipulated or controlled by the researcher. It is
used to explain or predict changes in the dependent variable. In
experiments, researchers intentionally change the values of the explanatory
variable to see how it affects the outcome. In regression analysis, the
explanatory variable is used to model or explain the variation in the
dependent variable.
Categorical Variable: A categorical variable, also known as a qualitative or
nominal variable, is a type of variable that represents categories or groups.
It consists of distinct categories or labels, and each observation or data
point falls into one of these categories. Categorical variables are often used
to represent non-numeric data, such as gender, colors, types of cars, or
educational levels. They are typically analyzed using techniques like
frequency counts, cross-tabulations, and chi-square tests.
Experimental Variable: An experimental variable is a specific type of
explanatory variable used in experimental research. It refers to the variable
that the researcher intentionally manipulates or changes in an experiment
to observe its effect on the dependent variable. The experimental variable
is crucial for conducting controlled experiments where the goal is to
establish cause-and-effect relationships. For example, in a drug efficacy
study, the dosage of the drug administered would be the experimental
variable.
Response Variable (dependent variable): A variable that is possibly affected
by another variable is called a response variable. The response variable is
what researchers measure or observe in response to changes or variations
in another variable, known as the independent variable. Researchers
analyze the relationship between the independent variable and the response
variable to determine whether changes in the independent variable have an
effect on the response variable. This is also known as the dependent
variable.
Help with question 2 (Definition apply question)
Discrete Numerical Data:
, Movie Rankings: The rankings themselves (1st, 2nd, 3rd, etc.) are examples
of discrete numerical data. These rankings are whole numbers that
represent the positions of the movies in the list. Discrete data consists of
distinct, separate values with no intermediate values between them. In this
case, the rankings are countable and distinct, making them discrete.
Number of Movies in the Top 10 List: The count of movies in the top 10 list
is another example of discrete numerical data. It represents the total
number of movies in the list, which is a whole number.
Continuous Numerical Data:
Box Office Revenue: Although the post does not provide specific revenue
figures, box office revenue for movies is an example of continuous
numerical data. Continuous data can take any value within a certain range,
and revenue can be measured in dollars and cents, allowing for infinitely
fine gradations. For example, a movie could earn $100,000, $100,001,
$100,001.50, and so on. It has a continuous range of possible values.
Nominal Data:
Movie Titles: The names of the movies themselves, such as "Avatar,"
"Avengers: Endgame," and "Titanic," represent nominal data. Nominal data
consists of categories or labels that do not have a natural order or ranking.
In this case, the movie titles are labels used to identify different films, and
there is no inherent order or numeric value associated with them in the
context of this list.
Ordinal Data:
You read a list of the top 10 bestselling novels of all time. You are not
provided with the number of copies sold but only their rankings in the list.
This scenario also falls into the ordinal data category. In this case, the
rankings of the novels (1st, 2nd, 3rd, etc.) represent an ordered sequence
based on their popularity or sales, but the numerical difference between
the rankings does not convey consistent or meaningful information about
the actual number of copies sold for each novel. The order matters, but the
gaps between the ranks are not necessarily equal or indicative of specific
quantities.
Help with question 3 (Definition apply question)
Help with question 1 (Definition apply question)
Explanatory Variable (Independent Variable): This is a variable in a
statistical analysis that is manipulated or controlled by the researcher. It is
used to explain or predict changes in the dependent variable. In
experiments, researchers intentionally change the values of the explanatory
variable to see how it affects the outcome. In regression analysis, the
explanatory variable is used to model or explain the variation in the
dependent variable.
Categorical Variable: A categorical variable, also known as a qualitative or
nominal variable, is a type of variable that represents categories or groups.
It consists of distinct categories or labels, and each observation or data
point falls into one of these categories. Categorical variables are often used
to represent non-numeric data, such as gender, colors, types of cars, or
educational levels. They are typically analyzed using techniques like
frequency counts, cross-tabulations, and chi-square tests.
Experimental Variable: An experimental variable is a specific type of
explanatory variable used in experimental research. It refers to the variable
that the researcher intentionally manipulates or changes in an experiment
to observe its effect on the dependent variable. The experimental variable
is crucial for conducting controlled experiments where the goal is to
establish cause-and-effect relationships. For example, in a drug efficacy
study, the dosage of the drug administered would be the experimental
variable.
Response Variable (dependent variable): A variable that is possibly affected
by another variable is called a response variable. The response variable is
what researchers measure or observe in response to changes or variations
in another variable, known as the independent variable. Researchers
analyze the relationship between the independent variable and the response
variable to determine whether changes in the independent variable have an
effect on the response variable. This is also known as the dependent
variable.
Help with question 2 (Definition apply question)
Discrete Numerical Data:
, Movie Rankings: The rankings themselves (1st, 2nd, 3rd, etc.) are examples
of discrete numerical data. These rankings are whole numbers that
represent the positions of the movies in the list. Discrete data consists of
distinct, separate values with no intermediate values between them. In this
case, the rankings are countable and distinct, making them discrete.
Number of Movies in the Top 10 List: The count of movies in the top 10 list
is another example of discrete numerical data. It represents the total
number of movies in the list, which is a whole number.
Continuous Numerical Data:
Box Office Revenue: Although the post does not provide specific revenue
figures, box office revenue for movies is an example of continuous
numerical data. Continuous data can take any value within a certain range,
and revenue can be measured in dollars and cents, allowing for infinitely
fine gradations. For example, a movie could earn $100,000, $100,001,
$100,001.50, and so on. It has a continuous range of possible values.
Nominal Data:
Movie Titles: The names of the movies themselves, such as "Avatar,"
"Avengers: Endgame," and "Titanic," represent nominal data. Nominal data
consists of categories or labels that do not have a natural order or ranking.
In this case, the movie titles are labels used to identify different films, and
there is no inherent order or numeric value associated with them in the
context of this list.
Ordinal Data:
You read a list of the top 10 bestselling novels of all time. You are not
provided with the number of copies sold but only their rankings in the list.
This scenario also falls into the ordinal data category. In this case, the
rankings of the novels (1st, 2nd, 3rd, etc.) represent an ordered sequence
based on their popularity or sales, but the numerical difference between
the rankings does not convey consistent or meaningful information about
the actual number of copies sold for each novel. The order matters, but the
gaps between the ranks are not necessarily equal or indicative of specific
quantities.
Help with question 3 (Definition apply question)
