Psychology 2812 - Statistics for Psychology II - Midterm Exam
1. Week 1 - lecture 1.1!: Welcome lecture.
2. Keep in mind that statistics is not math or calculations.
How do we define statistics instead?: A logical framework for interpreting data.
3. We use inferential statistics in this course.
Why?: To make conclusions about how the world works (the population) based on our data (the sample).
4. What is the purpose of linear models?: We use linear models of data to describe phenomena.
5. What form of testing do we use to make inferences about the world?: Null hypothesis significance testing.
6. Week 1 - lecture 1.2 & readings!: Data Wrangling & Visualization I - ggplot2
7. Outline the basic structure of a ggplot2 plot.: Import ’ Tidy ’ understand the data through Transform, Visualiz
Model, repeat ’ Communicate.
8. Explain transformation in the context of statistics.: Transformation includes narrowing in on observations of
interest, creating new variables that are functions of existing variables, and calculating a set of summary statistics.
9. Tidying and transforming the data are called .: wrangling.
10.An R is a collection of functions, data, and documentation that extends the capabilities of
R.
You can install the complete tidyverse package with a single line of code. What is this code?: - package.
- install.packages("tidyverse").
11.You will not be able to use the functions, objects, or help files in a package until you load it with what code
every time you open R studio?: library().
12.What are the four reasons why we make plots using code?: - 1. Repeatable.
- 2. Extensible.
- 3. Shareable.
- 4. Durable.
13.What does the ggplot2 system do?: - Its the system we use to make graphs.
- It implements the grammar of graphics, a coherent system for describing and building graphs.
14.How do we define a data frame?: A rectangular collection of variables (in the columns) and observations (in the
rows).
15.Define variables.: Quantity, quality, or property that you can measure.
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, Psychology 2812 - Statistics for Psychology II - Midterm Exam
16.Define values.: - The state of a variable when you measure it.
- The value of a variable may change from measurement to measurement.
17.What is an observation? What is another name for it?: - A set of measure- ments made under similar
conditions.
- An observation contains several values, each associated with a different variable.
- Also referred to as a data point.
18.What is tabular data?: - A set of values, each associated with a variable and an observation.
- Tabular data is tidy if each value is placed in its own "cell", each variable in its own column, and each observation in
its own row.
19.What is a tibble?: Tibble is a package in the R programming language that is used to manipulate and print data
frames.
20.What is the difference between tibbles and data frames?: The most im- portant difference between tibbles
and data frames is the way tibbles print; they are designed for large datasets, so they only show the first few rows and
only the columns that fit on one screen.
21.What does ggplot() do?: Defines a plot object that you then add layers to.
22.What does the mapping argument function do?: Defines how variables are mapped to visual properties.
23.What is a geom?
Give some examples.: - The geometrical object that a plot uses to represent data available using geom .
- Ex. geom bar, geom line, geom boxplot, geom point (scatterplot).
24.What does it mean when you get a warning message like "removed 2 rows containing missing values
(geom_point())"?: This means that two of the objects in our dataset are missing values, therefore ggplot2 has no way of
representing them on the plot.
25.Scatterplots are useful for displaying the relationship between two variables.: numerical.
26.What is scaling?: When a categorical variable is mapped to an aesthetic, ggplot2 will automatically assign a
unique value of the aesthetic to each unique level of the variable.
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, Psychology 2812 - Statistics for Psychology II - Midterm Exam
27.When aesthetic mappings are defined in ggplot(), at the level, they're
passed down to each of the subsequent geom layers of the plot. However, each geom function in ggplot2 can also
take a mapping argument, which allows for aesthetic mappings at the level that are
added to those inherited from the global level.: global; local.
28.Some graphs (ex., scatterplots) plot your values in your dataset.
Others (bar charts, histograms, box plots, smoothers) calculate values. We call these a for short.: - raw.
- new; stat.
29.What are the 7 parameters that, when combined, we can use to make any plot?: You can uniquely describe
any plot as a combination of a dataset, a geom, a set of mappings, a stat, a position adjustment, a coordinate system, and
a faceting scheme.
30.A variable is categorical if it can only take of values.
What type of chart do we use to examine them?: - one small set.
- Bar charts.
31.How do we define numerical variables?
What type of chart do we use to examine them?: - A variable is numerical if it can take on a wide range of numerical
values, and it is sensible to add, subtract, or take averages with those values. They can be continuous or discrete.
- Histograms.
32.For a histogram, in terms of binwidth, if the width is too , it makes it
difficult to determine the shape of the distribution. If it's too , it produces way
less bars, which also makes it difficult to determine the shape.: narrow; high.
33.An alternative is the density plot. What does it do?: This is a smoothed out version of a histogram and a
practical alternative for continuous data that comes from an underlying smooth distribution.
34.Visualizing a relationship requires at least variables mapped to aes- thetics.:
two.
35.We use side by side boxplots or density plots to visualize the relationship between numerical and categorical
variables.
3/
18
1. Week 1 - lecture 1.1!: Welcome lecture.
2. Keep in mind that statistics is not math or calculations.
How do we define statistics instead?: A logical framework for interpreting data.
3. We use inferential statistics in this course.
Why?: To make conclusions about how the world works (the population) based on our data (the sample).
4. What is the purpose of linear models?: We use linear models of data to describe phenomena.
5. What form of testing do we use to make inferences about the world?: Null hypothesis significance testing.
6. Week 1 - lecture 1.2 & readings!: Data Wrangling & Visualization I - ggplot2
7. Outline the basic structure of a ggplot2 plot.: Import ’ Tidy ’ understand the data through Transform, Visualiz
Model, repeat ’ Communicate.
8. Explain transformation in the context of statistics.: Transformation includes narrowing in on observations of
interest, creating new variables that are functions of existing variables, and calculating a set of summary statistics.
9. Tidying and transforming the data are called .: wrangling.
10.An R is a collection of functions, data, and documentation that extends the capabilities of
R.
You can install the complete tidyverse package with a single line of code. What is this code?: - package.
- install.packages("tidyverse").
11.You will not be able to use the functions, objects, or help files in a package until you load it with what code
every time you open R studio?: library().
12.What are the four reasons why we make plots using code?: - 1. Repeatable.
- 2. Extensible.
- 3. Shareable.
- 4. Durable.
13.What does the ggplot2 system do?: - Its the system we use to make graphs.
- It implements the grammar of graphics, a coherent system for describing and building graphs.
14.How do we define a data frame?: A rectangular collection of variables (in the columns) and observations (in the
rows).
15.Define variables.: Quantity, quality, or property that you can measure.
1/
18
, Psychology 2812 - Statistics for Psychology II - Midterm Exam
16.Define values.: - The state of a variable when you measure it.
- The value of a variable may change from measurement to measurement.
17.What is an observation? What is another name for it?: - A set of measure- ments made under similar
conditions.
- An observation contains several values, each associated with a different variable.
- Also referred to as a data point.
18.What is tabular data?: - A set of values, each associated with a variable and an observation.
- Tabular data is tidy if each value is placed in its own "cell", each variable in its own column, and each observation in
its own row.
19.What is a tibble?: Tibble is a package in the R programming language that is used to manipulate and print data
frames.
20.What is the difference between tibbles and data frames?: The most im- portant difference between tibbles
and data frames is the way tibbles print; they are designed for large datasets, so they only show the first few rows and
only the columns that fit on one screen.
21.What does ggplot() do?: Defines a plot object that you then add layers to.
22.What does the mapping argument function do?: Defines how variables are mapped to visual properties.
23.What is a geom?
Give some examples.: - The geometrical object that a plot uses to represent data available using geom .
- Ex. geom bar, geom line, geom boxplot, geom point (scatterplot).
24.What does it mean when you get a warning message like "removed 2 rows containing missing values
(geom_point())"?: This means that two of the objects in our dataset are missing values, therefore ggplot2 has no way of
representing them on the plot.
25.Scatterplots are useful for displaying the relationship between two variables.: numerical.
26.What is scaling?: When a categorical variable is mapped to an aesthetic, ggplot2 will automatically assign a
unique value of the aesthetic to each unique level of the variable.
2/
18
, Psychology 2812 - Statistics for Psychology II - Midterm Exam
27.When aesthetic mappings are defined in ggplot(), at the level, they're
passed down to each of the subsequent geom layers of the plot. However, each geom function in ggplot2 can also
take a mapping argument, which allows for aesthetic mappings at the level that are
added to those inherited from the global level.: global; local.
28.Some graphs (ex., scatterplots) plot your values in your dataset.
Others (bar charts, histograms, box plots, smoothers) calculate values. We call these a for short.: - raw.
- new; stat.
29.What are the 7 parameters that, when combined, we can use to make any plot?: You can uniquely describe
any plot as a combination of a dataset, a geom, a set of mappings, a stat, a position adjustment, a coordinate system, and
a faceting scheme.
30.A variable is categorical if it can only take of values.
What type of chart do we use to examine them?: - one small set.
- Bar charts.
31.How do we define numerical variables?
What type of chart do we use to examine them?: - A variable is numerical if it can take on a wide range of numerical
values, and it is sensible to add, subtract, or take averages with those values. They can be continuous or discrete.
- Histograms.
32.For a histogram, in terms of binwidth, if the width is too , it makes it
difficult to determine the shape of the distribution. If it's too , it produces way
less bars, which also makes it difficult to determine the shape.: narrow; high.
33.An alternative is the density plot. What does it do?: This is a smoothed out version of a histogram and a
practical alternative for continuous data that comes from an underlying smooth distribution.
34.Visualizing a relationship requires at least variables mapped to aes- thetics.:
two.
35.We use side by side boxplots or density plots to visualize the relationship between numerical and categorical
variables.
3/
18
