R Dataset / Package Stat2Data / Milgram

How To Create a Barplot

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Description

Describes how to create a bar plot based on count data. For an example of count data, see the email50 curated data set which was taken from the Open Intro AHSS textbook (not affiliated). An example of count data in this dataset would be the spam column.

Usage

Select one (1) column to create its barplot and then click 'Submit'. If you do not choose count data, you may get unexpected results.

See Also

Students may also be interested in creating barplots for contingency tables.

For a stacked side-by-side barplot, see the other barplot app.

How To Create a Stacked Barplot

Webform

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Usage

Select 1 (one) column from a contingency table like the Gender and Politics or VADeaths curated datasets.

If you do not choose a contingency table, you may get unexpected results. You can import a dataset if you are logged-in.

Details

Shows the student how to create a single stacked bar plot based on a column in a contingency table.

See Also

For a basic barplot (single column) based on count data see the count data barplot app.

For a stacked side-by-side barplot see the other stacked barplot app for categorical data.

How To Create a Pie Chart

Webform

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Usage

Select 1 (one) column from a contingency table. If you don't have your own dataset, you can choose the Gender and Politics or VADeaths curated datasets. If a contingency table is not chosen, you may get unexpected results.

A contingency table has columns like a regular dataset, but the first row contains row names that categorize and "split-up" the dataset. An example of a contingency table would be something like this:

LIBERAL CONSERVATIVE
F 762 468
M 484 477

This contingency table is take from the Gender and Politics dataset. You can get a preview by selecting the dataset from the Curated Data dropdown above.

Details

This app shows the student how to create a pie chart from a contingency table by hand using a Quadstat dataset.

A pie chart shows proportions of a sample or population. Each piece of a pie chart corresponds to some subset of the sample or population. In this case, we will use the contingency table rows to subset the sample.

See Also

Students may also want to view the app for creating a pie chart from count data.

How To Compute the Mean

Webform

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Usage

Click "Submit" after selecting one column to see how to compute the arithmetic mean (average) of data (vectors).

Description

If all the values of a sample were plotted on a number line, the average would be the point in the middle that would balance the two sides.

The average is greatly influenced by outliers, meaning extreme points can pull the average to the left or right.

If we are referring to the average of population (all observations), the symbol for the average (arithmetic mean) is $\mu$.

If we are referring to the average of a sample (a subset of the population), the symbol for the average (arithmetic mean) is $\bar{x}$.

Computing the average

Suppose we have a sample consisting of $x_1, x_2, x_3,...,x_n$. This means we have $n$ observations. Then,

$$\bar{x}=\frac{x_1, x_2, x_3,...,x_n}{n}.$$

The formula tells us that we need to add all the observations and then divide by the number of observations to compute the mean.

Example 1

Compute the mean of $A = \{1,2,3\}$.

$$\bar{x} = \frac{1+2+3}{3} = 2.$$

How To Create a Plot

Webform

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Usage

Select two columns which are to be used in the scatterplot. The first column clicked will be the independent variable (X-axis).

Description

This web application describes how to create a scatterplot of two dataset variables plotted on the xy-axes.

How to Compute the Median

Webform

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Median Value

Description

Compute the sample median.

Usage

median(x, na.rm = FALSE, ...)

Arguments

x

an object for which a method has been defined, or a numeric vector containing the values whose median is to be computed.

na.rm

a logical value indicating whether NA values should be stripped before the computation proceeds.

...

potentially further arguments for methods; not used in the default method.

Value

The default method returns a length-one object of the same type as x, except when x is logical or integer of even length, when the result will be double.

If there are no values or if na.rm = FALSE and there are NA values the result is NA of the same type as x (or more generally the result of x[FALSE][NA]).

References

Becker, R. A., Chambers, J. M. and Wilks, A. R. (1988) The New S Language. Wadsworth & Brooks/Cole.

Boxplot

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Correlation Coefficient

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Cumulative Frequency Histogram

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Dotplot

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Hollow Histogram

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Mean

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Pie Chart

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Plot

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Regression

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Stem and Leaf Plots

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Summary

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Visual Summaries

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Attachment Size
dataset-32963.csv 462 bytes
Dataset License
GNU General Public License v2.0
Documentation

Milgram

Description

Attitudes towards ethics of a famous Milgram experiment

Format

A dataset with 37 observations on the following 2 variables.

Results Treatment group: Actual, Complied, or Refused
Score Ethical score from 1 (not at all ethical) to 9 (completely ethical)

Details

One of the most famous and most disturbing psychological studies of the twentieth century took place in the laboratory of Stanley Milgram at Yale University. Milgram's subjects were asked to monitor the answers of a "learner" and to push a button to deliver shocks whenever the learner gave a wrong answer. The more wrong answers, the more powerful the shock. Even Milgram himself was surprised by the results: Every one of his subjects ended up delivering what they thought was a dangerous 300-volt shock to a slow "learner" as punishment for repeated wrong answers.
Even though the "shocks" were not real and the "learner" was in on the secret, the results triggered a hot debate about ethics and experiments with human subjects. To study attitudes on this issue, Harvard graduate student Maryann de Mateo conducted a randomized comparative experiment. Her subjects were 37 high school teachers who did not know about the Milgram study. Using chance, Maryann assigned each teacher to one of three treatment groups: Group 1: Actual results. Each subject in this group read a description of Milgram's study, including the actual results that every subject delivered the highest possible "shock."
Group 2: Many complied. Each subject read the same description given to the subjects in Group 1, except that the actual results were replaced by fake results, that many but not all subjects complied.
Group 3. Most refused. For subjects in this group, the fake results said that most subjects refused to comply.
After reading the description, each subject was asked to rate the study according to how ethical they thought it was, from 1 (not at all ethical) to 9 (completely ethical.)

Source

"An experimental study of attitudes toward deception" by Mary Ann DiMatteo. Unpublished manuscript, Department of Psychology and Social Relations, Harvard University (1972).

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Dataset imported from https://www.r-project.org.

Documentation License
GNU General Public License v2.0

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