R Dataset / Package wooldridge / wageprc

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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.

Category

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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.

Category

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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.

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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 $A = \{1,2,3\}$.

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

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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.

Category

Boxplot

Submitted by pmagunia on April 22, 2018 - 3:07 PM

Select any column or the entire dataset option for its boxplot.

Correlation Coefficient

Submitted by pmagunia on April 22, 2018 - 3:08 PM

Select any two columns or the entire dataset option to compute the correlation coefficient matrix.

Cumulative Frequency Histogram

Submitted by pmagunia on April 22, 2018 - 3:09 PM

Select any column to plot its cumulative frequency histogram.

Dotplot

Submitted by pmagunia on April 22, 2018 - 3:10 PM

Select any column for its dotplot.

Hollow Histogram

Submitted by pmagunia on April 22, 2018 - 3:10 PM

Select any two columns to plot them simultaneously using a histogram.

Mean

Submitted by pmagunia on April 22, 2018 - 3:11 PM

Select any column to compute the arithmetic mean.

Pie Chart

Submitted by pmagunia on April 22, 2018 - 3:11 PM

Select any column to create its pie chart.

Plot

Submitted by pmagunia on April 22, 2018 - 3:07 PM

Select any two columns to plot.

Regression

Submitted by pmagunia on April 22, 2018 - 3:12 PM

Select any two columns for a simple regression analysis. The first column selected will be the independent variable.

Stem and Leaf Plots

Submitted by pmagunia on April 22, 2018 - 3:12 PM

Select any column for its stem and leaf plot.

Summary

Submitted by pmagunia on April 22, 2018 - 2:51 PM

Select any column to compute its mean, variance, and also other summary statistics.

Visual Summaries

Submitted by pmagunia on April 22, 2018 - 3:13 PM

Select any column for various visual summaries.

Submitted by pmagunia on March 9, 2018 - 1:06 PM
Attachment Size
92.77 KB
Dataset License
GNU General Public License v2.0
Documentation

wageprc

Description

Data loads lazily. Type data(wageprc) into the console.

Usage

data(wageprc)


Format

A data.frame with 286 rows and 20 variables:

• price. consumer price index

• wage. nominal hourly wage

• t. time trend = 1, 2 , 3, ...

• lprice. log(price)

• lwage. log(wage)

• gprice. lprice - lprice[_n-1]

• gwage. lwage - lwage[_n-1]

• gwage_1. gwage[_n-1]

• gwage_2. gwage[_n-2]

• gwage_3.

• gwage_4.

• gwage_5.

• gwage_6.

• gwage_7.

• gwage_8.

• gwage_9.

• gwage_10.

• gwage_11.

• gwage_12.

• gprice_1. gprice[_n-1]

Examples

 str(wageprc)

--

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

Documentation License
GNU General Public License v2.0

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