AP Statistics Curriculum 2007 EDA Pics - Revision history

Tdlee: Text replacement - "{{translate|pageName=http://wiki.stat.ucla.edu/socr/" to ""{{translate|pageName=http://wiki.socr.umich.edu/"

2020-03-03T19:15:30Z

Text replacement - "{{translate|pageName=http://wiki.stat.ucla.edu/socr/" to ""{{translate|pageName=http://wiki.socr.umich.edu/"

Jenny: /* Stem-and-Leaf Plots */

2010-06-29T15:55:25Z

‎Stem-and-Leaf Plots

Jenny: /* Summary */

2010-06-28T18:53:08Z

‎Summary

Jenny: /* Stem-and-Leaf Plots */

2010-06-28T18:52:29Z

‎Stem-and-Leaf Plots

Jenny: /* Summary */

2010-06-04T21:11:27Z

‎Summary

Jenny: /* Pictures of Data */

2010-06-04T21:10:06Z

‎Pictures of Data

IvoDinov: added Problems section

2008-11-08T03:12:48Z

added Problems section

IvoDinov at 03:12, 8 November 2008

2008-11-08T03:12:21Z

JayZzz: /* Pictures of Data */

2008-10-21T02:20:58Z

‎Pictures of Data

IvoDinov: /* Pictures of Data */ Fixed a bad notation of "sample-size"

2008-04-07T03:59:21Z

‎Pictures of Data: Fixed a bad notation of "sample-size"

← Older revision		Revision as of 19:15, 3 March 2020
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	* SOCR Home page: http://www.socr.ucla.edu		* SOCR Home page: http://www.socr.ucla.edu

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 |+Stem-and-Leaf Plot for Hot-Dog Calories
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 ===Summary===
-* Histograms can handle large data sets, but they can’t tell exact data values and require the user to set-up classes
+* Histograms can handle large data sets, but they can’t tell exact data values and require the user to set-up classes.
-* Dot plots can get a better picture of data values, but they can’t handle large data sets
+* Dot plots can get a better picture of data values, but they can’t handle large data sets.
-* Stem and leaf plots can see actual data values, but they can’t handle large data sets
+* Stem and leaf plots can see actual data values, but they can’t handle large data sets.
 <hr>

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 ===Stem-and-Leaf Plots===
-Stem-and-leaf plot is a method for presenting quantitative data in a graphical format, similar to a histogram. It is used to assist in visualizing the shape of a distribution. Stem-and-leaf plots are useful tools in exploratory data analysis. Unlike histograms, stem-and-leaf plots retain the original data to at least two significant digits, and put the data in order, which simplifies the move to order-based inference and non-parametric statistics. A basic stem-plot contains two columns separated by a vertical line. The left column contains the '''stems''' and the right column contains the '''leaves'''.
+Stem-and-leaf plot is a method of presenting quantitative data in a graphical format, similar to a histogram. It is used to assist in visualizing the shape of a distribution. Stem-and-leaf plots are useful tools in exploratory data analysis. Unlike histograms, stem-and-leaf plots retain the original data to at least two significant digits, and put the data in order, which simplifies the move to order-based inference and non-parametric statistics. A basic stem-plot contains two columns separated by a vertical line. The left column contains the '''stems''' and the right column contains the '''leaves'''.
 * Construction: To construct a stem-and-leaf plot, the observations must first be sorted in ascending order. Then, it must be determined what the '''stems''' will represent and what the '''leaves''' will represent. Frequently, the leaf contains the last digit of the number and the stem contains all of the other digits. Sometimes, the data values may be rounded to a particular place value (such as the hundreds place) that will be used for the leaves. The remaining digits to the left of the rounded place value are used as the stems.

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 ===Summary===
-* Histograms can handle large data sets, but can’t tell exact data values and require the user to set-up classes
+* Histograms can handle large data sets, but they can’t tell exact data values and require the user to set-up classes
-* Dot plots can get a better picture of data values, but can’t handle large data sets
+* Dot plots can get a better picture of data values, but they can’t handle large data sets
-* Stem and leaf plots can see actual data values, but can’t handle large data sets
+* Stem and leaf plots can see actual data values, but they can’t handle large data sets
 <hr>
 ===References===
 * [http://www.stat.ucla.edu/%7Edinov/courses_students.dir/07/Fall/STAT13.1.dir/STAT13_notes.dir/lecture02.pdf Lecture notes on EDA]

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 There are [[SOCR_EduMaterials_ChartsActivities | a variety of graphs and plots]] that may be used to display data.
 * For '''quantitative''' variables, we need to make classes (meaningful intervals) first. To accomplish this, we need to separate (or bin) the quantitative data into classes.
-* For qualitative variables, we need to use the frequency counts instead of the native measurements as the latter may not even have a natural ordering (so binning the variables in classes may not be possible).
+* For qualitative variables, we need to use the frequency counts instead of the native measurements, as the latter may not even have a natural ordering (so binning the variables in classes may not be possible).
 * How to define the number of bins or classes? One common ''rule of thumb'' is that the number of classes should be close to <math>\sqrt{\texttt{sample size}}.</math> For accurate interpretation of data, it is important that all classes (or bins) are of equal width. Once we have our classes, we can create a frequency/relative frequency table or histogram.

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 * [http://www.stat.ucla.edu/%7Edinov/courses_students.dir/07/Fall/STAT13.1.dir/STAT13_notes.dir/lecture02.pdf Lecture notes on EDA]
-===Problems==
+===Problems===
 [[EBook_Problems_EDA_Pics | See these practice Problems]]