# AP Statistics Curriculum 2007 EDA Pics

### From Socr

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<center>[[Image:SOCR_EBook_Dinov_EDA_012708_Fig6.jpg|500px]]</center> | <center>[[Image:SOCR_EBook_Dinov_EDA_012708_Fig6.jpg|500px]]</center> | ||

- | + | === Dot Plots=== | |

+ | * Using [http://socr.ucla.edu/htmls/SOCR_Charts.html SOCR Charts] and the [[SOCR_EduMaterials_Activities_DotChart | Dot Plot Charts activities]] you can produce a number of interesting graphical summaries for [[SOCR_012708_ID_Data_HotDogs | this hotdogs dataset]]. | ||

+ | |||

+ | * The graph below shows the dot-plot of the '''Calory''' content for all 3 types of hotdogs. | ||

+ | <center>[[Image:SOCR_EBook_Dinov_EDA_012708_Fig7.jpg|500px]]</center> | ||

+ | |||

+ | ===Summary=== | ||

+ | * Histograms can handle large data sets, but 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 | ||

+ | * Stem and leaf plots can see actual data values, but can’t handle large data sets | ||

+ | |||

<hr> | <hr> | ||

===References=== | ===References=== |

## Revision as of 20:53, 27 January 2008

## Contents |

## General Advance-Placement (AP) Statistics Curriculum - Pictures of Data

### Pictures of Data

There are a varieties 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).
- How to define the number of bins or classes? One common rule of thumb is that the number of classes should be close to . 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.

### Example

People who are concerned about their health may prefer hot dogs that are low in salt and calories. The Hot dogs datafile contains data on the *sodium* and *calories* contained in each of 54 major hot dog brands. The hot dogs are also classified by type: *beef*, *poultry*, and *meat* (mostly pork and beef, but up to 15% poultry meat). For now we will focus on the calories of these sampled hotdogs.

### Frequency Histogram Charts

- Using SOCR Charts and the Charts activities you can produce a number of interesting graphical summaries for this hotdogs dataset.

- The histogram of the
**Calory**content of all hotdogs in shown in the image below. Note the clear separation of the calories into 3 distinct sub-populations. Could this be related to the type of meat in the hotdogs?

- The histogram of the
**Sodium**content of all hotdogs in shown in the image below. What patterns in this histogram can you identify? Try to explain!

### Box and Whisker Plots

- Using SOCR Charts and the Box-And-Whisker Charts activities you can produce a number of interesting graphical summaries for this hotdogs dataset.

- The graph below shows the box and whisker plot of the
**Calory**content for all 3 types of hotdogs.

- The graph below shows the box and whisker plot of the
**Sodium**(salt) content for all 3 types of hotdogs.

### Dot Plots

- Using SOCR Charts and the Dot Plot Charts activities you can produce a number of interesting graphical summaries for this hotdogs dataset.

- The graph below shows the dot-plot of the
**Calory**content for all 3 types of hotdogs.

### Summary

- Histograms can handle large data sets, but 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
- Stem and leaf plots can see actual data values, but can’t handle large data sets

### References

- SOCR Home page: http://www.socr.ucla.edu

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