# AP Statistics Curriculum 2007 EDA Pics

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[[Image:SOCR_EBook_Dinov_EDA_012708_Fig6.jpg|500px]]
[[Image:SOCR_EBook_Dinov_EDA_012708_Fig6.jpg|500px]]
- + === 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. +
[[Image:SOCR_EBook_Dinov_EDA_012708_Fig7.jpg|500px]]
+ + ===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=== ===References===

## 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 $\sqrt{sample-size}$. 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

• 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

• 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

• 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