# AP Statistics Curriculum 2007 EDA Pics

(Difference between revisions)
 Revision as of 05:26, 28 January 2008 (view source)IvoDinov (Talk | contribs)m ← Older edit Revision as of 16:31, 10 February 2008 (view source) (→ General Advance-Placement (AP) Statistics Curriculum - Pictures of Data)Newer edit → Line 2: Line 2: ===Pictures of Data=== ===Pictures of Data=== - There are a [[SOCR_EduMaterials_ChartsActivities | varieties of graphs and plots]] that may be used to display data. + There are [[SOCR_EduMaterials_ChartsActivities | 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 '''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 $\sqrt{sample-size}$. 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. + * 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=== ===Example===

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

### Pictures of Data

There are 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 data file 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 Calorie 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 Calorie 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 Calorie content for all 3 types of hotdogs.
• The graph below shows the dot-plot of the Sodium content for all 3 types of hotdogs.

### 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.

• 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.
• Example: The stem-and-leaf plot for the calorie variable of the Hot dogs data is shown below.
• Legend: Stem-and-leaf of Calories, N = 54; Leaf Unit = 1.0
 8 67 9 49 10 22677 11 13 12 9 13 1225556899 14 01234667899 15 223378 16 17 235569 18 1246 19 00015

### 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