A few researchers set out to determine the optimal length of chopsticks for children and adults. They came up with a measure of how effective a pair of chopsticks performed, called the "Food Pinching Performance." The "Food Pinching Performance" was determined by counting the number of peanuts picked and placed in a cup (PPPC).
Link to Abstract and Paper
the abstract below was adapted from the link
Chopsticks are one of the most simple and popular hand tools ever invented by humans, but have not previously been investigated by ergonomists. Two laboratory studies were conducted in this research, using a randomised complete block design, to evaluate the effects of the length of the chopsticks on the food-serving performance of adults and children. Thirty-one male junior college students and 21 primary school pupils served as subjects for the experiment to test chopsticks lengths of 180, 210, 240, 270, 300, and 330 mm. The results showed that the food-pinching performance was significantly affected by the length of the chopsticks, and that chopsticks of about 240 and 180 mm long were optimal for adults and pupils, respectively. Based on these findings, the researchers suggested that families with children should provide both 240 and 180 mm long chopsticks. In addition, restaurants could provide 210 mm long chopsticks, considering the trade-offs between ergonomics and cost.
Download the data set for the adults, then answer the following questions based on the abstract and the data set.
If you double click on this cell, you will see the text change so that all of the formatting is removed. This allows you to edit this block of text. This block of text is written using Markdown, which is a way to format text using headers, links, italics, and many other options. You will learn more about Markdown later in the Nanodegree Program. Hit shift + enter or shift + return to show the formatted text.
Think about the participants who generated the data and what they have in common. You don't need to guess any variables or read the full paper to determine these variables. (For example, it seems plausible that the material of the chopsticks was held constant, but this is not stated in the abstract or data description.)
Each group has the same gender and similar age (e.g. 31 male junior college students is one group). This means the age and gender were matched. Matching age could eliminate the effects of reduced flexibility, agility or mental focus that might be present in older subjects. Matching gender may be important because smaller hands (among women) may be better suited to smaller chopsticks.
One great advantage of ipython notebooks is that you can document your data analysis using code, add comments to the code, or even add blocks of text using Markdown. These notebooks allow you to collaborate with others and share your work. For now, let's see some code for doing statistics.
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import pandas as pd
# pandas is a software library for data manipulation and analysis
# We commonly use shorter nicknames for certain packages. Pandas is often abbreviated to pd.
# hit shift + enter to run this cell or block of code
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path = r'/Users/pradau/Dropbox/temp/Downloads/chopstick-effectiveness.csv'
# Change the path to the location where the chopstick-effectiveness.csv file is located on your computer.
# If you get an error when running this block of code, be sure the chopstick-effectiveness.csv is located at the path on your computer.
dataFrame = pd.read_csv(path)
dataFrame
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Let's do a basic statistical calculation on the data using code! Run the block of code below to calculate the average "Food Pinching Efficiency" for all 31 participants and all chopstick lengths.
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dataFrame['Food.Pinching.Efficiency'].mean()
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This number is helpful, but the number doesn't let us know which of the chopstick lengths performed best for the thirty-one male junior college students. Let's break down the data by chopstick length. The next block of code will generate the average "Food Pinching Effeciency" for each chopstick length. Run the block of code below.
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meansByChopstickLength = dataFrame.groupby('Chopstick.Length')['Food.Pinching.Efficiency'].mean().reset_index()
meansByChopstickLength
# reset_index() changes Chopstick.Length from an index to column. Instead of the index being the length of the chopsticks, the index is the row numbers 0, 1, 2, 3, 4, 5.
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# Causes plots to display within the notebook rather than in a new window
%pylab inline
import matplotlib.pyplot as plt
plt.scatter(x=meansByChopstickLength['Chopstick.Length'], y=meansByChopstickLength['Food.Pinching.Efficiency'])
# title="")
plt.xlabel("Length in mm")
plt.ylabel("Efficiency in PPPC")
plt.title("Average Food Pinching Efficiency by Chopstick Length")
plt.show()
There appears to be a peak efficiency at 240mm and less efficiency for either smaller or longer lengths. It could be that the curve has a secondary peak at 300mm but it is difficult to determine without sampling at additional lengths.
Yes.
First we must accept that the operational definition of food-pinching performance is reasonable, which it appears to be from this brief description. Then we can do a test to see if length shows any effect on efficiency. A paired t-test for the 240mm and 330mm lengths (2 tailed, alpha =0.01) is significant with p= 5.4e-6. Therefore the null hypothesis (mean efficiency in PPPC is equal for the 240mm and 330mm lengths) must be rejected, for this pair. We can conclude that chopstick does have a significant effect on efficiency in PPPC.
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