Introduction
“I’m going to fail this exam if I can’t find a place to focus!”. Have you ever wondered where the quietest place to study is at Messiah but still fill your stomach while running on empty? Look no further because in this website, our team has compiled all the data and information you need to choose between Lottie, Cafe Diem, or Union to gain a sense of peace and knock out the work that has to be complete. This project is essential for choosing the perfect environment to survive tough classes, munch on some food, and improve one’s cognitive ability by eliminating noise pollution and loud distractions.
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Problem & Hypothesis
Our project aims to provide valuable information to Messiah University students so that they can use their study time most effectively and efficiently. This project will evaluate some of the prime study locations on campus. These locations are the Murray Library, the Larsen Student Union, and Lottie Nelson Dining Hall. At each location, we will be testing for the characteristics that make a good or bad study location, these characteristics are temperature, sound level, and foot traffic. In addition, we will vary the day of the week and time of day we are at each location so that students can precisely pinpoint the place and time they should study to optimize their time.
Being a smaller institution, Messiah University does not have the same number of study locations to offer its students, compared to larger institutions. This is not necessarily a bad thing, but it does pose the average Messiah University student with a challenge. Will they just simply work from the comfort of their room or will they choose a change of scenery? For many students, the risk is not worth the reward. The risk that they will be too distracted or that they will not be able to focus or that they will not be comfortable. But why does it have to be a risk? Why can’t we make informed decisions based on proven statistical information? This is the need for this study. By conducting this project, we will be able to provide answers to the unknown and help improve the Messiah University experience for all.
We hypothesize that the Union in the evening on a weekday is the worst time and place to study. This is because we believe it will be the loudest, most distracting (based on foot traffic), and the least comfortable temperature compared to the other options. We also hypothesize that the Murray Library in the afternoon of a weekend will be the best time and place to study because it will be the quietest, least distracting (based on foot traffic) and most ideal temperature.
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Design of Experiment
The purpose of this diagram is to clarify which input factors are being manipulated to determine the effect on the desired output known as our response variables. For our project, a person entering a location, and observing the environment is the process. To gain a response from this process, we will determine the noise levels, ambient temperature, and foot traffic in each setting. The setting will vary by location, time of day, and day of the week to determine how each response variable changes from each setting. Also, this DOE block diagram helps account for potential uncontrollable input factors (Figure 1) and different SPC strategies to prevent them.
Figure 1: DOE block diagram for project
Experimental Planning of DOE
Figure 2: Selected controllable factors and their levels
Based on the calculation (Figure 3), we know that we need 42 trials because one of the factors has three levels while the other two have two levels. Also, we will be using 3 replications giving us a total number of 42 trials.
Figure 3: Calculation used to determine the number of trials and runs
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Possible Interactions:
1) One interaction we might discover is that places are overall quieter in the evening than places during the afternoon. However, this trend might be because people go to bed earlier during the week than on the weekends. So, a more accurate way to show this trend would be to have one graph for noise level during the week, afternoon to evening. And then another graph for noise level during the weekend (afternoon to evening)
2) Another possible interaction we might discover is that the Union is colder overall than Lottie. However, this trend might be because the windows in the Union are not well insulated, so they are very sensitive to the cold at night. So a better way to compare this would be to have one graph showing the afternoon temperature of the places, and then another graph showing the evening temperature of the places.
Baseline Test Plan
To take a baseline test, we will be testing the sound level for a one-minute period along with a single temperature reading. The baseline experiment is a single test taking place within the library (away from Cafe Diem) where we will record the sound level for one minute to compare with the baseline at the end of each replication, being mindful of possible drift. In addition, we will record the temperature level and foot traffic during the each baseline test.
Figure 4: The Full Factorial Experiment with corresponding 3 replications
Practical Considerations
Screening Experiment
Our screening experiment was a 30 minute interval where we recorded sound levels for a minute every 5 minutes. We also recorded the temperature at the beginning of every 5 minute interval. We set up our temperature probe and the Decibel X app in the Union in a central location and took a reading of both once every five minutes. In addition to measuring the temperature and noise level at the union, we also counted the amount of foot traffic that was present as well. The graph shown below (Figure 5) is the average noise level within each five-minute interval.
Figure 5: Temperature probe and Decibel X app used
Figure 6: Average Noise Level based on screening data
Equipment Feasibility
Our experiment will require us to have a temperature probe, the Decibel X app, and a computer. We have received permission from the physics department to borrow a temperature probe, and all team members already have the Decibel X app installed on their phones. None of these pieces of equipment will require any training.
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Time Feasibility
We are going to be running 42 trials of this experiment, so our experiment should take about 21 hours total. We have split the 21 hours amongst ourselves and split up the times throughout the week. In total, this experiment should take the span of a week.
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Expertise Feasibility
Everyone in the group is already prepared to operate all of the equipment that we will be using in this experiment.
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Cost
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Figure 7: Cost estimate associated with running the full experiment
Personnel Management
We have set up our experiment so that each team member is recording data for an equal amount of time. Throughout the week, each team member will be recording data for between 1.5 and 2 hours 4 times a week.
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Potential for Benefit
There is motivation to run this experiment so that we as Messiah University students can find a place to study in the most productive way possible. It can be a struggle to find a place where we can be comfortable and focus well, and hopefully this experiment will allow us to find a place that will make all of those things possible.
Brief Traditional Experiment
For the "Traditional" vary x, measure y part of the experiment, we will vary the time of day (x-variable) from 9:00 am to 9:00 pm. We will measure the temperature every hour during this time period. We will keep the variables location (Union) and day of the week (Weekday) the same. See Figure 9 for anticipated results.
Points for Further Study
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One challenge that we could face in our experiment is an error in our measuring tools. To measure the temperature in each of our locations, we are using a temperature probe. We have found this temperature probe to be somewhat inaccurate in its readings, so getting a clear and accurate set of data points could be a challenge. Another challenge within this experiment could be measuring foot traffic. This measurement is somewhat arbitrary, and between three people recording the data, there could be some deviation in what is seen as “close” to one person and what is close to another. Finally, we may not be able to complete our experiment in the time that we are given. We are planning to record a large amount of data, so being able to acquire all of this could be challenging for our group in the window of time that we are given.
We might find that distractions in places where we study may go beyond sound, temperature, and foot traffic. Other activities that don’t necessarily raise the noise level or the amount of foot traffic could also be distracting. We can pay attention to and record these things. In this experiment, we will be observing the effects that time, location, and day of the week have on the temperature, noise, and foot traffic of different study locations. An important factor that affects all of these things is the reason that these response variables change. In some cases, it could be that our control variables are not what correlates to our response variables, but instead to another outside source. We could observe and record the events that are taking place and what the reason beyond these three controlled variables could be for the changes. One more extension of this experiment could be how our presence affects the experiment. There could be instances where other people that are in the same locations as us know us or are friends with us and could come up and talk to us, which could mess up our data. This is something that we can take into consideration and record as well.
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Anticipated Results
Figure 8 shows that we expect the greatest variation to come from the change of location. We expect the Union to be the loudest overall location because of the number of people there to strictly socialize. Also, we expect the Library to be the quietest because it is a much more study focused location. In addition, we also expect a significant change in the morning to afternoon sound levels, but we do not expect a large change in the weekend to weekend sound levels.
Figure 8: Expected DOE means output graph
For our traditional “Vary X, Measure Y” experiment, we plan to measure the temperature in the Union over the course of a day, from 9:00 am to 9:00 pm. We will record one data point every hour to produce a complete set. Experimentally, we expect this data to look similar to the graph shown in Figure 9. We expect the Union to be colder in the morning but then warm up incrementally during the day, reaching a peak at about 5:00 pm when the sun is directly shining in through the windows. Then once the sun sets, we would expect the temperature to steadily drop until 9:00 pm.
Figure 9: Expected results for Temperature vs. Time of Day