The presentations are complete and everything went as planned. My data was actually contradictory of what I believed it would be. When I first set out for my project, I expected to see that the New Orleans restaurants would have more business, but this was not the case. I saw that the two areas had about an even amount of patrons in the local restaurants. Seeing this, I began to assess the possible reasons for this. The one I came up with is that while the French Quarter is the center of tourism in our city (which is why I assumed they would have more business), it does not necessarily mean that the tourists are expected to dine at these establishments. Also, those visiting our city are not likely to stay in a hotel near the French Quarter, for the high costs, therefore may stay in hotels located near Metairie and Kenner, in turn causing them to dine close for a quick meal. It was indeed interesting to see such results from my experiment. I carried out a t-test (A statistical test to determine whether the difference between two sample means is statistically significant) with my data and came out with the following results:
t= .4884
p= .6312
mean 1=66.1 (mean on French Quarter data)
mean 2=58.6 (mean on Kenner and Metairie data)
standard deviation 1= 34.3287 (std. dev. of French Quarter)
standard deviation 2= 34.3485 (std. dev. of Kenner and Metairie data)
Final.
Data Update.
This past weekend, I completed my data collection. I was able to squash my worries of variability in my data as well as keep some sort of pattern in my data, and eliminate as many confounding variables as possible; I collected all of my data from up and down Veterans Boulevard. By doing so, I eliminated the amount of distance between restaurants that would have been present if I were to collect the data from different parts of the city. This also allowed me to add in a similarity between the two data collections (the New Orleans data was collected up and down Decatur). I used the same tactics to collect this set of data, as before--estimating if absolutely necessary and asking the hostess if possible. As you can see, there are much lower numbers at some restaurants in this area, and I concluded that there are two possible reasons for this-- that either a)the restaurant may have been having a slow night or b)that in the location of the restaurant is not as popular. Now that I have completed my data collection, I need to decide what programs would be best suited to analyze my data. I am not very familiar with MiniTab, so I am going to attempt to do my data analysis using Microsoft Excel. Aside from data analysis, I need to begin work on my final presentation. My plan is to attempt to make an interactive PowerPoint using the Promethean board (a board used in class, that when paired with a laptop and the proper technology, is interactive and touch screen). Whether or not this is plausible will be deciphered in the beginning of next week. I am completely open to suggestions on data analysis as well as how I should carry out my presentation. So, please, give me some feedback! Anything is welcome.
Food for Thought.
I began to think about the second half of my data and how it differs slightly from the criteria of my first. By this I mean that the fact that all of the restaurants from which I collected from in the French Quarter were located almost right next door to each other, and in Metairie and Kenner, this is not the case. The restaurants in these areas are located blocks away, sometimes with three or four streets in between. I am starting to wonder if this will have a profound effect on my data--possibly adding in some sort of bias into my results. Does anyone have any suggestions or comments on this issue? In the end I guess it might just be true when people say, "Location is everything."
Data Collection Update.
At this point I have collected my data from the restaurants in the French Quarter. Between the hours of 6 and 8 on Saturday April 4, I was able to collect data from 10 restaurants: Landry's, Hard Rock Cafe, Jax Brewery, Masperos, Tujague's, Envie, Margaritaville, Frank's, The Corner, and Bubba Gump. While some restaurants were easier to enter, others had lines out of the door. For those that I could actually step foot into, I asked the hostess for help. I explained my project to him/her, and in turn asked if they could give me the number of people per table and did simple addition to find the total. For the ones that had lines out of the door, or in which I was unable to communicate with a worker, I guesstimated. I counted by twos or fives, depending on how crowded the establishment seemed to be. By looking at my data, you can tell which ones I was able to get an exact number and which were estimated to the best of my ability. Although I did have to somewhat guess at a few, I think the numbers were reasonable enough to be close to the actual number, therefore while it will have some effect on the graphing of my data, it will not be as sever as it could have been. It will in fact contribute some error to my final project, one that I wish could have been avoided.
Now that I am near to completing my data collection, I will begin to use Excel and/or Minitab data analysis programs) to analyze and graph my data.
Data Progress.
Data collection--stress central. With a project such as mine, the data collection process is quite tedious and requires much thought. After much consideration, I decided not to pre-make a list of restaurants, but rather to visit the French Quarter, Kenner, and Metairie and collect data from as many restaurants as humanly possible in the time allotted. My plan is to enter each restaurant and estimate to the best of my ability the amount of patrons in the establishment. If this seems to be difficult, I will attempt to ask the hostess the number of people at each table and add up the total. The next task, which is almost more difficult than the first, is deciding what to do with the data after I have collected it. In the beginning, I had decided to only produce a scatter plot to display my data, but then realized that this would not work because that is used to show correlation--something that is not necessary with this activity. I then decided to make a box plot of each set of data to show the five number summary of each, just to give an overview of the data. I also decided to make an o-give (describes the probability distribution of real valued random variables) and histograms (graphical display of tabulated frequencies) for each data set. By performing each of these displays, I am giving my audience a chance to see the data in different settings--one in which they see the pattern of number of customers in each restaurant, and the probability of each number. By viewing the box plots of the data, it can be shown which setting receives more business, which can be helpful in many ways (refer to previous blog entry entitled "How is this useful?") I would really like helpful feedback on my process, both pre and post data collection. So please, tell me what you think!!
Changes!
This week I had to make some changes to my project. I found that the direction in which my project was heading was not going to work. It was way more feasible to compare and contrast the business levels between restaurants in the French Quarter and those restaurants located in surrounding cities such as Kenner and Metairie. Also, I decided not to limit myself to a certain number of restaurants-- I decided to visit as many as possible in a given time. By doing this, I am giving myself a wider range of data. It will also help me show a better correlation between the business levels. I am actually more excited about this project layout because I think people will care more about the comparison between the business levels in different areas as opposed to the comparison between the different cuisines.
How Is This Useful?
The data that I will be collecting may not seem important at first glance, but if you really think about it, it can be quite helpful. One of the most important reasons for this is the economic situation. In tough times as these, entrepreneurs are very skeptical when it comes to opening a business. By carrying out my project, I can possibly display the areas that are best for business. For example, if my data shows that there is a significant increase in the amount of business from Kenner to the French Quarter, then an aspiring restaurant owner would be more likely inclined to opening a restaurant in the Quarter. The same works if more business is shown in the smaller cities. Also, it may not be scientifically or mathematically proven that restaurants with more business are actually better in quality, but some may see it this way. Therefore, whichever area of New Orleans my data shows receives more business, that displays that I choose may supply these establishments with even more business. The data can build up the hype around each restaurant and make people that are natives to the areas, and even tourists even more interested in visiting them.
My data can also be useful to the restaurants themselves. I will provide my results to all restaurants involved, showing the differences in the amount of business between the different establishments and different areas of New Orleans. By seeing that restaurants in other areas may receive more business than they do will, of course, not make them want to move their location, but will most likely take this into consideration. They can reevaluate their establishment and make the proper changes to increase business.
While my data may not be as vital as some, it is vital in the business aspect. It can help people who are struggling entrepreneurs or already struggling business owners.
Help me!
Help me! As you all know, I’m doing my project on the amount of business traditional restaurants in the French Quarter receive in comparison to those that are considered untraditional—but I don’t know any good restaurants in that area! I have a few ideas but any suggestions would be great. Remember, what I have in mind as traditional would be as a restaurant that serves food that is considered unique to New Orleans, i.e po-boys, seafood, jambalaya, etc. Untraditional restaurants would be ones that serve food that isn’t considered unique to this area, such as Chinese, Mexican, or Italian. Please, please, please give me some ideas!
Altering my Toolbox.
This week I focused on improving my data analysis toolbox. I needed to be more descriptive and outline basically every small detail of my project. When I composed my first draft, I wasn’t exactly sure how I was going to go about collecting my data. Not only that, but I wasn’t sure how I was going to measure or analyze it. I figured out that using an arbitrary rating scale wouldn’t be appropriate because it could introduce some bias. By asking a restaurant manager to rate his/her amount of business, or anything about his business at all, they are automatically going to want to rate their business high on the scale. Knowing this, I started to think that maybe I could set qualifications to each rating, such as “1: business flow is low at this time” or “5: business flow is moderate at this time”. To eliminate the chance of this happening at all, I had to find another way to measure the amount of business the restaurants had. My next approach was to pre pick the restaurants, and the time at which I would visit, and go in and count the number of patrons as closely as possible. I decided to visit the chosen restaurants within the same time frame for three days of one week. I will go and count the number of patrons between the hours of 4 and 6 pm on most likely Monday, Wednesday, and Friday of the week of March 21. All restaurants are projected to be located in the French Quarter, making the setting both efficient for data collection and keeping some set norm to the atmosphere of the project. I’m still struggling to get everything in the toolbox that is needed. I know that it must be almost a script for how I will carry out my experiment.
How to Blog.
As with almost anything, there is a right way and a wrong way to do something. When blogging, the line between right and wrong, or hot and wack, is very thin. One image or color scheme too many, and your blog can become tacky and obnoxious, causing readers to abandon your site. On the other hand, eye catching images, flowing colors, and interesting topics will keep the readers coming and your blog with have more hits than you can keep up with. When being graded on our blog, I think our teacher should be looking for images that are both attention grabbing and somewhat relevant to the entry and/or topic. Also, I think that she should be looking for a user friendly, easily navigated blog. No one wants to visit a site that they cannot figure out how to work it. The layout of the blog should be appealing and somewhat mellow, you don't want to pick a layout that is bright, obnoxious, and gives the reader a headache as they explore your blog entries. When reading our entries, I think that our teacher should be looking for fluency, as well as our ability to stay on topic and get our point across. She should also pay attention to length; make sure that we don't get to wordy, yet still meet our quota. My opinion of a moderately "hot" blog is blanchetblog.net. This blogger has a user friendly blog that is easily navigated and seems to be updated on a timely basis. Not only does she have information that is relevant to what is going on within her classroom, which seems to be the basis of the blog, she has updates about things in her life, giving the blog a friendly atmosphere. The user uses entertainment to teach her students and gives them a reliable site to obtain content from their classes. Your blog should be a place where you can display information, either research based, entertainment, or just about yourself, in a way that reflects who you are as an individual.
Data Anlaysis Toolbox.
While writing my data analysis toolbox, I decided that I would pre determine a list of restaurants at which I will distribute my survey. The restaurants will be located within New Orleans, mainly the French Quarter, and surrounding cities such as Metairie and Kenner. I will attempt to have an equal amount of restaurants that serve native cuisine to those that serve food that isn’t unique to our city. The variables being measured will be the amount of business each restaurant receives and when they have the most customer traffic. I will attempt to get a count of business level at specific times of the day, namely breakfast, if applicable, lunch and dinner. Also, I would like to see on which days of the week business is the heaviest, and if certain holidays or times of the year are heavier than others. The level of business will be rated on a scale that goes from 1-10 or 1-5. A rating of 1 will be the least business and either 10 or 5 will stand for the most business. The rating will be determined by hopefully the manager of each restaurant. I would also like to include something in my survey that will help measure the amount of tourists in the restaurant as accurately as possible. I will record my findings using either a dot plot or box chart displaying the average rating for each restaurant. I will find the min, max, median, and average rating for each to represent the numerical interpretations. The point of gathering the data will be to see whether or not restaurants that serve traditional New Orleans food really do in fact get more business in its native setting, or do those that serve food that is not unique to New Orleans, or considered traditional, receive more business.
Experimental Design.
Question.
What restaurants in New Orleans receive more business? (those with traditional New Orleans cuisine or those without)
Materials.
-List of restaurants in New Orleans and surrounding areas
-Specialized Survey
-Transportation
-Helping hands (to deliver surveys)
-Method of collecting data
Subjects.
-Subjects will be chosen based on location and menu (types of food served and whether unique or traditional to New Orleans)
Procedure.
-Incorporate some sort of waiver allowing use of restaurant name and dishes and to solicit customers if needed
-Research local restaurants and find those that serve traditional (Cajun, southern, food that is unique to New Orleans (jambalaya, crawfish, etc.) etc.) and non traditional dishes (foreign foods, etc.)
-Have list pre determined so that distribution is easier and more efficient
-Distribute survey to pre determined amount of traditional vs. non traditional restaurants (survey will include some sort of rating scale)
-Collect and analyze data according to scaling system
Project Topic.
I chose to focus my project around New Orleans cuisine. I chose this because Louisiana and New Orleans in particular is known for its unique cooking style. I'm stuck between two directions for this project. The first direction is to conduct a survey amongst traditional New Orleans restaurants vs. non traditional New Orleans restaurants. The purpose will be to see which of the two receive the most business, and at what times of the day, week, etc. they are the busiest. Is it true that just because a restaurant serves what is considered to be "New Orleans food" that they will receive the most business, or do the restaurants that serve food that strays from this stereotype receive more? Also, I would like to include something in the survey to try and keep track of the amount of tourists that are patrons of said restaurants. The second direction that the project can take is to conduct a survey amongst Copeland's restaurants to see how business has changes since his death. I would also like to see if the way the restaurants are run and whether or not his traditions are being carried on.