Men vs. Women; Battle of the Temperatures

While it has been found that men are generally “cooler” than women, our data appears to be the opposite.  My average body temperatue was 97.1571 F, while Pat’s 98.3257 F. Most men are 0.1 to 0.2 degrees cooler than women, but I am on average 1.1686 degrees F cooler than Pat.  Men and women usually have about the same standard deviation of about 0.7 degrees.  Pat’s standard deviation (.45719) was a bit lower than this, while mine was quite a bit higher (1.29144). This proves that outliers have quite an influence on standard deviation as well, since mine was so high as a result of my one extreme data point. Based on the variance between our data and the average data collected about the temperature of men and women, we are not very representative of most of the population. 

There are many things that will affect the mean of the data, most of them being random.  This could include being sick, being ina freezing or very hot place, or having a broken thermometer. Things that might affect the mean of the entire population could be age, location, and health. 

If we continuted to take measurements throughout the semester, our central tendencies would be much more accurate.  With more data, we would be able to see more patterns and outliers would not have such an affect.  It is certain that we would be able to draw a more accurate mean, median, mode, and standard deviation.  Of course, it is always possible that something completely random might occur, such as one of us getting very sick, and our data would be less accurate, since our average temperature will be much more influenced by extremes.

Which Central Tendency is Most Influenced by Extremes?

I found that the central tendency that is most influenced by extremes is definitely the median.  The reason for this is that the median is found by placing the data points in numerical order, so when the numbers are all lined up and an outlier is added to the data, the median can shift from side to side.  For instance, if I had the temperatures 97.0, 97.2, 98.2, 98.5, and 98.6, the median is 98.2.  But,  if for some reason an exteme outlier is added, such as 91.5, it would have to go on the lowest end, forcing the median to shift from 98.2 to 97.7, which is quite a difference.  Of course, if an outlier was added on the higher end, such as 100.0 F, the two extremes would balance each other out at the median would remain the same. Extremes can occur for any number of randomly attributed instances, such as catching a cold or fever, or they can even be caused by a faulty intsrument. 

Extreme values are rare and unusual depending on how much data is present.  For instance, since this was a relatively small amount of data of only 35 points, only one extreme (91.5) was seen in the data.  This extreme was the result of a poor reading made by a faulty instrument.  If the experiment had continued longer and there was mroe data, more extremes would have been present. Extremes will also be seen more often in a larger sample size.  If we look at just the data from Pat and I, we would see very few extremes, but if we looked at the whole class or maybe even the whole school we would see more and more extremes, caused by many different reasons. 

 Outliers are usually not the most reliable data points because since they are so far away from the rest of the data, it generally means that they do not represent the average information and were most likely caused by a random act that does not pertain to the rest of the data.

My Data from SPSS

Here is the data that SPSS calculated regarding my body temperature.

Mean: 97.1571

Median: 97.7

Mode: 97.7

Standard Deviation: 1.29144

Variance: 1.668

Assignment Two

Just some stuff that I think could compromise the data computation.
-Different variable settings on the calculator
-Human error when entering Data
-using the n function as opposed to the n-1 setting

Presentation on Hourly Body Temperature- Summary Post

Question 1. There are some major differences between random and systematic events. A random event is something that literally cannot be predicted accurately since there are too many outside factors that may affect it.  Like the example from class, it would be impossible to predict whether or not Charlie and Sally will stay together, since there are limitless influences that will steer their relationship in all sorts of different ways.  A systematic event is something that is easier to predict, since the outside influences are not as variable.  For instance, a systematic event might be something with strong ties between the action and the outcome, such as doing well on a test if you study hard.  It is impossible to necessarily predict the exact outcome, but you can get a good idea based on the pattern of past experience.

Question 2.  It is impossible to predict a specific event by just guessing, but it is possible to get a pretty good idea using statistics.  For example, in class Dr. MacEwan said he couldn’t guess the IQ of the next baby randomly, but if he found out the average IQ and looked at how the data formed a bell curve, it was easy to predict the baby’s IQ without too much error.  But, it would have always been possible that the next baby born randomly happened to have a very high or a very low IQ, which would have made his prediction incorrect, but using statistics he could find an answer that is most likely close to correct.  In the same light, we can record our temperatures and make a prediction that is probably about right, but we cannot always account for the randomness of life, so therefore it is impossible to predict any future event correctly, but we can get a pretty good idea.
Question 3. There are in fact systematic effects in our data.  Like it says on the assignment sheet, people usually have a lower body temperature in the morning and their temperature peaks in the afternoon.  These influences affect our experiment, along with a number of other things, such as our daily routine.  For example, when I take my temperature around 6 o’clock, it is usually cooler than normal, since I have been outside at Frisbee practice, and when I take my temperature around 8′oclock, is usually warmer, since that is about the time that I get out of the shower.  All of these systematic events affect the results of our data. 
Sources: MacEwen, B. (2008, spring semester). Psychology 261. Class Lectures. University of Mary Washington.  7 Random events that can alter statistical phenomenon

1.  Traffic- This effects when we get where and our mood.  This could have direct correlations with attendance and punctuality at both work and school.
 
2. Whether or not we eat properly.  Bad nutrition directly effects how we feel and our energy throughout the day.

3. How much homework we have effects how we budget our time and our stress levels.

4. Our interactions with our peers which effects our time, mood, and activities.

5. The weather, which effects our dress, means of transportation, etc. 

 6. A sudden death of a loved one- For example a friend or relative in perfect health gets into a fatal car accident. This random event could trigger depressive symptoms, monetary trouble (cost of funeral, loss of income if the person is a parent or spouse) and so on. 7. Political situations- Unpredictable events like a political assasination or an unexpected victory could drastically effect the morale and policy of a nation and greatly influence statistical events like homeland security, war etc.Strengths and Weaknesses
This assignment gave us a chance to learn about how to write a blog, including what seems to work and what does not. A place where we could have improved was our data collection. Unfortunately our instruments appeared to be a bit faulty, demonstrated by their reading of highly unlikely temperatures. Also, our data was not collected in a controlled environment, and as a result we saw the effects of some systematic events. If we were to do this assignment again, we would have been sure that we would not get biased readings by making sure we took our temperature at times where we hadn’t just been outside or in a hot shower.   For example, if we were to limit systemic events like room temperature, to make sure each time we took a reading the thermostat was set to a specific degree.  Some other limitations we found that the assignment set fourth included the relatively short amount of time, and the how the demands of our everyday activity affected our ability to take our temperatures at appropriate times. Despite these restrictions, we were able to find some tactics that worked. We found that communication was very important, and found the blog a very useful way to collaborate together. After sharing and exchanging ideas, we were able to put together a cohesive and comprehensive blog that successfully analyzes the concept of randomness. Summary After taking our temperatures every two hours for five days, we did notice a  pattern, but we have concluded that some the data can be attributed to randomness.  The systematic events that effected the temperature pattern includes events such as the temperature of the room, or what we happened to be doing at the time I took my temperature. On Thursday when it snowed, I was outside walking around in the afternoon, thus my temperature was the lowest at that point in the day.  Each day my temperature was a bit higher in the afternoon, and then gradually decreased, creating a graph that looks a bit like a sine curve. The only time my temperature was actually 98.6 was when I had just got out of a very hot shower, so either I’m just a cold person or I spend alot of time in the cold. Starting on Monday, I think my cheap thermometer may have broken, since it read 91.5.  Unless some very random event occured that made me pretty seriously sick, I think the thermometer may have just been a bit faulty.

My Rough version of graph (without kathleen’s data.)

My Temperature Graph

I’m gonna add the graph later, when all the data is collected, but for now I’ll just ramble about it….

 After taking my temperature every two hours for five days, I do notice a slight pattern, but I think most of the data can be attributed to randomness.  The systematic events that effected my temperature pattern includes events such as the weather, or what I happened to be doing at the time I took my temperature. On Thursday when it snowed, I was outside walking around in the afternoon, thus my temperature was the lowest at that point in the day.  Each day my temperature was a bit higher in the afternoon, and then gradually decreased, creating a graph that looks a bit like a sine curve. The only time my temperature was actually 98.6 was when I had just got out of a very hot shower, so either I’m just a cold person or I spend alot of time outside in the cold. Starting on Monday, I think my cheap thermometer may have broken, since it read 91.5.  Unless some very random event occured that made me pretty seriously sick, I think the thermometer may have just been a bit faulty.