Collect Is How Much Time I Spend

¶ … collect is how much time I spend studying each day. I measured this by setting a stopwatch to run a cumulative total of all the time spent studying. I ran the data collection for 10 days, to achieve 10 observations. In this way, I calculated the amount of time studying each day for ten consecutive days. The basic logic of probability theory is that there are some events that are dependent and some that are independent.

Determining the probabilities of different events occurring involves understanding these relationships and accounting for them in the calculation. An independent event occurs no matter what the other events are. For example, two independent events would be the amount of studying that I do in a day and the amount of studying that my roommate does in a day, since we are in different courses and our study habits are therefore not related. A dependent event is one that is dependent on another event occurring.

For example, if we said that the number of hours I spent studying was dependent on my work schedule. The work schedule determines the amount of time I have for studying, and is therefore likely to influence my study habits. There is a high probability that on days where I work six hours or more I will study less than two hours. There are different types of probability as well. Simple probability is the probability of one independent event.

In this case, a simple probably would be the odds that I eat potato chips while studying. This is not related to my studying habits at all, but is mostly random. A conditional probability, however, reflects when the probability of an event occurring changes with another event. For example, the odds of me eating chips while studying is condition on whether or not I have chips in the house.

If I do, the probability is higher than when I do not, because if I do not I would have to go to the store. If I have chips, the odds are 66% than I will eat them; if I do not have chips, the odds are only 10% that I would go to the trouble of acquiring chips to eat. A joint probability is a multi-stage probability. For example, the odds that I would eat chips and study.

While each of these is an independent event, each has odds for whether I would do it on a particular day. The probabilities of each of these would be multiplied to determine the likelihood of me both eating chips and studying on the same day -- so 75% chance that I study and 33% chance that I eat chips means that on any given day there is a 25% chance that I do both. Part II. For this experiment, the dataset is contained in Exhibit A.

The measures of central tendency for this data set are as follows: The mean is defined as the sum of the numbers in a set, divided by the number of numbers in a set. In this case, the numbers add up to 32.2, and there are ten observations. The mean, therefore, is 3.22. The median is defined as the midpoint between the numbers in the set. In this case, the top four and bottom four observations would be removed, leaving 4 and 2.6. The midpoint between these two is 3.3, so that is the median.

The mode is the observation that occurs with the highest degree of frequency. In this case, that is 1.5 hours of studying in a day, which appears twice. All other observations only occur a single time. The measure of central tendency that most accurately describes the lifestyle variable I am looking at is the median. In this instance, the median and the mean are close to one another.

However, I believe that the median is the best descriptor, because of the high degree of variability in the amount of time I study in a given day. For example, the median of the first five observations is four and the median of the second five observations is 2.6. This indicates that I studied more during the first half of the experiment, which is true because I had an exam. During the.