This paper collects data on the amount of time spent cooking meals each day. Time, in this case, is a dependent variable because it is reliant on the type of activity (Chow and Teicher 54). This endeavor aims at using the probability theory to determine the likelihood that cooking consumes a given amount of time (Rohatgi and Saleh 3). Such an activity entails experimental probability because an actual situation is performed as an experiment (Mandel 90). I intend to keep track of time taken by setting a timer before I begin cooking and recording the time taken immediately after the task is complete. I expect the data to vary from day to day because I am not a regular cook. Some of the factors that are likely to skew my data include the type of meal preparation, the number of meals prepared in a day, and whether I decide to eat out or not. Different meals also require different times to prepare and cook. For example, most breakfasts involve making a cup of tea and frying an egg or toasting bread, which may take a maximum of 15 minutes. Lunches mostly involve the preparation of a simple sandwich and vegetable salad, which may take 30 minutes at most. Dinners, on the other hand, involve elaborate preparations such as peeling potatoes, chopping onions, tomatoes, and other vegetables.
Table 1 indicates the data collected after the first five days.
Table 1: The amount of time I spend cooking meals each day
From the data, the longest time was spent cooking on days when I had three meals in the house on days 1 and 4. In addition, I noticed that there was a twenty-minute difference in the time spent cooking on the first and fourth days. The difference could be attributed to the complexity of the meals. The dinner that was made on the first day required more preparation time compared to dinner on the fourth day. I took the shortest time cooking on day 2 when I only took breakfast in the house and had lunch and dinner at a restaurant. On the third day, I woke up late and skipped breakfast. I decided to make lunch only since I was to meet some family friends for dinner. I do not think the data presents a valid representation of these activities because the amount of time taken to prepare meals every day is influenced by several factors including the type of meal, the number of meals made in a day, and the choice of food, which have varying chances of occurring. Therefore, time is also a random variable because it can assume a set of likely different values, each with an accompanying likelihood (Downey 21).
It is not possible to forecast accurately the results of arbitrary occurrences (Ayyub and McCuen 93). However, if a series of discrete happenings is affected by other factors, it displays certain relationships, which can then be examined and anticipated (Buckley 34). In this case, the amount of time spent cooking in a given day is a factor of the type of meal, food, the number of meals prepared in a day. From the observed patterns, it is evident that more meals, complicated foods, and eating out influence the time spend cooking.
References
Ayyub, Bilal M. and Richard H. McCuen. Probability, Statistics, and Reliability for Engineers and Scientists, Boca Raton: CRC Press, 2011. Print.
Buckley, James J. Fuzzy Probabilities: New Approach and Applications, New York: Springer, 2012. Print.
Chow, Yuan Shih and Henry Teicher. Probability Theory: Independence, Interchangeability, Martingales, New York: Springer Science & Business Media, 2012. Print.
Downey, Allen B. Think Stats. Sebastopol, CA: O’Reilly Media Inc., 2011. Print.
Mandel, John. The Statistical Analysis of Experimental Data, New York: Courier Corporation, 2012. Print.
Rohatgi, Vijay K., and A. K. Ehsanes Saleh. An Introduction to Probability and Statistics, New York: John Wiley & Sons, 2011. Print.