Data Collection Project: Analysis of Commuting Time and Probabilities

This assignment presents an analysis of a data collection project focused on the time it takes to commute to work. The student identifies the dependent variable as commuting time and several independent variables such as traffic and construction. The student hypothesized a bell curve distribution for the collected data but found the data to be right-skewed due to construction delays on the route. The analysis calculates the probability of arriving on time, which is 70%, and discusses the implications of this probability over a year. The student acknowledges the limitations of the small sample size and suggests that a larger sample would provide a more comprehensive understanding of the commuting patterns. The project demonstrates an understanding of data analysis, probability, and the impact of external factors on data outcomes.

Module 1
Data Collection Project
The activity that I have considered for the collection of the data for this assessment is the
time it takes me to arrive to my work every day. There are two types of variable that I am
likely to encounter every day, namely, dependent variable and independent variable. The
dependent variable in my case is the time. I have considered the time as the dependent
variable as there are likely to be multiple factors that will impact my commuting from my
home to work.
The time I started counting on begins from the moment I leave my outside door. After that,
whatever time I spend is to be counted as the time consumed in reaching the destination.
There are various independent factors that are impacting the amount of time I spend when I
commute such as the traffic, construction, someone known I met on the way, and other
things.
Prior to the collection of the data, I developed a hypothesis that the data collect would form
the bell curve. The reason behind that will be my early arrival on someday due to no traffic
and roadblocks at all, and someday I may arrive very late due to number of hurdles.
However, it was found that the data collected for the ten days has not formed the proper bell
curve. It shows right skewed. The reason was that I spent longer duration on road for seven
days from total ten days measured. The reason was that the construction was going on the
route I daily take for commuting and out of ten days seven days were really disturbing.
Here, it can be stated that the construction is not a usual activity and it might be possible that
I could have taken less time in general. However, it does not matter how much time I spent,
the point is that whether I reached the destination on time.

I marked each arrival in the form of 0 and 1 and found that I reached late for three days out of
ten.
Therefore, p = 7/10 = 0.70
This shows that the probability of me arriving on time is 70 per cent of the total time. If only
the data is considered without the external factor consideration, then it can be stated that the
probability is not a good sign for me as it shows that I may arrive late for 30% of the one year
and that counts to 78 days. Here, it can be stated that large number of sample size could have
helped in better understand the situation.