TV Time Data Analysis Report

This report presents a statistical analysis of the author's daily TV viewing time over ten days. The data, collected using a stopwatch, shows varying viewing times ranging from 20 to 80 minutes. A frequency distribution table is created, and descriptive statistics (mean, standard deviation, and variance) are calculated using Excel. The report discusses the normality of the data distribution, noting the presence of an outlier (20 minutes) which affects the normality assumption. A histogram is included to visualize the data distribution. The report concludes that further data collection or outlier treatment might be necessary to improve the consistency and normality of the data. References to relevant statistics textbooks are provided.

Introduction:
This research is based on the topic of time spend on TV by me. The data collected here is for ten
days in minutes and this is an unbiased collection as I used a stopwatch. I usually watch
television in the evening time, so once I begin to watch television, I will start the stopwatch and
annotate the minutes I spend in front of the television.
This collection of data is random in nature because it was not collected purposely. I do watch
television daily, and hence there is no predisposition associated with the data set. However, in
the evening time I do not wait for a certain time to begin watching television. I watch certain
sitcoms and shows that I record due to conflict of schedules.
The data collected in first module are the values of time in minutes I spend watching television is
as shown below:
Day1 73 Minutes
Day2 80 Minutes
Day3 65 Minutes
Day4 20 Minutes
Day5 69 Minutes
Day6 75 Minutes
Day7 72 Minutes
Day8 73 Minutes
Day9 68 Minutes
Day10 70 Minutes
Frequency distribution table of the above data:

Time Frequency
20
Minutes
1
65
Minutes
1
68
Minutes
1
69
Minutes
1
70
Minutes
1
72
Minutes
1
73
Minutes
2
75
Minutes
1
80
Minutes
1
The calculation of mean, standard deviation, and variance for old data in Excel is as shown
below:

The distribution of the data is not normal because there exists an outlier value which is 20
minutes. Therefore, more collection of data might be needed or a technique can be used to deal
with outlying values which will make our data more consistent.
Let’s check it with the histogram. The histogram drawn from the above data is as shown below:
Although it shows that the data is approximately normal distribution but we cannot say that it is
normal because there is only one value that lies between 20 to 47 and there are two values that