INTRODUCTION
Mobile phones have been improving countless times from the first invention until today. In the early years of development of mobile phones, the cost for regular consumers to obtain a mobile phone was extremely high. For example, the first mobile phone, Motorola DynaTAC 800x, launched in 1983 was sold at a price of around $4000. It was obvious that no average Joe can afford to buy a mobile phone at that time. When compared to the price of mobile phones these days, the difference is astounding.
Mobile phones provide easy and simple ways to communicate with families and friends at all times and it can be used in any place. It is very convenient to use mobile phones because of its small weight and size.
BACKGROUND OF STUDY
The great advantages of mobile phones will make sure students’ life become more efficient and easier. Because of universities learning programs these days strongly depend on using mobile phones, every student is encouraged to buy their own mobile phone. This project is to study whether students’ income will affect the students’ mobile phone expenses among School of Computing UTM students.
OBJECTIVE
- To study university students’ expenses in buying mobile phones.
- To study the relationship between students’ income and students’ mobile phone expenses.
- To know students' mobile phone brand preference.
- To study student’s behavior patterns in buying their own mobile phone.
- To know how much student is willing to spend on buying mobile data
CONCLUSION
To conclude, we have recorded 60 students’ responses as a sample of data with 31 male respondents and 29 female respondents. The majority of respondents are 1st year students with 44 respondents and followed by 11 respondents from 2nd year students and only 5 students from 4th year students.
The most preferred smartphone brand is Apple with 13 students (21.7%) while the least preferred smartphone brands are Google and Redmi with only 1 student each (1.7%)
The highest frequency of monthly income of students each month is RM500 and below with 36 students (60%) and the lowest frequency is range RM1500-RM2000 and RM2500-RM3000 with the frequency of 3 students (5%) each range.
Last but not least, the relationship between students’ monthly income and monthly spending for data plans has a weak negative relationship and non-linear. From this, we can know that when the students’ monthly income decrease, monthly spending for data plans by students is increasing.
REFLECTION
In reflection on my experience in completing this project, I have enhanced my practical skill on how to use R software to produce many types of graph from a sample of data. Before this, majority of what I learn is only in-class learning. So, this project gave me the opportunity to apply and experimenting with R software in daily life.
VIDEO
Video already upload in elearning by Muhammad Isyraff