KEBARANGKALIAN STATISTIK & ANALISIS DATA (SECI2143)

Since the pandemic of Covid-19 is still going on, there is no final exam for Probability and Statistical Data Analysis course. So, my lecturer told us to do an individual project as a replacement for the final examination. The scope of this project is inferential statistics and every student must use secondary data that can be found from any websites or any organization. I had found a suitable dataset which is about birth weight with the help of my friends and lecturer. So, I decided to use this dataset for my project.

After I had completed and submitted my proposal of the project, I continued to study this dataset and done some research on birth weight while waited for the lecturer to comment on my proposal. I was so grateful that my lecturer accepted my proposal. Then, I immediately started to do the project so that I could finish it early. In this project, I used the R Studio to do the inferential statistics which are hypothesis testing one sample, correlation, regression and goodness-of-fit test. It was not as hard as I thought to use R Studio as I referred to the R slides provided by the lecturer in the e-learning. Whenever I did not understand to do the coding, I would ask my friend to help me.

I did the hypothesis testing using one sample to test on the mean of birth weight and the null hypothesis is failed to reject. As for the correlation test, I tested the relationship between a mother’s pre-pregnancy weight and birth weight. It can be seen that this relationship is positive hence linear correlation exists but the strength of the relationship is weak. In regression analysis, the dependent variable that I chose for this test was birth weight and the independent variable was gestation. I have found that a linear relationship does exist between these two variables and 50% of the variation in birth weight is explained by variation in gestation. As for the goodness-of-fit test, there are two different categories in the variable that I chose which are low and normal birth weight with unequal frequencies. I have found that the observed frequency and expected frequency are close, the chi-square value is small and the P-value is large. So, the null hypothesis is failed to reject hence it shows that this is a good fit with the assumed distribution.

By studying the birth weight dataset, I can conclude that the mother’s pre-pregnancy weight and gestation are some of the factors that will affect birth weight. Then, I continued to do the video to present this project and it took only one day for me to complete the video. I used a few software applications to edit the video. In the video, I included the introduction, all the test results and conclusion. After I produced the video, I uploaded it on Youtube. I really hope that people who watch my video will gain something good and understand what I explained in the video.

This project really taught me to be an independent person as it was an individual project. It was tiring but I am so relieved that there was no final examination because I am scared if my internet connection faces a problem while answering the online examination. I would like to thank my lecturer, family and friends who always support me in completing this project. There are many new things that I learned from this project. I hope it will help me in the future.

Dr. Chan Weng Howe told us to do a project on descriptive statistics by using primary data such as a survey. My group has four members which are Husny, Fatin, Amir and I.At first, Husny created a group Whatsapp for us to communicate easily with each other. We discussed the topic that we are going to use for the survey. After several opinions from us, we chose “The Usage of Smartphone Among Students” as our topic. We wanted to know the usage of smartphones among Computer Science students.

We thought that it would be easier to use an electronic form of a survey using Google form. After we made the survey, we shared the link of the survey through Whatsapp groups consists of students from this faculty. Many students helped us by completing our survey. We succeed to get 105 respondents on our survey. Therefore, data analysis was much easier to do. Unfortunately, Covid-19 cases have been rising rapidly. The Government has decided to implement a nationwide Restriction of Movement Order beginning the 18th of March until now.

All my group members including me went back to our home. We decided to make online discussions using an application named Discord. By using the application, we can share our screen and communicate easily. We discussed how to use RStudio to generate graphs like a bar chart, pie chart, scatter plot, stem-and-leaf, box plot, frequency distribution and histogram. I made a Google document for the report so that all my group members can edit too after I added them as collaborators. We took less than a month to finish the report. I checked the report many times to make sure we did not make any spelling mistakes on the report.

After we finished editing the report, we continued to make the video presentation for our project. I thought it was hard to make the group video as we could not meet each other. My internet connection is quite bad to join the live record with others. Therefore, we recorded our own videos and combined it with our slides. After that, all of us sent our videos to Husny for him to combine the videos to make as a one group video. We tried our best to minimize the length of the video and we managed to make it to seven minutes and nine seconds. We hope our lecturer can accept the video as he told us the video must not exceed seven minutes.

Based on this project, from the sample of 105 students, most of the students often use social media on their smartphone and Instagram is the most preferred social media by students. The mean of monthly data usage among students is 16.93 GB. The highest value of students' average call is 60 minutes and the lowest is 2 minutes. 35 out of 105 students are very likely to watch movies or dramas on their smartphones daily. Based on the generated scatter plot of relation between student's CGPA and monthly data usage of students, we conclude that students' CGPA will not be affected by the amount of data they used monthly. 

I am happy to have group members like them because this task becomes easier to be done as they gave good cooperation in completing this project. We really hope that we can get high marks for this project as we had tried our best in making this project. I want to thank my lecturer, Dr. Chan Weng Howe for guiding us until the end of this project and also thank my friends and family who always support me in any situation.