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NIT5130 Database Analysis And Design
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NIT5130 Database Analysis And Design
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Course Code: NIT5130
University: Victoria University
MyAssignmentHelp.com is not sponsored or endorsed by this college or university
Country: Australia
Question:
Handling and interpreting data
The purpose of this exercise is to get you familiar with scientific data, both in generating, computing, and producing graphic representations of data, and in interpreting and understanding scientific data in the literature. Scientists present data to show patterns of results and as evidence for claims. Graphs are a good way to summarise data and highlight their patterns. To be literate about any area of science, we need to read and interpret graphs. This exercise helps with the basic skills in that task.
This exercise is again in two parts. For the first part, Tasks 1-2 , you need to answer some quiz questions on iLearn after doing the exercises. We suggest that you do the exercises first because the quiz questions are based on them. The second part, Task 3, requires a graph to be uploaded along with a little talk about your graph.
Task 3: graphing your own data from your own study program
In Task 3, you get to do some data crunching, a popular term for handling, analysing, and statistically testing data. No inferential statistical testing is required in this class, but you will get to crunch data to compute various descriptive statistics for plotting.
Data for plotting
The data for plotting is to come from a study program that you set up for yourself. Brief instructions on setting up such a program, as well as why you should do it for your university career, are in Chapter 1 of the textbook, in a brief movie.
Answer:
Work plan analysis
The graph above is a work plan analysis graph, a graph to analyze the effectiveness of my work plan (Dalland & Klette, 2014). It is a graph of the weekly average time I had planned to use while undertaking personal studies and average the time I actually took in doing personal studies in the 10 weeks of the semester, including mid semester’s exam weeks. I have drawn the graph with an aim of assessing the effectiveness of my work plan and to determine how disciplined I was in the ten weeks of the semester in using my planned study time in doing personal studies. To obtain the data I have used to draw the graph, I developed a personal study timetable by allocating some few hours for personal studies per day. The time I allocated for each day differed from one day to another depending on how busy the day would be. I then developed a cross check timetable template whereby I would fill the number of hours I used for my personal studies and therefore I came up with two data sets. These are the number of hour I had planned to study per day and the number of hours I actually studied per day.
In order to draw the graph, I did little bit of data processing so that I could produce data set fitting for use in drawing of the graph. I calculated weekly average time for the two data sets and then used this result to draw the graph. The graph shows that there is a disparity between the number of hours I had planned to study and what I actually used. In most of the weeks I never utilized all the time I had planned to undertake personal studies to do so. It was only in two weeks in the whole semester where I was disciplined enough to use all the time I had planned to use for personal studies (Oaten, & Cheng. 2006), in week five and week 9. Week 7 was the semester week when I used the least planned time for personal studies. This is because it was the week when I had my midsemester exam.
References
Oaten, M., and K. Cheng. 2006. Improved self-control: The benefits of a regular program of
academic study. Basic and Applied Social Psychology 28 (1):1-16.
Dalland, C.P. and Klette, K., 2014. Work-plan heroes: Student strategies in lower-secondary
Norwegian classrooms. Scandinavian Journal of Educational Research, 58(4), pp.400 423.
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