Title:
A Study On The Influence Of Background And Attitudes Of Secondary School Students On Mathematics Grades in Portugal
Introduction
The title of the Project 2 is ” A Study On The Influence Of Background And Attitudes Of Secondary School Students On Mathematics Grades in Portugal”. I choose this because I am interested in education level and I found that 50% of the Portuguese aged between 25 and 64 did not complete the education in secondary school. So, the data that I used is secondary data and it is all about the students’ mathematics courses, their background and students’ information in secondary school. The data is purposely used to predict students’ final grades, or it has been used in exploratory data analysis (EDA). I carry out this testing is to test whether the background and attitudes of students really affect the students’ grades in Mathematics subject in Portugal. The background here is actually the jobs of students’ parents. I also consider the address as the students’ background too. The attitudes here are about how the students spend their time, their absences, their future study plans and more. The target population is secondary students who take Mathematics subject in Portugal. My expected outcome is the background and attitudes of students really affect the students’ grades in Mathematics subject in Portugal. In this Project 2, R Studio is used in all tests and analysis. I begin this project 2 with a proposal, start to analyse data and come out with a conclusion.
inferential Analysis
2 sample test (3 tests here)
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- Since all test statistics are larger than critical value.
- Mean of students’ grades (G1, G2, G3) in urban address is higher than the mean of students’ grades (G1, G2, G3) in rural address.
Correlation
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- When number of school absences increase , all the grades includes first period, second and final grades in subject Mathematics will also not tend to either increase or decrease.
Regression
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- weekly study time will affect the first period grade and second period grade in Mathematics subject.
The goodness fit test shows
- students will choose secondary school based on the course, home, reputation or other reason. One or more of them will have higher proportions compared with other.
Chi-square test of Independence shows
- students’ decision to take the higher education is not influenced by their mothers and fathers’ jobs.
Conclusion
In conclusion, what I have get from project 2 is the background of students only have little influences on the mathematics grades. Living area will affect the grades but parents’ jobs don’t. Parents’ jobs also do not affect the students’ future study plan. Although students have high absence rate, their mathematics grades will not tend to increase or decrease. In conclusion, background and attitudes of students not really affect the secondary school students’ grades in Mathematics subject in Portugal. I am thankful to have a chance to complete the Project 2 and learnt a lot from it. I have gained more experiences to learn and use the R Studio to analyse the data.
Project 2 Reflection
RSS
In this project, I am using R studio to analyse the data. Since the secondary data is used, I don’t have to prepare questionnaire and collect. In order to conduct the survey, the data analysis process must be undergone. Firstly, we have to understand what the types of data are and test them using different kind of analysis. The suitable variables is filtered and chose so that some errors will be prevented else we can provide a more accurate testing. For example, the chi-square test of independence and goodness of fit test are used in the nominal data. The 2 sample mean test is used in the ratio data. Pearson‘s Product–Moment correlation coefficient is used in ratio or interval data while Spearman’s Rho Rank is used in ordinal data. Regression is done as well to test the causal effect between 2 variables. I do all this tests is to know that whether students’ background and their attitudes will affects their Mathematics grades.
I face some problems in this project 2. Firstly, I have to really spend time to think of what are the suitable variables to be used and pick some of the variables from the data set. At the same time, what kind of testing should be done with these variables. As we know, we have to come up with conclusion after doing the hypothesis testing. I am poor in this section because I am not good in describing and I have to think hard what to describe after we get the conclusion. A good description will make the flow of content looks good. After the report is completed, I have to make a clear presentation about the survey we have carried out. There are a lot of animations and extra small conclusions added in the slides to make the video presentation looks more interesting.
I did learn something from this project. This project 2 is about statistical inference which is concerned with selecting and using a sample to draw inference about population from which sample is drawn. I know how to generalize findings to a large population after doing the inference testing. We can just estimate what to happen in a population by analyzing the sample getting from it. The thing to take note is the sample size must be greater than 30 to have an accurate testing.
R file
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Download RScript_project2.R
RScript_project2.R Details
- Thursday, 25 June 2020 [7.1KB]
Data Set
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Download DataSet.7z.2
DataSet.7z.2 Details
- Thursday, 25 June 2020 [12.1MB]