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Landing Page MATH 9102 PORTFOLIO

This is the landing page for the Digital Portfolio I created as part of TU060 Math9102 Module. This page will serve as youre index, from which you'll be able to navigate to each section of the portfolio. It also contains administration information such as links to the sources code. Lines are also provided in each subsection to the relevant RMD file.

Lastly references are provided to what learning outcoming are covered in each section. Model and Analyse phase where combined for reporting purposes but split between correlation and difference into a part 1 and a part 2 for admin reasons due to the size of the RMD files. 

Student Details

  • Student number: C03001130
  • Student name: Joseph O’Carroll
  • Course Code: TU060 MATH9910
Source Code: Github Link Package list: Github_Link

Datasets

  • Delahoz-Dominguez, Enrique, Rohemi Zuluaga, and Tomas Fontalvo-Herrera. “Dataset of academic performance evolution for engineering students.” Data in Brief (2020): 105537.

  • Cortez and A. Silva. “Using Data Mining to Predict Secondary School Student Performance.” In A. Brito and J. Teixeira Eds., Proceedings of 5th FUture BUsiness TEChnology Conference (FUBUTEC 2008) pp. 5-12, Porto, Portugal, April, 2008, EUROSIS, ISBN 978-9077381-39-7. (https://repositorium.sdum.uminho.pt)

 R Version Details
platform x86_64-w64-mingw32 
arch x86_64 
os mingw32 
system x86_64, mingw32 
status 
major 4 
minor 0.2 
year 2020 
month 06 
day 22 
svn rev 78730 
language R 
version.string R version 4.0.2 (2020-06-22)
nickname Taking Off Again

Digital Portfolio INDEPENDENT PROJECT

This is the landing page for the Digital Portfolio Independent Project I created as part of TU060 Math9102 Module. This page will serve as your index, from which you'll be able to navigate to each section of the portfolio. 

LEARNING OUTCOMES COVERED:

  • Present a summary of the variables representing the concepts of interest, critically discussing relevant issues which impact statistical analysis; 
    • Include statistical summaries of the variables of interest and evidence to support relationships or difference to justify their inclusion in a dimension reduction or regression model.
  • Use appropriate statistical techniques to achieve your goals.
  • Present and interpret the findings;
  • Briefly draw conclusions discussing your findings;
  • Adopt the APA guidelines for reporting statistical analysis using APA citation and referencing.
  • You must use R to conduct your analysis;
  • You should cite appropriate sources (which are accessible) in order to support the guidelines you adopt in your decision making and interpretation of findings.

Demonstrate: 

  • An ability to state a research question suitable for a statistical analysis;
  • Generate and correctly state a hypothesis or hypotheses; 
  • The ability to correctly prepare, present, analyse and critically assess the dataset used from the perspective of statistical analysis;
  • The ability to correctly execute, present and interpret appropriate statistical tests using statistical software;
  • The ability to analyse and present the findings gained from your statistical analysis in a clear and accurate way to a standard expected of postgraduate level academic work;
  • The ability to construct a report on a statistical inquiry.

Digital PROFOLIO MATH 9102

This is the landing page for the Digital Portfolio I created as part of TU060 Math9102 Module. This page will serve as youre index, from which you'll be able to navigate to each section of the portfolio. It also contains administration information such as links to the sources code. Lines are also provided in each subsection to the relevant RMD file.

Lastly references are provided to what learning outcoming are covered in each section. Model and Analyse phase where combined for reporting purposes but split between correlation and difference into a part 1 and a part 2 for admin reasons due to the size of the RMD files. 

PrePARE

Learning outcomes covered: 

  • Populations and samples
    • Explain the difference between population and sample
    • Explain the difference between a statistic and a parameter
    • Explain how you infer from a sample to a population
    • Explain the importance of of representatives
  • Describing a sample
    • Explain the importance of correct sampling i.e. how to collect to ensure representatives and randomness
    • Explain the importance of having enough data in a sample
    • Describe the different types of statistical measures
    • Discuss the concept of missing data and its importance
  • Explain what a hypothesis test aims to achieve.
    • Explain the concepts of hypothesis testing
    • Explain the difference between the null hypothesis and the alternate hypothesis
  • Apply the understanding of the issues above to the dataset for your variables of interest

LEARNING OUTCOMES COVERED:

Correlation only 

  • Descriptive Statistics
    • Explain which types of descriptive summary statistic and visualization are most appropriate for each type of statistical measure
  • Assessing a variable for normality
    • Explain the steps involved
    • Describe the statistics and visuals which can be used and the heuristics that can be used to make a judgement
  • Explain Type I and Type II errors
  • Explain Statistical Power and Statistical Significance
  • Correlation
    • Explain the concepts of correlation
    • Explain the steps involved in conducting a correlation test
    • Identify the correct test to use for normally distributed data and for non-normally distributed data
    • Explain what needs to be reported for a correlation test when reporting findings
  • Apply understanding to the dataset provided
    • Provide an example of each general type of descriptive and visual for variables from the dataset
    • Assess at least one variable for normality
    • Identify some at least one correlation between your concepts of interest you want to investigate
    •  

LEARNING OUTCOMES COVERED

Difference only

  • Descriptive Statistics
    • Explain which types of descriptive summary statistic and visualization are most appropriate for each type of statistical measure
  • Difference
    • Explain the concepts for difference involving two groups
    • Explain the concepts for difference involving more than two groups
    • Identify the correct test to use for normally distributed data and for non-normally distributed data (for two groups and for more than two groups)
    • Explain what needs to be reported for difference test when reporting findings (for two groups and for more than two groups)
  • Apply your understanding to the dataset provided
    • Provide an example of each general type of descriptive and visual for variables from the dataset
    • Assess at least one variable for normality
    • Identify at least one difference effect involving a concept of interest and involving two groups you want to investigate
    • Identify at least one difference effect involving a concept of interest and involving more than two groups you want to investigate

LEARNING OUTCOMES COVERED

  • What is an interaction effect?
  • What is a dummy/indicator variable?
  • Linear Regression
    • How to conduct
    • How to interpret
    • Assumptions to test
    • What to report
    • How to compare models
  • Logistic Regression
    • How to conduct
    • How to interpret
    • Assumptions to test
    • What to report
    • How to compare models
  • Apply your understanding to the dataset provided
    • Build a multiple linear regression model (needs at least two predictors)
    • Build a binary logistic regression model (needs at least two predictors)
    • Illustrate a differential effect (in either linear/logistic regression)
    • Illustrate an interaction effect (in either linear/logistic regression)

Model