MATH1324 Applied Analytics Due Date: 10/05/2020 Overview This assignment is simple. You are asked to work on a project and data is provided. This is your opportunity to demonstrate all that you have...

MATH1324 Applied Analytics Due Date: 10/05/2020 Overview This assignment is simple. You are asked to work on a project and data is provided. This is your opportunity to demonstrate all that you have learnt so far in this course. You will be awarded (with marks) the clearer you demonstrate your skills. This isn’t about showing off. You won’t be rewarded for applying advanced statistical analysis outside the course. That’s not what this course is about. This course is about asking and answering interesting questions about the world using the fundamentals of statistics. This investigation will seek to understand if there is any statistical significant difference in the average length of stay (ALOS) between large and medium hospitals which might make patients choose one over the other. Download the data from the website of the Australian Institute of Health and Welfare. (Please, acknowledge the source of your data in your report) This assignment is worth 20% and must be uploaded to the Assignment 2 submission by 10/05/2020. Submission Instructions The report must be slides uploaded as a PDF with your code showing. You can also use other format to provide slideshow. Presentations are limited (maximum) to 20 slides. The presentation must be composed of the following sections. You can add more if your wish, but you must include these sections as a minimum. 1. Presentation title and group/individual details [Plain text]: You can add the title of your presentation and student(s) details by updating the “title” and “author” entries at the top. 2. Introduction [Plain text]: A good introduction provides a brief background to the problem, defines important terms, and leads to a strong rationale. 3. Problem Statement [Plain text]: State the overall problem/question driving the investigation. Summarise how you will use statistics to solve the problem or answer the question. 4. Data [Plain text]: Explain how you collected your data and pre-processed it for analysis. There should be enough detail here so that someone else could replicate your data collection. Ensure you reference the data source if you have used Open Data. List and explain the important variables. Explain and justify everything that you do to pre-process the data e.g. outliers, NAs or missing values. For example, you may give NP a value, like NP = 3.4, or eliminate the record. You decide, it’s your project! You just need to explain and justify your choices. 5. Descriptive Statistics and Visualisation [Plain text & R code & Output]: Summarise the important variables in your investigation. Use visualisation to highlight interesting features of the data and tell the overall story. Explain how you dealt with data issues (if any), e.g. missing data and outliers. 6. Hypothesis Testing [Plain text & R code & Output]: Apply an appropriate hypothesis test for your investigation. Ensure you state the hypotheses and check any assumptions. Report the appropriate values and interpret the results. 7. Discussion [Plain text]: Discuss the major findings of your investigation. Discuss any strengths and limitations. Propose directions for future investigations. This is a good place to re-state your findings as a final conclusion. What is the one take home message the reader should leave with? 8. References [Plain text]: Provide a list of any references you use in the presentation.