4. NORMAL MODEL14. NORMAL MODEL TypeMOD2 MODELING DATA Materials Reviewed Module 1 Revision: Sample Exam Question 1. Using the commentary under the plot, explain in your own words what the 'Attendance' variable represents? a. Attendance variable represents the percentage of year1-10 students that attend school equal to or greater than 90% of the time. 2. How many variables are presented in the plot? What type are they? a. Year - categorical ordinal b. Type of school - categorical nominal c. % of students that attended school =,> 90% of time - quantitative continuous 3. In context, in one sentence, explain an insight from the plot. a. The indepandent schools attendance levels are the highest from the three regardless of year b. School attendance rates dropped dramatically in 2022 across all school types
4. NORMAL MODEL24.1 imagine Learning outcome LO4Apply the Normal approximation to data, with consideration of measurement error. Discussion During high intensity training, high performance athletes intake 10-20times more air than the normal person, hence conducting this training session even at reduced air
4. NORMAL MODEL3quality allows for a significant amounts of pollutant exposure - can end up with damage to respiratory system scatterplot graph showing respiratory health vs hours of exercises Sydney Data Stories Kylie Moulds (PhD Exercise and Sport Science) investigates how a coach can create a "motivational climate" for their players. She shares insights about the different models used in sports science, including survival analysis, with tips on the use of models. normal mode to regression model used in sports science survival analysis model is a time to event model so looks at the time it takes for swimmer to drop out of a sport data from swimmers NSW set of 17,000 plus swimmers ages 10-15 10 and 11yr old swimmers were dropping at ages younger than previously recorded swimmers in bigger cities were dropping out at higher rates than rural models can be applied to real life contexts really need to be clear ab the questions before you want to apply the appropriate model. Topic 4 Summary "all models are wrong, but some models are useful" Module 2 is all about 'modelling data': which is finding a mathematical formula which can approximately describe the shape of the data (eg histogram). There are many different models - we focus on the Normal model (for 1 quantitative variable) and the Linear model (for 2 quantitative variables).