Just a test

_freeze/week1/index/execute-results/html.json

@@ -2,7 +2,7 @@
   "hash": "0dfc06e329b0f30f0a195b8230d86134",
   "result": {
     "engine": "knitr",
-    "markdown": "---\ntitle: \"Week 1: What is forecasting?\"\n---\n\n::: {.cell}\n\n:::\n\n\n\n\n## What you will learn this week\n\n* How to think about forecasting from a statistical perspective\n* What makes something easy or hard to forecast?\n* Using the `tsibble` package in R\n\n## Pre-class activities\n\nBefore we start classes, make sure you are familiar with R, RStudio and the tidyverse packages. If you've already done one of ETX2250/ETC1010 or something equivalent you should be fairly familiar with these concepts and probably will not need much help. If you're new to R and the tidyverse, then you will need to get yourself up-to-speed. \n\n-   Install/update R, RStudio. See [https://otexts.com/fpp3/appendix-using-r.html](https://otexts.com/fpp3/appendix-using-r.html)\n-   Install required packages `install.packages(c(\"tidyverse\",\"fpp3\", \"GGally\"), dependencies = TRUE)`\n-   Explore StartR: [https://startr.numbat.space/](https://startr.numbat.space/). Work through **Getting started** (5 modules) and **Writing documents** (1 module). Do as much of it as you think you need. For those students new to R, it is strongly recommended that you do all these. For those who have previously used R, concentrate on the parts where you feel you are weakest.\n-    Read [Chapter 1 of the textbook](http://OTexts.com/fpp3/intro.html) and watch all embedded videos. Pay particular attention to [Section 1.7](https://otexts.com/fpp3/perspective.html).\n-   Read [Section 2.1 of the textbook](https://otexts.com/fpp3/tsibbles.html#tsibbles) and watch the embedded video.\n\n\n\n\n\n\n## [Workshop activities](activities.qmd)\n\n\n\n\n\n<!-- ## Seminar code -->\n\n<!-- ::: {.callout appearance=\"minimal\"} -->\n<!-- <i class=\"bi bi-download\"></i> [Seminar_code_week1.R](Seminar_code_week1.R){download=\"Seminar_code_week1.R\"} -->\n<!-- ::: -->\n\n\n## Tutorial exercises\n\nThe main tasks for Week 1 tutorials will be:\n\n1. To ensure that you have successfully installed R and RStudio on your own laptop.\n2. Work your way through **Getting started** (5 modules) and **Writing documents** (1 module). This is material we have prepared for you and other Monash students working in R. You should do these at your own pace to understand the concepts.\n3. Discuss [IA1](https://bf.numbat.space/assignments/A1.html) in class. How do you go about forecasting at the moment that you are untrained? \n\nYour tutors will be in your tutorial class to assist you. \n\n<!-- [**Check your understanding quiz**](https://learning.monash.edu/mod/quiz/view.php?id=2327331) -->\n\n\n\n\n\n## Assignments\n\n* [IA1](../assignments/A1.qmd) is due on Monday 10 March.\n",
+    "markdown": "---\ntitle: \"Week 1: What is forecasting?\"\n---\n\n::: {.cell}\n\n:::\n\n\n\n## What you will learn this week\n\n* How to think about forecasting from a statistical perspective\n* What makes something easy or hard to forecast?\n* Using the `tsibble` package in R\n\n## Pre-class activities\n\nBefore we start classes, make sure you are familiar with R, RStudio and the tidyverse packages. If you've already done one of ETX2250/ETC1010 or something equivalent you should be fairly familiar with these concepts and probably will not need much help. If you're new to R and the tidyverse, then you will need to get yourself up-to-speed. \n\n-   Install/update R, RStudio. See [https://otexts.com/fpp3/appendix-using-r.html](https://otexts.com/fpp3/appendix-using-r.html)\n-   Install required packages `install.packages(c(\"tidyverse\",\"fpp3\", \"GGally\"), dependencies = TRUE)`\n-   Explore StartR: [https://startr.numbat.space/](https://startr.numbat.space/). Work through **Getting started** (5 modules) and **Writing documents** (1 module). Do as much of it as you think you need. For those students new to R, it is strongly recommended that you do all these. For those who have previously used R, concentrate on the parts where you feel you are weakest.\n-    Read [Chapter 1 of the textbook](http://OTexts.com/fpp3/intro.html) and watch all embedded videos. Pay particular attention to [Section 1.7](https://otexts.com/fpp3/perspective.html).\n-   Read [Section 2.1 of the textbook](https://otexts.com/fpp3/tsibbles.html#tsibbles) and watch the embedded video.\n\n\n\n\n\n## [Workshop activities](activities.qmd)\n\n\n\n\n<!-- ## Seminar code -->\n\n<!-- ::: {.callout appearance=\"minimal\"} -->\n<!-- <i class=\"bi bi-download\"></i> [Seminar_code_week1.R](Seminar_code_week1.R){download=\"Seminar_code_week1.R\"} -->\n<!-- ::: -->\n\n\n## Tutorial exercises\n\nThe main tasks for Week 1 tutorials will be:\n\n1. To ensure that you have successfully installed R and RStudio on your own laptop.\n2. Work your way through **Getting started** (5 modules) and **Writing documents** (1 module). This is material we have prepared for you and other Monash students working in R. You should do these at your own pace to understand the concepts.\n3. Discuss [IA1](https://bf.numbat.space/assignments/A1.html) in class. How do you go about forecasting at the moment that you are untrained? \n\nYour tutors will be in your tutorial class to assist you. \n\n<!-- [**Check your understanding quiz**](https://learning.monash.edu/mod/quiz/view.php?id=2327331) -->\n\n\n\n\n## Assignments\n\n* [IA1](../assignments/A1.qmd) is due on Monday 10 March.\n",
     "supporting": [],
     "filters": [
       "rmarkdown/pagebreak.lua"

index.qmd

@@ -54,6 +54,8 @@ Email: [[email protected]](mailto:[email protected]
 
 ## Weekly schedule
 
+Testing this
+
 * **Pre-class preparation**, [pre-recorded videos](https://www.youtube.com/watch?v=uwKiT1o1TkI&list=PLyCNZ_xXGzpm7W9jLqbIyBAiSO5jDwJeE), approximately 60 minutes.
 * **Seminars**, 10-11 Tuesday, [Building K Level 3, K321, CA_K_K321](https://www.monash.edu/__data/assets/pdf_file/0010/2658961/Caulfield-campus-map.pdf).
 * **Lectorials**, 11-12 Tuesday, [Building K Level 3, K321, CA_K_K321](https://www.monash.edu/__data/assets/pdf_file/0010/2658961/Caulfield-campus-map.pdf).