• A little bit about the TAs and myself
• The process of learning at uni (as opposed to high school…)
• Why are we here? What do you expect?
• Why come to lectures?
• Relationship student-teacher, student-TA (make it interactive!)
• Relationship student-student (learning groups!)
• learning and emotions, creativity (studies show that learning difficult stuff is associated with positive emotions!)

“If you want to learn to think you need a dialogue”

Immanuel Kant, 1786

• Head TA: Sanaa Nair
• Cassandra Buhle Tshili
• Sydney Curtis-Wilson
• Roen McLeod
• Dexter Liu

Please do not email them privately, use the course email address

• the twofold difficulty of learning statistics and R
• the twofold reward of learning statistics and R!
• with R, you are standing on the shoulder of giants!
• The difficulty of teaching statistical concepts and R… (what first?)
• This can make a tricky start, but we are here to help!
• Have the right attitude (YOU are responsible for your learning, we are here to help)

• First, read the information on Canvas (slides, announcements, weekly overviews)!
• Read the FAQ (frequently asked questions, on Canvas)
• Ask Dr Google!
• BIOL501 2022 Facebook group. Here to discuss things and form learning groups. We will also monitor chats there (ask to join!), Canvas BIOL501 forum (chat)
• only if all of the above don’t get you there, email
• EMAIL: please use BIOL501@aut.ac.nz
• If you cannot meet a deadline, apply for ‘special consideration’ on Canvas, don’t email us
• If you would like to chat with one of us in person, please use our personal email addresses and make an appointment

• COME TO YOUR LECTURES/LABS/TUTORIALS: those who are present tend to do well
• Lectures are recorded, labs/tutorials are not
• Be organised, time yourself: Revise your labs every single week!
• Be creative, inquisitive, ask questions, don’t miss the train!
• Be realistic: 102 hours of self-directed learning (see course guide) equates to 8.5 hours of homework per week, that’s 2 full afternoons/week!
• Don’t get lost in the ocean of available resources, choose one, stick to it, read, practice!!

• Lecture: Monday 8:00-9:50am
• Be there, be there on time!
• Labs: 8 lab/tutorial streams: Please look up times and room numbers from your timetables
• You are encouraged to bring your own laptop, but there are desktops provided
• NO labs/tutorials in week 1, they start in week 2.

• Need to access Canvas and AUT email address regularly!

• Change lab stream? Please see the Science office staff (WS level 1)

• Assessments: midsemester test/exam (25%/40%) plus two marks from the two assignments (15 and 20%)

• Calculate your overall mark as follows

\[assign_1 \cdot 0.15 + assign_2 \cdot 0.2 + test \cdot 0.25 + exam \cdot 0.4\] - There are no longer practical/theory parts to be passed individually!

• A preview of what is to come at the beginning of a lecture (weekly ‘learning outcomes’)
• A summary of what you should have learnt at the end of a lecture
• A glossary at the end of the lecture
• The lab reports: take them seriously, they are your foundation for passing this paper. Follow the rules, but be creative! Add to them, make them your personal little course manual!
• Practice in them and with them!
• E.g., take notes during the lectures, then put them into your lab reports under the section ‘what have I learnt today…’
• Please help us keep lectures and labs interactive. There is nothing better than a seemingly ‘dumb question’!

‘If you want a statisics lecturer, I’ll do anything I can for you - I’m your man’ (modified from Leonard Cohen)…but:

‘Follow me the wise man said, but he walked behind.’ (also by Leonard Cohen, unmodified)

In plain language: we are here for you, but you first need to do your homework, come to lectures, do the labs, read…

• MOST IMPORTANT: Lectures (your presence, your notes) and lab reports, including your own additions, tutorials
• Slides from lectures (via a link but also as pdf)
• Countless internet resources (pdfs, videos), some suggestions will be sent out during the semester

If you want to dig deeper:

• M.J. Crawley, The R Book, Wiley (approx. $120)

Slides are not enough! Not everything is online, if you are not in the lecture, it’s up to you to catch up, this is not an online only paper.

Dates refer to lecture only!

1. Demonstrate an understanding of biological variability and how it is measured and reported.

• Explain systematic vs. unsystematic biological variation, explain accuracy and precision
• Understand what a frequency distribution is
• Be able to plot and interpret histograms
• Be familiar with the most common distributions that are relevant for biological data sets (e.g. normal, uniform, poisson), be able to calculate probabilities and quantiles

2. Demonstrate an understanding of sampling.

• Explain the difference between a population and a sample
• Know when to use the standard deviation vs. the standard error
• Be able to design a simple biological experiment or observational study

3. Demonstrate competence in basic biological and ecological data analyses

• Be able to organise and process biological data sets
• Use a series of simple tests like the t-test, correlation test, regression, Chi-squared, and their non-parametric alternatives where applicable.
• Interpret the results in a biological context

4. Demonstrate competence in reporting experimental results in a written format suitable for publication

• Be able to draw scientifically accurate and correct figures and tables
• Understand the basic structure of a scientific report
• Appropriately report statistical results (scientific writing and layout, proper use of units etc.)

5. Demonstrate an understanding of “significance”

• Understand the difference between the word ‘significance’ in common language vs. statistics
• Understand the concept of type I and type II errors
• Understand the concept of statistical power

6. Present work at an appropriate academic standard.

• A free statistics package, created by Ross Ihaka (Prof. at UoA) and Robert Gentleman in the early nineties
• Now covering everything from data processing to graphing
• Based on a command line interface
• R has become the data processing, analysis, plotting and programming tool in science
• Your future employer may well ask for your R skills!
• Commercial users include Google, Pfizer, Shell, Bank of America… but also DOC, crown research institutes, councils…
• Enormous free online resources (helplists, code)
• If this is the first and last time you do stats, look at R as an important learning experience!

• The short answer is: there is no way around R if you are doing a BSc at AUT and probably any top-ranking university
• You will use it in Research Techniques (BIOL602), Terrestrial Ecology (ECOL602), Biogeography (ENVS622), Pacific Island Ecosystems (ECOL732), Research Project (SCIE701), and most definitely in your postgraduate studies!
• You will need it for your Honours, Masters and PhD if that’s where you’re heading
• Our recommendation to use R is not commercially motivated (R is free)
• AUT is a top-ranking uni and we are simply keeping up with the best universities

…you will receive an ‘R Markdown’ file for every lab:

• the file is ready to be compiled or ‘knitted’, but you still need to do work… (fill things in, edit it and write short pieces of R code)
• you don’t need to submit the lab reports, but they are the best practice for your assignments and your test/exam!
• Feedback on your lab reports is given during tutorials!

• Yes, you MUST use R markdown
• We will help you with the technicalities, it may appear messy at first but you will see the benefits soon and get used to the process
• Some flexibility is expected (trouble shooting, using google)
• You may delete the instructions if you want to, the lab reports are living, interactive, and personalised documents!
• You are encouraged to add extra stuff, but keep it concise and well organised. Messy and partly wrong additions will end up confusing you!
• Use ‘>’ before you type, it makes it easier for you to distinguish your answers from our instructions

• They are based on the lab reports, but they are NOT the same
• They need to be handed in on Canvas
• Make sure you save both your code (the Rmarkdown file) and your (compiled) word file!
• ALWAYS submit the word file, not the Rmarkdown file
• You must submit your own report. If you plagiarise your answers, there will be drastic consequences

• Download and install R (google ‘R software’ > Download R for Windows/mac)
• Close R
• Download and install RStudio (google ‘Rstudio’)
• Open RStudio
• Click on Packages > Install, select ‘knitr’, ‘markdown’, and ‘rmarkdown’
• Go to file > new file > R Markdown
• Start playing :)

• How to study for this paper
• What resources to use
• How/when to hand in the assignments (this is most important!)
• How to use RMarkdown for lab reports/assignments

• R
• RStudio
• RMarkdown
• to ‘knit’ (compile) an Rmarkdown file
• Console
• Editor
• R function, arguments, argument names