Exam Revision

# Exam Revision
## Lecture 11
### Drs Jennifer Mankin and Milan Valášek
### 30 April 2021

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## Today

- Exam Details

- Types of Questions
  
- Concepts to Revise

- Kahoot! Revision Practice
  
- Tips for Success

- Revision Resources

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## Exam Details

- Multiple choice exam online via Canvas

- Weighted 50% of your overall module mark

- 120 minutes (two hours) + 30 minutes for technical problems

- Must attempt the exam in the 24-hour period it is available

- If you have any adjustments, these will be applied automatically
  
  - If something comes up unexpectedly, [apply for exceptional circumstances](https://student.sussex.ac.uk/assessment/exceptional-circumstances/)
  
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- Questions cover **all lectures and tutorials**

- Includes reading and interpreting `R` output!
  
  - You will **NOT** need to use `R` yourself to complete the exam

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## Types of Questions

- Definitions and concepts
  
- Reading R output for analyses, figures, and tables

- Some calculations (no more than basic maths)

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## Concepts to Revise

Part one: Foundations of Statistics

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## Week 1

### Lecture

- Distributions

- The normal and standard normal distributions

- Samples and estimates

- The sampling distribution and standard error

- The Central Limit Theorem

]

### Tutorial

- Reading tibbles of data

- Using the pipe `%>%`

]

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## Week 2

### Lecture

- Point and interval estimates

- Confidence intervals

- The *t*-distribution
]

### Tutorial

- Critical values

- Cut-off points

]

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## Week 3

### Lecture

- Types of hypotheses

- Logic and procedure of null hypothesis significance testing (NHST)

- Using and interpreting *p*-values

]

### Tutorial

- Types of hypotheses

- Logic and procedure of null hypothesis significance testing (NHST)

- Using and interpreting *p*-values

]

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## It's Kahoot! Time

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## Concepts to Revise

Part two: Statistical Testing

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## Week 4

### Lecture

- Understanding the value of *r* and its relationship to causation

- Reading a correlation matrix and scatterplot

- Interpreting and reporting significance tests of *r*

]

### Tutorial

- Reading and interpreting the output of `cor()` and `cor.test()`

- Reporting the results of a correlation analysis in APA style

]

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## Week 5

### Lecture

- Concepts behind goodness-of-fit and association

- Reading tables and figures of counts

- Interpreting and reporting significance tests of `\(\chi^2\)`

]

### Tutorial

- Reading and interpreting the output of `chisq.test()` and tables of expected and observed counts

- Reporting the results of a `\(\chi^2\)` analysis in APA style

]

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## Week 6

### Lecture

- Concepts behind comparing two means

- Independent and paired-samples *t*-tests

- Where the *t*-statistic comes from and what it means

- How to read histograms and means plots

]

### Tutorial

- Reading and interpreting a means plot

- Reading and interpreting the output of `t.test()`

- Reporting the results of a *t*-test in APA style

]

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## It's Kahoot! Time

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## Concepts to Revise

Part three: Stats Wars (the Linear Model)

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## Week 7

### Lecture

- Concepts behind statistical modeling

- The equation for a linear model with one predictor

- How to interpret *b*-values in the linear model equation

- Using the equation to predict an outcome for given values of the predictors

- How to read scatterplots and lines of best fit
]

### Tutorial

- Understanding the meaning of *b*<sub>0</sub>

- Using the `lm()` function to create a linear model

- Interpreting the output from `lm()` and translating it into the linear model equation
]

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## Week 8

### Lecture

- How to interpret *b*-values in the linear model equation

- Confidence intervals and *p*-values for *b* estimates

- Interpreting `\(R^2\)`
]

### Tutorial

- Deviations and residuals

- Using the `lm()` function to create a linear model

- Interpreting the output of `summary()`, `broom::tidy()`, and `broom::glance()`
]

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## Week 9

### Lecture

- Adding predictors to the linear model

- Interpreting *b*-values for multiple predictors

- Comparing *b*-values with standardised *B*s

- Transforming variables in the model
]

### Tutorial

- Using the `lm()` function to create a linear model with multiple predictors

- Interpreting the output of `summary()`, `broom::tidy()`, and `broom::glance()`

- Interpreting the output of `QuantPsyc::lm.beta()`
]

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## Week 10

### Lecture

- Categorical predictors in the linear model

- Comparing models with *F*

- Interpreting `\(R^2\)` and adjusted `\(R^2\)` for models with multiple predictors
]

### Tutorial

- Using the `lm()` function to create a linear model

- Interpreting the output of `anova()` for model comparison
]

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## It's Kahoot! Time

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## Tips and Tricks

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## Tips for Revision

- Work through practicals again

- Do the tasks, don't just read the answers!

- Look through quiz answers

- Watch/read other resources

- e.g. Khan Academy, YouTube
  
- Ask questions on Piazza

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## Tips for Preparation
  
- Memorise key concepts and definitions

- e.g. the *p*-value, standard error
  
- Put together a glossary of terms

- Build it collaboratively if you like!

- Learn to ballpark numbers

- Know how to navigate the output for statistical tests

Lots of detail and more advice in [last week's StatsChats](https://and.netlify.app/statschats/recordings/?v=7&t=4231)!

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## Resources

- Sample exam: Half the length, all the fun

- Take as many times as you like on Quizzes!

- Same kind of questions as the real exam

- Handout for this lecture

- Use as a revision guide
  
- Everything from PAAS and AnD!

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# Have a fantastic summer!!!