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class: middle, inverse, title-slide # End of the line ### Dr Jennifer Mankin ### 25 April 2022 --- <!-- Some slides have the exclude flag set to true because of the strikes; week 6 has no Lecure because it was cancelled --> ## Today - Exam Details - Types of Questions - Concepts to Revise - Kahoot! Revision Practice - Tips for Success - Revision Resources --- ## 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/) -- - Questions cover **all lectures and tutorials** - Includes reading and interpreting `R` output! - You will **NOT** need to use `R` yourself to complete the exam --- ## Types of Questions - Definitions and concepts - Reading `R` output for analyses, figures, and tables - Some calculations (no more than basic maths) --- class: center, middle ## Concepts to Revise Part one: Foundations of Statistics --- ## Week 1 .pull-left[ ### Lecture - Distributions - The normal and standard normal distributions - Samples and estimates - The sampling distribution and standard error - The Central Limit Theorem ] .pull-right[ ### Tutorial - Reading tibbles of data - Using the pipe `%>%` ] --- ## Week 2 .pull-left[ ### Lecture - Point and interval estimates - Confidence intervals - The *t*-distribution ] .pull-right[ ### Tutorial - Critical values - Cut-off points ] --- ## Week 3 .pull-left[ ### Lecture - Types of hypotheses - Logic and procedure of null hypothesis significance testing (NHST) - Using and interpreting *p*-values ] .pull-right[ ### Tutorial - Types of hypotheses - Logic and procedure of null hypothesis significance testing (NHST) - Using and interpreting *p*-values ] --- class: center, middle ## It's Kahoot! Time --- class: center, middle ## Concepts to Revise Part two: Statistical Testing --- exclude: true ## Week 4 .pull-left[ ### Lecture - Understanding the value of *r* and its relationship to causation - Reading a correlation matrix and scatterplot - Interpreting and reporting significance tests of *r* ] .pull-right[ ### Tutorial - Reading and interpreting the output of `cor()` and `cor.test()` - Reporting the results of a correlation analysis in APA style ] --- exclude: true ## Week 5 .pull-left[ ### Lecture - Concepts behind goodness-of-fit and association - Reading tables and figures of counts - Interpreting and reporting significance tests of `\(\chi^2\)` ] .pull-right[ ### 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 ] --- ## Week 6 ### Tutorial and Practical - Concepts behind comparing two means - Independent *t*-test - Where the *t*-statistic comes from and what it means - How to read histograms and means plots - Reading and interpreting a means plot - Reading and interpreting the output of `t.test()` - Reporting the results of a *t*-test in APA style --- exclude: true class: center, middle ## It's Kahoot! Time --- exclude: true class: center, middle ## Concepts to Revise Part three: Stats Wars (the Linear Model) --- ## Week 7 .pull-left[ ### 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 ] .pull-right[ ### 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 ] --- ## Week 8 .pull-left[ ### Lecture - How to interpret *b*-values in the linear model equation - Confidence intervals and *p*-values for *b* estimates - Interpreting `\(R^2\)` ] .pull-right[ ### Tutorial - Deviations and residuals - Using the `lm()` function to create a linear model - Interpreting the output of `summary()`, `broom::tidy()`, and `broom::glance()` ] --- ## Week 9 .pull-left[ ### 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 ] .pull-right[ ### 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 `parameters::model_parameters( standardize = "refit")` ] --- exclude: true ## Week 10 .pull-left[ ### Lecture - Categorical predictors in the linear model - Comparing models with *F* - Interpreting `\(R^2\)` and adjusted `\(R^2\)` for models with multiple predictors ] .pull-right[ ### Tutorial - Using the `lm()` function to create a linear model - Interpreting the output of `anova()` for model comparison ] --- class: center, middle ## It's Kahoot! Time --- class: center, middle ## Tips and Tricks --- ## 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 --- ## 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 --- ## 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! --- exclude: ![:live] .pollEv[ <iframe src="https://embed.polleverywhere.com/discourses/IXL29dEI4tgTYG0zH1Sef?controls=none&short_poll=true" width="800px" height="600px"></iframe> ] --- class: last-slide weekend background-image: url("/lectures_assets/end.jpg") background-size: cover # Have a fantastic summer!!!
class: slide-zero exclude: ![:live] count: false
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