SC1-10 Statistics for Chemists
This course is made up of 8 sessions which will be based around the topics below: unlike other courses in the Graduate Lecture Series, it is essential to attend all 8 sessions to benefit from this training. Places are limited so please be absolutely certain upon booking that you will commit to the entire course.
Number of sessions: 8
| # | Date | Time | Venue | Trainer | |
|---|---|---|---|---|---|
| 1 | Mon 15 Jan 2018 10:00 - 12:00 | 10:00 - 12:00 | G30 | map | Matt Castle |
| 2 | Wed 17 Jan 2018 10:00 - 12:00 | 10:00 - 12:00 | G30 | map | Matt Castle |
| 3 | Mon 22 Jan 2018 10:00 - 12:00 | 10:00 - 12:00 | G30 | map | Matt Castle |
| 4 | Wed 24 Jan 2018 10:00 - 12:00 | 10:00 - 12:00 | G30 | map | Matt Castle |
| 5 | Mon 29 Jan 2018 10:00 - 12:00 | 10:00 - 12:00 | G30 | map | Matt Castle |
| 6 | Wed 31 Jan 2018 10:00 - 12:00 | 10:00 - 12:00 | G30 | map | Matt Castle |
| 7 | Mon 5 Feb 2018 10:00 - 12:00 | 10:00 - 12:00 | G30 | map | Matt Castle |
| 8 | Wed 7 Feb 2018 10:00 - 12:00 | 10:00 - 12:00 | G30 | map | Matt Castle |
1. Introducing R and RStudio. Familiarise participants with software; R interface and scripts; Calculations; Variables; Functions; Data Structures.
2. Data Visualisation, Manipulation and Summaries. Importing and exporting real data; Interrogating dataframes; Plotting and visualisation techniques; Extracting summary statistics.
3. Comparing up to two samples. Overview of hypothesis testing; One and two sample hypothesis tests; Binomial test; Chi-squared test (extrinsic and intrinsic); Fisher’s exact test; One sample t-test; Student’s t-test; Mann-Whitney test; paired t-test; Wilcoxon signed-rank test.
4. Comparing more than two samples One-way analysis of variance (ANOVA); Assumptions for ANOVA (Shapiro-Wilk test, Bartlett’s test, Wald-Wolfowitz test); Kruskal-Wallis test.
5. Comparing two continuous variables. Pearson’s product-moment correlation coefficient; Spearman’s rank correlation coefficient; Simple linear regression; Assumptions of linear regression.
6. Multiple Predictor Variables. Categorical predictors with continuous response: Two-way ANOVA; Categorical and continuous predictors with continuous response: Blending ANOVA and regression.
7. Linear Model Framework. Continuous response variables, multiple predictor variables; Constructing and interpreting linear models; Revisiting ANOVA and regression; Model selection; stepwise regression and AIC
8. Logistic regression and Generalised Linear models.
9. Experimental Design Errors, power, randomization, replication, good and bad designs, determining sample size, power analysis
10. Analysing Data and Writing Statistical Reports Bringing everything together in a systematic fashion., structuring statistical analyses and presenting results clearly.
Booking / availability