Chemistry: 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.

• Chemistry postgraduate students
• Further details regarding eligibility criteria are available
• If you are from outside the Department of Chemistry, please arrive 15 minutes early and wait to be collected from reception

Number of sessions: 8

• Introducing R and RStudio, familiarise participants with software; R interface and scripts; Calculations; Variables; Functions; Data Structures.
• Data Visualisation, Manipulation and Summaries.,Importing and exporting real data; Interrogating dataframes; Plotting and visualisation techniques; Extracting summary statistics.
• 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.
• 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.
• Comparing two continuous variables, Pearson’s product-moment correlation coefficient; Spearman’s rank correlation coefficient; Simple linear regression; Assumptions of linear regression.
• Multiple Predictor Variables, categorical predictors with continuous response: Two-way ANOVA; Categorical and continuous predictors with continuous response: Blending ANOVA and regression.
• Linear Model Framework -Continuous response variables, multiple predictor variables; Constructing and interpreting linear models; Revisiting ANOVA and regression; Model selection; stepwise regression and AIC
• Logistic regression and Generalised Linear models.
• Experimental Design, Errors, power, randomization, replication, good and bad designs, determining sample size, power analysis
• Analysing Data and Writing Statistical Reports

Bringing everything together in a systematic fashion., structuring statistical analyses and presenting results clearly.

• Eight sessions of two hours

• Yearly