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Venue:
Wed 15 Jan, Mon 20 Jan, ... Mon 10 Feb 2020
10:00 - 12:00

Venue: G30

Provided by: Department of Chemistry


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Chemistry: SC1-10 Statistics for Chemists

Wed 15 Jan, Mon 20 Jan, ... Mon 10 Feb 2020

Description

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.

Target audience
  • 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
Sessions

Number of sessions: 8

# Date Time Venue Trainer
2 Wed 15 Jan   10:00 - 12:00 10:00 - 12:00 G30 map Dr Matt Castle
3 Mon 20 Jan   10:00 - 12:00 10:00 - 12:00 G30 map Dr Matt Castle
4 Wed 22 Jan   10:00 - 12:00 10:00 - 12:00 G30 map Dr Matt Castle
5 Mon 27 Jan   10:00 - 12:00 10:00 - 12:00 G30 map Dr Matt Castle
6 Wed 29 Jan   10:00 - 12:00 10:00 - 12:00 G30 map Dr Matt Castle
7 Mon 3 Feb   10:00 - 12:00 10:00 - 12:00 G30 map Dr Matt Castle
8 Wed 5 Feb   10:00 - 12:00 10:00 - 12:00 G30 map Dr Matt Castle
9 Mon 10 Feb   10:00 - 12:00 10:00 - 12:00 G30 map Dr Matt Castle
Objectives
  • 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.

Duration
  • Eight sessions of two hours
Frequency
  • Yearly
Theme
Statistics for Chemists

Booking / availability