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Venue:
Mon 9 Jan, Wed 11 Jan, ... Wed 8 Feb 2017
09:30, ...

Venue: G30

Provided by: Department of Chemistry


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SC1-10 Statistics for Chemists

Mon 9 Jan, Wed 11 Jan, ... Wed 8 Feb 2017

Description

This course is made up of 10 sessions which will be based around the topics below: unlike other courses in the Graduate Lecture Series, it is essential to attend all 10 sessions to benefit from this training. Places are limited so please be absolutely certain upon booking that you will commit to the entire course.

Sessions

Number of sessions: 10

# Date Time Venue Trainer
1 Mon 9 Jan 2017   09:30 - 12:00 09:30 - 12:00 G30 map Matt Castle
2 Wed 11 Jan 2017   09:30 - 12:00 09:30 - 12:00 G30 map Matt Castle
3 Mon 16 Jan 2017   10:00 - 12:00 10:00 - 12:00 G30 map Matt Castle
4 Wed 18 Jan 2017   10:00 - 12:00 10:00 - 12:00 G30 map Matt Castle
5 Mon 23 Jan 2017   10:00 - 12:00 10:00 - 12:00 G30 map Matt Castle
6 Wed 25 Jan 2017   10:00 - 12:00 10:00 - 12:00 G30 map Matt Castle
7 Mon 30 Jan 2017   10:00 - 12:00 10:00 - 12:00 G30 map Matt Castle
8 Wed 1 Feb 2017   10:00 - 12:00 10:00 - 12:00 G30 map Matt Castle
9 Mon 6 Feb 2017   10:00 - 12:00 10:00 - 12:00 G30 map Matt Castle
10 Wed 8 Feb 2017   10:00 - 12:00 10:00 - 12:00 G30 map Matt Castle
Objectives

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.

Theme
Statistics for Chemists

Booking / availability