1. Phylogenetics & Phylogeography (lecture-part 1)

2. Phylogenetics & Phylogeography (lecture-part 2)

3. Phylogenetics & Phylogeography (lecture-part 3)

4. Phylogenetics & Phylogeography (lecture-part 4)

5. Phylogenetics & Phylogeography (lecture-part 5)

6....

1. Molecular epidemiology - practical 1

2. Molecular epidemiology - practical 1 review

3. Molecular epidemiology - practical 2 part 1

4. Molecular epidemiology - practical 2 part 2

5. Molecular epidemiology - practical 2 review

6. Molecular epidemiology - practical 3 part 1

7....

1. Phylogenetic analysis of pathogens (lecture - part1)

2. Phylogenetic analysis of pathogens (lecture - part2)

3. Phylogenetic analysis of pathogens (lecture - part3)

4. Phylogenetic analysis of pathogens (lecture - part4)

5. Phylogenetic analysis of pathogens (lecture - part5)

6....

1. Introduction

2. Many Views of A Pathway(Lecture)

3. Pathway Models & Pathway Types (Lecture)

4. Sources of ‘Pathway Data’ (Lecture)

5. Issues (Lecture)

6. SBGN (Lecture)

7. mEPN (Lecture)

8. Practical Session: Pathway to Pathway Construction - yED(1)

9. Practical...

1.Introduction to data analysis - course overview (lecture)

2.Introduction to networks (lecture)

1. BioLayout Express3D - background and benefits (lecture)

2. BioLayout Express3D – getting started 1 (Practical Session)

3. BioLayout Express3D – getting started 2 (Practical...

1. Phylogenetics & Phylogeography Practical - Overview

2. Phylogenetics & Phylogeography Practical 1

3. Phylogenetics & Phylogeography Practical 2 - Part 1

4. Phylogenetics & Phylogeography Practical 2 - Part 2

5. Phylogenetics & Phylogeography Practical 3

6....

1. Introduction

2. Many Views of A Pathway(Lecture)

3. Pathway Models & Pathway Types (Lecture)

4. Sources of ‘Pathway Data’ (Lecture)

5. Issues (Lecture)

6. SBGN (Lecture)

7. mEPN (Lecture)

8. Practical Session: Pathway to Pathway Construction - yED(1)

9. Practical...

1.Introduction

1. Data summary

2. Demo – Data Summary

3. Demo – Data Summary cont.

4. Data summary using PROC TABULATE and PROC GPLOT

5. Statistical analysis

6. Example analysis

7. Example analysis cont.1

8. Example analysis cont.2

9. Reading and manipulating...

1. A Brief Introduction to R

2. Very Basic Functions

3. Demo Data - Rivers

4. Demo Data - Rivers (Cont.)

5. Demo Data - Orange

6. Demo Data - Orange (Cont.)

7. Demo data - Anderson's Iris Data

8. Demo Data - US Judge Rating

9. Summary

1. Introduction

2. Introduction to Data Summary & Types of data

3. Continuous Data – Measures of Location

4. Continuous Data – Measures of Spread & Accuracy

5. Continuous Data with Skewed or Odd Distributions – Measures of Spread

6. Choosing Appropriate Summary Statistics...

1. Introduction

2. Hypothesis Testing

3. Choosing Between Parametric & Non-Parametric Tests

4. T-Test for Comparing Two Groups

5. T-Test – Interpreting Software Output

6. Paired T-Test

7. Mann-Whitney U-Test to Compare Two Groups When Data Are Not Normally Distributed

8....

1. Introduction

2. Achieving Statistical Significance

3. Achieving Generalisable Results

4. Avoiding Bias

5. Questions to Address When Planning

6. Examples of Experimental Designs

7. Randomised Block Design

8. Crossover & Repeated Measures Designs

9. Pilot & Proof...

1. Introduction

2. Sample Size Calculation – Basic Formula

3. Example 1

4. Calculating In Terms of ‘Difference to Detect’ & Power From Sample Size

5. Example 2

6. Different Numbers Per Group

7. Different Variances for Groups

8. Binary Data

9. Allowing for...

1. Introduction

2. One-Way Analysis of Variance (ANOVA) Recap

3. Two-Way ANOVA - Assessing Two Effects in Same Model

4. Two-Way ANOVA - Allowing for Structure in The Data

5. Paired T-Test Using Two-Way ANOVA & ANOVA With More Effects

6. Regression Recap

7. General Linear...

1. Introduction

2. What Are Mixed Models?

3. Potential Advantages of Mixed Models

4. Historical Perspective

5. Example 1: Biological & Technical Replicates

6. Example 2: Rabbit Inspiration Time – Sources of Variation

7. Non-Normal Data

8. Example 3: Non-normal data

9....

1. Introduction

2. Potential Analysis Approaches

3. Mixed Model Analysis

4. Covariance Pattern Model

5. Example – Covariance Pattern Model

6. Significance Testing for Fixed Effects

7. Model Checking

8. Random Coefficients (Slopes) Models

9. Example – Random Coefficients...