Welcome and Course Overview
Module 1: Uncertainty in data
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Welcome to Module 1 - Things aren't always what they seem (1:04)
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Excel Tools - Descriptive Statistics (3:21)
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Precision and Accuracy (4:34)
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Spread and Deviation
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Activity: Precision and Accuracy
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Random Error (9:15)
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The Normal Distribution (3:17)
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Activity: The normal distribution
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How rare is rare?
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Example 1.1 - Uncertainty and probability (4:53)
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Activity: Build simple equation
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Exercise 1
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Population and Sampling (7:05)
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Activity: Sampling Tools
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The standard error (7:46)
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Sampling a Population - the standard error
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Example 1.2 - Standard error (1:30)
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Central Limit Theorem (Concept) (2:50)
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Central Limit Theorem - Sample Size
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Activity: Central Limit Theorem
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Confidence intervals and confidence limits (7:57)
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Confidence Limits (Error Bars)
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Example 1.3 - Confidence limits for large samples (1:12)
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Sample size for sample mean
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Activity: Experiment summary
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Exercise 2
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Error propagation in simple formulae & Example 1.4 (5:47)
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Tolerance limits & Example 1.5 (2:56)
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Relative errors
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Relative Error & Example 1.6 (6:54)
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Propagation of Error & Example 1.7 (5:58)
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Further Examples - Example 1.8 (3:43)
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Further Examples - Example 1.9 (3:09)
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Exercise 3
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Exercise 4
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Knowledge Check - Module 1
Module 2: Comparing quantities and hypothesis testing
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Module 2 Introduction (0:36)
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t-tests (5:11)
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The t-distribution
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Example 2.1 - Confidence interval using t (6:35)
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Hypothesis (significance) Testing (2:20)
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Example 2.2 - Two sample t-test: 1-sided (9:14)
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Testing the Significance of Observed Differences
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t-test: 2-sided for two means & Worked Example 2.2 (18:22)
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Confidence limits on the difference & Worked Example 2.3 (9:24)
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Exercise 5
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Paired t-test
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Paired t-test & Worked Example 2.4 (13:03)
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Beware the mean (2:27)
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Exercise 6
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Compare sample mean with standard & Worked Example 2.5 (7:36)
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t-tests summary and assumptions (4:44)
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Exercise 7
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Type I and Type II errors
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Sample Size for t-tests (8:31)
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Activity: Hypothesis Testing with error analysis
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The formula for sample size (7:49)
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Example 2.6 (4:39)
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Example and Excel Worked Example 2.7 (7:44)
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Interpreting Errors (Risks) (10:14)
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Final Hypothesis Testing (2:16)
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Exercise 8
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Testing Outliers, Example & Worked Example 2.8 (10:55)
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Grubbs Test
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F-test
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F-test to compare two variances & Example 2.9 (9:44)
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Exercise 9
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Principles of ANOVA
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Analysis of Variance (ANOVA) & Example 2.10 (10:10)
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Activity: ANOVA
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Example and Worked Example 2.11 (7:11)
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The one-way ANOVA
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Activity: One-way ANOVA
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Exercise 10
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Exercise 11
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Chi-square test
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Chi-square test & Example 2.12 (5:16)
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Example and Excel Worked Example 2.13 (5:57)
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Level of Significance (6:34)
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P-value summary (5:25)
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Exercise 12
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Exercise 13
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Knowledge Check - Module 2
Module 3: Developing models with regression analysis
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Introduction (3:28)
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Activity: Linear Regression
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Example & Worked Example 3.1 (10:57)
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What is R-squared and R (8:06)
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R-squared, R and Correlations
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Regression Analysis - Correlations (2:27)
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Exercise 14
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Multiple Linear Regression - Example 3.2 (5:45)
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Worked Example 3.2 (7:20)
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Process Model (1:59)
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Exercise 15
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General Approach to Regression Modelling
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How to Judge Regression Models
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Judging Regression - Outliers Example 3.2 (revisited) (6:41)
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Improving the Regression Model (8:44)
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Scaling x-variables - Example 3.2 (revisited) (3:40)
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Comparing Two Trendlines
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Considerations for Comparing Two Trendlines (5:01)
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Example and Worked Example 3.3 (13:59)
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Exercise 16
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Knowledge Check - Module 3
Module 4: Design of experiments
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Module 4 Introduction (4:27)
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Preliminary Steps (3:55)
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Activity: Design of Experiments-Preliminary Steps
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Design of Experiments - Nomenclature
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General Mathematical Model (2:35)
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Introduction to Randomised Block Design
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Randomised Block Design (3:23)
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Example and Worked Example 4.1 (7:34)
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Example and Worked Example 4.2 (9:58)
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Collecting Data (1:32)
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Exercise 17
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Exercise 18
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Factorial Experiments (4:19)
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Factorial Design
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Advantages of Factorial Designs (6:14)
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Analysis of Factorial Experiments
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Example 4.3 (5:30)
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Example 4.4 (2:23)
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Activity: Experimental Error Estimates
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Exercise 19
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Yates Analysis (2:15)
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Yates Analysis of 2^n factorial
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Example 4.5 (5:29)
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Yates Mathematical Model (5:30)
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Example 4.6 (12:20)
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Exercise 20
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Fractional Factorials (1:33)
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Response Surface Designs (5:00)
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Response Surface Designs - CCRDs
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Knowledge Check - Module 4
Module 5: Designing, running and analysing plant trials
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Module 5 Introduction (1:52)
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Overview (6:16)
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Process Variability (10:19)
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Conducting a Plant Trial
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Paired Trial (5:05)
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Paired Trial and Example 5.1 (2:41)
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Randomised Block Design & Example 5.2 (3:15)
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Design Criteria
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Worked Example 5.2 (2:17)
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Modelling - Comparing Trendlines
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Regression Models & Example 5.3 (4:53)
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Worked Example 5.3 (4:13)
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Multiple Regression Process Model
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Multi-linear Regression Example 5.4 and Worked Example 5.4 (9:57)
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Modelling Time series - Intervention Analysis
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Modelling a Time Series (3:49)
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Example 5.5 and Worked Example 5.5 (5:42)
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Time Series and Cumulative Sum (2:09)
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Cusum Charts and Worked Example 5.6 (5:59)
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Recovery Cusum (4:41)
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Exercise 21
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Exercise 22
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Cautionary Tale (6:31)
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Analysing Plant Trials (1:17)
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Which Method? (1:18)
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Practical Matters (2:51)
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Pro-Forma (5:30)
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Knowledge Check - Module 5
Module 6: Course summary and interactive flowchart
