Prepare the data for entry into your statistical software package (see below). S teps in the statistical analysis of your experimentġ. Example 1 A two-sample experiment using a t-test,Ħ. Steps in the statistical analysis of your experimentģ. The raw data is available so that you can try it out on your own software, should you want to do so.ġ. It is a long section about statistical analysis rather than experimental design, giving a single example of each type of statistical analysis. You can skip it and still self-certify that you have covered the course. 14.3 - Measures of Association for Binary Variables.14.2 - Measures of Association for Continuous Variables.13.5 - Obtain the Canonical Coefficients.13.4 - Obtain Estimates of Canonical Correlation.Test for Relationship Between Canonical Variate Pairs 13.1 - Setting the Stage for Canonical Correlation Analysis.Lesson 13: Canonical Correlation Analysis.12.7 - Maximum Likelihood Estimation Method.12.6 - Final Notes about the Principal Component Method.12.4 - Example: Places Rated Data - Principal Component Method.11.7 - Once the Components Are Calculated.11.6 - Example: Places Rated after Standardization.11.5 - Alternative: Standardize the Variables.11.4 - Interpretation of the Principal Components.11.2 - How do we find the coefficients?.11.1 - Principal Component Analysis (PCA) Procedure.Lesson 11: Principal Components Analysis (PCA).10.5 - Estimating Misclassification Probabilities.10.1 - Bayes Rule and Classification Problem.9.6 - Step 3: Test for the main effects of treatments.9.5 - Step 2: Test for treatment by time interactions.9.3 - Some Criticisms about the Split-ANOVA Approach.8.10 - Two-way MANOVA Additive Model and Assumptions.8.9 - Randomized Block Design: Two-way MANOVA.8.7 - Constructing Orthogonal Contrasts.8.4 - Example: Pottery Data - Checking Model Assumptions.8.2 - The Multivariate Approach: One-way Multivariate Analysis of Variance (One-way MANOVA).8.1 - The Univariate Approach: Analysis of Variance (ANOVA).Lesson 8: Multivariate Analysis of Variance (MANOVA).7.2.8 - Simultaneous (1 - α) x 100% Confidence Intervals.7.2.7 - Testing for Equality of Mean Vectors when \(Σ_1 ≠ Σ_2\).7.2.6 - Model Assumptions and Diagnostics Assumptions.7.2.4 - Bonferroni Corrected (1 - α) x 100% Confidence Intervals.7.2.2 - Upon Which Variable do the Swiss Bank Notes Differ? - Two Sample Mean Problem.7.2.1 - Profile Analysis for One Sample Hotelling's T-Square.7.1.15 - The Two-Sample Hotelling's T-Square Test Statistic.7.1.12 - Two-Sample Hotelling's T-Square.7.1.11 - Question 2: Matching Perceptions.7.1.8 - Multivariate Paired Hotelling's T-Square.7.1.7 - Question 1: The Univariate Case.7.1.4 - Example: Women’s Survey Data and Associated Confidence Intervals.7.1.1 - An Application of One-Sample Hotelling’s T-Square.Lesson 7: Inferences Regarding Multivariate Population Mean.6.2 - Example: Wechsler Adult Intelligence Scale.Lesson 6: Multivariate Conditional Distribution and Partial Correlation.5.2 - Interval Estimate of Population Mean.5.1 - Distribution of Sample Mean Vector.Lesson 5: Sample Mean Vector and Sample Correlation and Related Inference Problems.4.7 - Example: Wechsler Adult Intelligence Scale.4.6 - Geometry of the Multivariate Normal Distribution.4.4 - Multivariate Normality and Outliers.4.3 - Exponent of Multivariate Normal Distribution.Lesson 4: Multivariate Normal Distribution.Lesson 3: Graphical Display of Multivariate Data.Lesson 2: Linear Combinations of Random Variables.
1.5 - Additional Measures of Dispersion.Lesson 1: Measures of Central Tendency, Dispersion and Association.Error (a) is the effect of subjects within treatments and Error (b) is the individual error in the model. The sources of the variation include treatment Error (a) the effect of Time the interaction between time and treatment and Error (b). N: the total number of all experimental units
It involves modeling the data using the linear model shown below: The Split-plot ANOVA is perhaps the most traditional approach, for which hand calculations are not too unreasonable.
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