Split Plot Design In Xlstat Full Factorial With#

The analysis of split-plot design takes into account the restriction on randomization. Making the design easier, and more cost effective to conduct. Your variable of interest should be continuous, be normally distributed, and have a similar spread across your groups.Split-plot designs are used to control the number of times a HTC factor must be changed. The Split Plot ANOVA is a statistical test used to determine if 2 or more repeated measures from 2 or more groups are significantly different from each other on your variable of interest.

Split Plot Design In Xlstat Full Factorial With

In the box, select Full factorial with 2 whole plots and 8 subplots. In Number of hard-to-change factors, select 1. From Total number of factors, select 4. In Type of Design, select 2-level split-plot (hard-to-change factors).

Normal Variable of Interest and Population Variance Known Menu Toggle Your StatsTest Is The Single Sample T-Test Normal Variable of Interest Menu Toggle All middle, all min, all max. By using this you can do your experiments using max (min or middle) together. One Sample Tests (single group) Menu ToggleYou can use Split-plot designs for response surface.

Your StatsTest Is The Independent Samples Z-Test Your StatsTest Is The Independent Samples T-Test Two Sample Tests (2 groups) Menu Toggle Your StatsTest Is The Single Sample Wilcoxon Signed-Rank Test Skewed Variable of Interest Menu Toggle

Many Samples Tests (3+ groups) Menu Toggle Your StatsTest Is The Wilcoxon Signed-Rank Test Your StatsTest Is The Paired Samples Z-Test Your StatsTest Is The Paired Samples T-Test Paired Samples (repeated measurements) Menu Toggle

(2 or more group variables) Your StatsTest Is The Split Plot ANOVA (one group variable) Your StatsTest Is The One-Way Repeated Measures ANOVA Your StatsTest Is The Kruskal-Wallis One-Way ANOVA (2 or more group variables) Your StatsTest Is The Factorial ANOVA (one group variable with covariate) Your StatsTest Is The One-Way ANCOVA

Your StatsTest Is The G-Test Of Goodness Of Fit More Than 10 In Every Cell (and more than 1000 in total) Menu Toggle Your StatsTest Is The One-Proportion Z-Test Your StatsTest Is The Exact Test Of Goodness Of Fit

Continuous Dependent Variable Menu Toggle Your Stats Test is Kendall’s Tau or Spearman’s Rho Your StatsTest is Point Biserial Correlation

Two or More Dependent Variables Menu Toggle Your StatsTest is Multiple Linear Regression Your StatsTest is the Mixed Effects Model More Than One Independent Variable Menu Toggle

Categorical Dependent Variable Menu Toggle Your StatsTest is Multiple Logistic Regression Your StatsTest is Mixed Effects Logistic Regression More than One Independent Variable Menu Toggle Your StatsTest is Simple Logistic Regression

Difference Continuous Methods Menu Toggle Your StatsTest is Ordinal Logistic Regression Ordered Categorical Dependent Variable Menu Toggle Your StatsTest is Multinomial Logistic Regression

ContinuousThe variable that you care about (and want to see if it is different across the 3+ groups) must be continuous. Assumptions mean that your data must satisfy certain properties in order for statistical method results to be accurate.The assumptions for the Split Plot ANOVA include:Let’s dive in to each one of these separately. Multivariate Multiple Linear RegressionThe Split Plot ANOVA is also sometimes called a Mixed Design ANOVA, Mixed Design Analysis of Variance, Two-Way Repeated Measures ANOVA (special case), or Mixed-Model Design ANOVA.Every statistical method has assumptions. Exact Test of Goodness of Fit (multinomial model) Difference Proportion/Categorical Methods Menu Toggle

This is important because if your groups were not randomly determined then your analysis will be incorrect. This means that if you wanted to see if drinking sugary soda makes you gain weight, you would need to randomly select a group of soda drinkers for your soda drinker group.The key here is that the data points for each group were randomly selected. Random SampleThe data points for each group in your analysis must have come from a simple random sample. Only use a Split Plot ANOVA with your data if the variable you care about is normally distributed.If your variable is not normally distributed, you should use the Friedman Test instead. In statistics, this is called being normally distributed (aka it must look like a bell curve when you graph the data). Normally DistributedThe variable that you care about must be spread out in a normal way.

Similar Spread across GroupsIn statistics this is called homogeneity of variance, or making sure the variable of interest is spread similarly between the two or more non-repeated measures groups (see image below). This assumption can be tested in most statistical software. For instance, if there are 3 repeated measures, then for each other grouping variable, the variance of (time 1 – time 2) should be the same as the variance of (time 1 – time 3) and so on. SphericityIn statistics this refers to the idea that the variances of the differences between each possible pair of repeated measures is the same for each group.