![We estimate the β^2= 1:4625 kg/ha, which is the effect of density 2 in comparison to density
1 for the Barkant genotype which then becomes 2:9375 + 1:4625 = 4:400
The null hypothesis that the interaction effect is zero can not be rejected, while the two null hypotheses that the main effects are zero can be rejected (at a signi cance level of 0.05). for Leverage and further diagnostic plots, it is good to obtain the predicted mean (i. e. the fi tted value) for each group according to our model.]()
We estimate the β^2= 1:4625 kg/ha, which is the effect of density 2 in comparison to density
1 for the Barkant genotype which then becomes 2:9375 + 1:4625 = 4:400
The null hypothesis that the interaction effect is zero can not be rejected, while the two null hypotheses that the main effects are zero can be rejected (at a signi cance level of 0.05). for Leverage and further diagnostic plots, it is good to obtain the predicted mean (i. e. the fi tted value) for each group according to our model. ![Visualization- plots of mean yield of turnip against the combined effects of genotypes and the density in four Blocks B1, B2, B3 and B4]()
Visualization- plots of mean yield of turnip against the combined effects of genotypes and the density in four Blocks B1, B2, B3 and B4 ![Visualization- plots of mean yield of turnip against the combined effects of genotypes and the density showed no interaction effects]()
Visualization- plots of mean yield of turnip against the combined effects of genotypes and the density showed no interaction effects ![Q-Q plots of transformed turnip data indicates the normalized plots without outliers]()
Q-Q plots of transformed turnip data indicates the normalized plots without outliers ![Accounting for non-normality- two options: 1.abandon normal distribution based methods and directly switch to nonparametric or robust methods, 2. or we can try a transformation g such that the sample g(Y ) produces residuals looks as if they came from a normal distribution.]()
Accounting for non-normality- two options: 1.abandon normal distribution based methods and directly switch to nonparametric or robust methods, 2. or we can try a transformation g such that the sample g(Y ) produces residuals looks as if they came from a normal distribution. ![Graphical normality checking- This works exactly as for one-way ANOVA. Considering the QQ plot of the residuals indicates that The QQ plot shows some problems with a perhaps slightly heavy upper tail and one outlier. Perhaps robust methods or nonparametric methods could be useful to control the effect of the outlier]()
Graphical normality checking- This works exactly as for one-way ANOVA. Considering the QQ plot of the residuals indicates that The QQ plot shows some problems with a perhaps slightly heavy upper tail and one outlier. Perhaps robust methods or nonparametric methods could be useful to control the effect of the outlier ![Visualization of turnip yield datasets using ggplots - combined effects of genotypes and density]()
Visualization of turnip yield datasets using ggplots - combined effects of genotypes and density ![Another Visualization of turnip yield datasets using boxplots- combined effects of genotypes and density]()
Another Visualization of turnip yield datasets using boxplots- combined effects of genotypes and density ![First Visualisation of mcomway.turnip datasets using boxplots indicates that The Barkant variety seems to have produced a slightly higher average yield than Marko; # The research question is how the planting density in kg/ha affects the mean yield of turnips of two genotypes]()
First Visualisation of mcomway.turnip datasets using boxplots indicates that The Barkant variety seems to have produced a slightly higher average yield than Marko; # The research question is how the planting density in kg/ha affects the mean yield of turnips of two genotypes ![Summary function of InsectSprays Data showing the p-values, R squared and F-statistics]()
Summary function of InsectSprays Data showing the p-values, R squared and F-statistics ![Visualization of InsectSprays Data using Scipot barplot]()
Visualization of InsectSprays Data using Scipot barplot ![The linear model of InsectSprays Data using Speci fic comparisons of Multicomp Library to redefine the reference level for Dunnett and the order of the levels for the sequence as one wish]()
The linear model of InsectSprays Data using Speci fic comparisons of Multicomp Library to redefine the reference level for Dunnett and the order of the levels for the sequence as one wish ![Visulaization of InsectSpray Dataset- using the stripplot function from the lattice package]()
Visulaization of InsectSpray Dataset- using the stripplot function from the lattice package ![Accounting for non-normality using the transformation approach and the Residual plots]()
Accounting for non-normality using the transformation approach and the Residual plots ![Graphical normality checking of InsectSpray Datasets using normal quantile-quantile (QQ) plots]()
Graphical normality checking of InsectSpray Datasets using normal quantile-quantile (QQ) plots ![Graphical normality checking of InsectSpray Datasets using normal quantile-quantile (QQ) plots]()
Graphical normality checking of InsectSpray Datasets using normal quantile-quantile (QQ) plots ![Visualization of InsectSpray Datasets produced line plot]()
Visualization of InsectSpray Datasets produced line plot ![Description of my Project2: My Journey on becoming Data Analyst and Machine Learning Engineer]()
Description of my Project2: My Journey on becoming Data Analyst and Machine Learning Engineer ![Description of my Project1: My Journey on becoming Data Analyst and Machine Learning Engineer]()
Description of my Project1: My Journey on becoming Data Analyst and Machine Learning Engineer The Project covered broad topics as: The one-way layout; Parametric models: one-way ANOVA ,Pairwise group comparisons, NonParamteric Robust methods; Design of experiments; Factor level combinations; Fixed and random effects; Sample size; The two-way layout; Balanced factorial two-way ANOVA with replications; Balanced factorial two-way ANOVA without replications; Unbalanced designs; Data Analysis using R- programming; Graphical Data presentation; Experimental Design; Stastiscal pinciples; Hypothesis testing; Simple Linear regression.
The major focus area is on Analysis of variance and the aim of the analysis is usually to investigate whether the mean of the numeric variable differs significantly between the groups and to quantify the divergence.
Published:March 21, 2022
