Category: Statistics – Experimental Design & Data Analysis Using R

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Output: According to this petal length and petal width, sepal length and petal length , Sepal length, and petal width are highly correlated. This leads to multicollinearity. This issue can be reduce using PCA analysis. Now there is no correlation between multiple variables therefore there is no  multicollinearity issue. Output: BIPLOT is useful to understand what is … Read More “Principal component analysis (PCA) in R studio” »

Multivariate analysis is a statistical methods that perform simultaneous analysis of multiple variables. identify the relationships among variables and identify the patterns and trends of the information. Types of multivariate analysis Factor Analysis Principal component analysis Cluster analysis Discriminant analysis Canonical correlation analysis. Factor Analysis Factor analysis is used to identify the factors or dimensions … Read More “Multivariate Analysis” »

Mainly there are four experimental design models. Completely Randomized Design (CRD) Random Completely Block Design (RCBD) Split Plot Design Latin Square Design (LSD) Completely Randomized Design (CRD) Experimental unit in the population has an equal equally treated with the treatments/randomly treated. E.g. – Effect of the fertilizer is monitored of crop cultivation, plants (Experimental unit) … Read More “Experimental Design Models” »

Most commonly used statistical sampling methods are shown below. Random sampling Stratified sampling Cluster sampling Systematic sampling Random sampling Each member in the population has equal chance to being selected. This can be used when the population is homogeneous. E.g. – Select the plant leaves randomly for the measure the photosynthetic rate E.g – Plant … Read More “Statistical Sampling Methods.” »

The Spearman correlation is a non-parametric alternative to the linear correlation coefficient. Assumptions of Spearman Correlation Random sampling Independent measurements Data should be measurements counts or ordinal Steps in Spearman Correlation

Mann Whitney U test is the alternative for the two independent sample t-test Assumption for Mann Whitney u test Random sampling Independent measurement Two population should same shape of distribution. Steps for Mann Whitney U test.

The sign test is used for hypothesis testing by concerning the value of the median for one population, and comparing the median differences for two variables for two dependent samples. Assumptions of the sign test. Random sampling Independent measurements Data should be measurement, counts or scores. Sign test for one population in Excel Step 1: … Read More “Sign Test” »

Nonparametric statistics deals with median rather than mean, because mean can be easily influenced by the outliers or skewness of data, and data are not normally distributed. The main difference between parametric and nonparametric tests is that in parametric tests, data are normally distributed, whereas in nonparametric tests, data are not normally distributed. Assumptions of … Read More “Nonparametric Statistics.” »

Linear regression analysis describe the relation between two or more variables,and find the best fit line to the graph and equation of the straight line; that is used for make predictions. Assumption of Regression Analysis Random sampling independent measurements or observations. Dependent variable should be normally distributed equal variance. R2 = Coefficient of Determination/ Linear … Read More “Linear Regression Analysis Using R” »