Agricultural Statistics

The blog explains correlation analysis in R (Reading time 10 min.) For data click here and for R-script click here   The Pearson correlation coefficient is a measure of the strength of a linear association between two variables and is denoted by r. The value of r ranges between -1 to +1. Let's see how to calculate correlation, the test of significance and fancy graphics to explain the relationship between variables in R. Step-I: Import the data  In the II quadrant click on import data and select "For Excel".  After that new dialogue box appears, click on "browse" and select your file and click on "import". After doing this step the "iris" data gets imported in the system and can be seen in Global Environment. Step-II: Load the script which you had downloaded. Let's understand the script step by step. Calculating the correlation and p values #Gives structure of the data str(iris)  tibble [150 x 5] (S3:

This blog explains the concept of local control.   ( reading time 6 min. ) There are three principles of designs of experiments: 1) Replication 2) Randomization 3) Local Control Replication and randomization alone can't remove the external influence of exogenous factors. So local control comes to the rescue. Local control is the grouping the experimental units into blocks such that within block there is homogeneity and between the block there is heterogeneity. The word block and replication are used interchangeably. By use of local control, between the block (replications) variations gets accounted into ANOVA and the experimental error gets reduced. Thus, local control is also termed as error control . Ideally, experimental error in an experiment is the measure of "within block (replication)" variation. In the field of agriculture research soil is the experimental unit. Blocking is done by technique of uniformity trials and soil fertility maps. L

This blog deals with the basic concept of randomization and its functions along with a suitable example. ( reading time 7 min. ) There are three principles of designs of experiments: 1) Replication 2) Randomization 3) Local Control In a previous blog , we have seen how replication provides an estimate of an experimental error. For a sound experiment, we need to estimate experimental error and that estimate must be an unbiased estimate. Randomization helps us to get an unbiased estimate of experiment error. Definition When all the treatments have equal chances (probability) of being allocated to all the experimental units it is called randomization. Experimental units can be seeds, plants, animals etc. depending on your research. The procedure of randomization varies with the experimental design used.   Randomization also allows us to use probability theory and different statistical techniques. The complex statistical techniques can be used only if randomization is us

This blog deals with the basic concept of replication and its functions along with suitable examples. ( reading time 10 min. ) There are three principles of designs of experiments: 1) Replication 2) Randomization 3) Local Control In this blog, we will confine to replication. The repeated application of treatments under investigation is known as replication. Sometimes we use repetition instead of replication. In designs like CRD where the experimental material is homogeneous, we replace the word replication with repetition. Why do we need replication? Let’s understand it by one example. Suppose you are a selector in Olympic selection committee of India looking for a suitable candidate for 10 m Air Rifle event. You have two candidates Abhinav Bindra and CarryMinati . You asked both of them to fire one shot and Abhinav scored 9 while Carry scored 9.6. Will you send Carry for Olympics? Is this scenario sufficient to reach the conclusion?   I kno