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Course Content
Few Important questions
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Few important questions
Introduction to statistics
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Statistics
Some terminologies
Variable
Statistical tools for analyzing and presenting the qualitative data
Sampling Methods
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Sampling
Sampling Methods/Techniques
Types of Probability sampling
Difference between Strata and Clusters
Types of Non-Probability sampling
Few other types of sampling techniques
Types of event sampling
Sampling distribution of mean
Levels of Data Measurement
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Introduction
Some key points to remember
Standard deviation
Quartile
Skewness
Kurtosis
Inferential statistics
Test for hypothesis
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t-test
Types of t-test
Paired t-test
Unpaired t-test
F-test
Chi-square test
Parametric and Non-parametric test
Phi-coefficient
Analysis of variance (ANOVA)
One-way ANOVA
Statistical tests
Correlation and Regression
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Correlation
Correlation coefficient
Method of finding correlation coefficient
Regression
Scatter plot
Correlation
Regression analysis
Types of regression analysis
Covariance analysis
Difference between correlation and Regression
Some consideration in interpreting correlation
Design of experiments
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Completely randomized design ( CRD):
Randomized Block design ( RBD or RCBD):
Latin square design or Least square difference (LSD)
Factorial experiment
Difference between CRD and RCBD
Blocking
Measuring gains in RCBD
Important terminologies related to experimental designs
Principle of experimental design
Significance of replication and randomization in experiments
Types of environments
Normal distribution ( Gaussian distribution)
Characteristics of a Normal distribution
Difference between Normal distribution and standard normal distribution
P-value ( Probability value)
Degree of freedom and how does it affect the precision
Master Agricultural Statistics – Notes, Case Studies & Practical Insights – with Rahul

Types of regression analysis

a. Simple linear regression analysis:

  • Functional relationship between two variables expressed in straight line.
  • The equation of simple linear regression is given by

Y = α + βx

 

b. Multiple regression:

  • Functional relationship between more than two variables.

 Y = α + β1x1 + β2 x2 + ….+ βp xp + e

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