Course Content
Few Important questions
0/1
Most Important questions
Introduction to statistics
0/4
Introduction
Some terminologies
Variable
Statistical tools for analyzing and presenting the qualitative data
Sampling Methods
0/12
Sampling
Sampling Methods/Techniques
Types of Probability sampling
Simple random sampling
Systematic sampling
Stratified sampling
Cluster sampling
Types of cluster sampling
Types of Non-Probability sampling
Few other types of sampling techniques
Types of event sampling
Sampling distribution of mean
Levels of Data Measurement
0/10
Introduction
Some key points to remember
Central limit theorem
Probability
Standard deviation
Quartile
Skewness
Kurtosis
Inferential statistics
Types of inferential statistics
Test for hypothesis
0/13
t-test
Types of t-test
Hypothesis and assumptions of paired t-test
When to use paired t-test?
Unpaired t-test
F-test
Chi-square test
Z-test
Parametric and Non-parametric test
Phi-coefficient
Analysis of variance (ANOVA)
One-way ANOVA
Statistical tests
Correlation and Regression
0/15
Correlation
Correlation coefficient
Method of finding correlation coefficient
Regression
Regression coefficient
Scatter plot
Regression analysis
Types of regression analysis
Covariance analysis
Principle uses of covariance
Difference between correlation and covariance
Difference between correlation and Regression
Some consideration in interpreting correlation
Types of outliers
Reasons for Outliers and ways of reducing it
Design of experiments
0/19
Completely randomized design ( CRD)
Randomized Block design ( RBD or RCBD)
Latin square design or Least square difference (LSD)
other design
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
Why experimental designs are used?
Normal distribution ( Gaussian distribution)
Characteristics of a Normal distribution
Difference between Normal distribution and standard normal distribution
P-value ( Probability value)
Least squares method (LSM)
Degree of freedom and how does it affect the precision
Learn Agriculture Statistics with Rahul
About Lesson

P-value ( Probability value)

  • Null hypothesis ( H0) : There is no relationship between the two variables being studied.
  • States results are due to chance.
  • Null hypothesis assumes that whatever you are trying to prove didn’t happen.
  • P-value is a number describing how likely it is true that your data would have occurred by random chance.
  • P-value lies between 0 and 1.
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