Course Content
Few Important questions
0/1
Few important questions
Introduction to statistics
0/4
Statistics
Some terminologies
Variable
Statistical tools for analyzing and presenting the qualitative data
Sampling Methods
0/8
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
0/7
Introduction
Some key points to remember
Standard deviation
Quartile
Skewness
Kurtosis
Inferential statistics
Test for hypothesis
0/11
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
0/11
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
0/16
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
Learn Agricultural Statistics with Rahul

Analysis of variance (ANOVA)

  • It is a statistical technique developed to study significance of difference between two or more than two sample means [ or equality of several means].

 

Assumptions of ANOVA

  • The distribution of residual is normal.
  • The data/samples are independent of each other and are taken at random.
  • Each one of the populations has same variance.
  • Variance are added or summed.

 

 

Characteristics of ANOVA

  • The dependent variable must be continuous and independent variable must be categorical.
  • Used in regression studies to determine the influence of independent variables on dependent variables.
  • Variance is assumed to be constant.
  • Adding and multiplying a constant to all observations doesn’t alter significance.

Note:

i) One-way ANOVA: When an independent factor ( Single factor) influences different sample groups.

ii) Two-way ANOVA: When two independent factors influence same sample groups.

 

Uses of ANOVA

  • To support other statistical tools.
  • To compare models with the objective of selecting samples that adequately describe the data.
  • To test hypothesis about batches of coefficient.
  • As a tool for summarizing complex high-dimensional inferences.
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