BOT-312-MJ-T : Computational Botany
1. Introduction to Computational Botany............................9
1.1 Introduction
1.2 Definition and concept
1.3 Role in biological and botanical research
1.4 Applications in computational botany
1.5 Population v/s Sample
1.6 Parameter v/s Statistic
1.7 Variables and attributes
1.8 Qualitative v/s Quantitative data
1.9 Discrete and continuous variables
1.10 Exercise
2. Data Collection and Classification, Types of Biological Data................19
2.1 Introduction
2.2 Primary v/s Secondary data
2.3 Classification of biological data
2.4 Frequency distribution
2.5 Class intervals
2.6 Types of Biological Data - Measurement scales, Nominal, Ordinal, Interval, Ratio
2.7 Exercise
3. Sampling Methods.....................................................28
3.1 Introduction
3.2 Definitions - Population, sample, parameter, statistic
3.3 Need of sampling, Advantages
3.4 Simple random sampling - with and without replacement
3.5 Stratified and systematic sampling - applications in plant science
3.6 Non-random sampling
3.7 Sampling errors or outliers in biological studies
3.8 Exercise
4. Tabular and Graphical Representation........................38
4.1 Introduction
4.2 Tables, parts of table and frequency tables
4.3 Histogram and Frequency polygon
4.4 Line graph, Bar chart and Stacked Bar chart
4.5 Pie diagram
4.6 Scatter plot and Radar Plot
4.7 Exercise
5. Measures of Central Tendency and Dispersion............56
5.1 Introduction
5.2 Definition and concept of Mean (Arithmetic Mean), Median, Mode
5.3 Interpretation of Mean, Median, Mode
5.4 Mean, Median, Mode relationship
5.5 Range
5.6 Mean Deviation
5.7 Variance
5.8 Standard Deviation (SD)
5.9 Coefficient of Variation (CV)
5.10 Importance of variability in plant modelling
5.11 Exercise
6. Probability and Probability Distributions.......................73
6.1 Introduction
6.2 Random experiments, events – simple, compound, mutually exclusive, independent (only concepts)
6.3 Definitions of probability, types, classical and empirical
6.4 Basic probability distributions – Binomial, Poisson, Normal (concepts and applications)
6.5 Standard deviation and probability area
6.6 Exercise
7. Hypothesis Testing and Experimental Design.................82
7.1 Introduction
7.2 Null and Alternate hypothesis; Types of errors, Type I and Type II with one suitable example each; Degrees of freedom (concept), level of significance, critical region, p-value
7.3 Experimental Design, Completely Randomized Design (CRD), Randomized Block Design (RBD), Factorial design,
7.4 Key types of experimental Errors, Random Error, Systematic Error, Blunders
7.5 Exercise
8. Statistical Tests for Biological Data...............................94
8.1 Introduction
8.2 Tests of Significance
8.3 Z test: only concept
8.4 Student’s t-test: O ne sample, independent and paired, one example each
8.5 Chi-Square test, Goodness of fit-test of independence, one example each
8.6 Introduction to ANO VA, Types: O ne, way ANO VA, Two, way ANO VA with one example each
8.7 F test, only concept
8.8 Post, hoc comparison (basic idea) e.g. Tukey’s HSD, only concept
8.9 Exercise
9. Correlation and Regression........................................122
9.1 Introduction
9.2 Definition of correlation, types, scatter diagram.
9.3 Karl Pearson’s coefficient of correlation and Spearman’s rank correlation coefficient. Their test of significance.
9.4 Linear regression (concept), equations, definition and properties of regression
9.5 Comparison of regression and correlation coefficients.
9.6 Exercise
10. Computational Biostatistics Using Jamovi................134
10.1 Introduction to JAMOVI
10.2 Needs of software in modern biology
10.3 Installation and interface of JAMOVI
10.4 Exercise
BOT-315-MJP : Practical based on BOT-312-MJ–T
Practical No. 1...................................................................141
Introduction to Biological Data Analysis using JAMOVI.
Practical No. 2..................................................................144
Data Visualization for Biological Research
Practical No. 3...................................................................147
Testing Normality of Biological Data
Practical No. 4..................................................................149
Student t-Test using Biology data set - One sample and independent.
Practical No. 5..................................................................153
Student t-Test using Biology data set - Paired t-Test
Practical No. 6..................................................................156
Chi-Square Test in Biological Data.
Practical No. 7..................................................................158
One-way ANOVA for Biological Data set.
Practical No. 8..................................................................161
Two-way ANOVA for Biological Data set.
Practical No. 9..................................................................163
Post-Hoc Analysis (Tukey post-hoc test)
Practical No. 10................................................................165
Correlation analysis using biology data set.
Practical No. 11..................................................................167
Regression analysis using biology data set.
Practical No. 12................................................................169
Non-Parametric test in biological research Mann-Whitney U
Practical No. 13................................................................171
Non-Parametric tests in biological research Wilcoxon Signed Rank.
Practical No. 14.................................................................173
Non-Parametric tests in biological research Kruskal-Wallis
