This course provides the applied statistics background for survey and experimental work in Agriculture. Case studies and critical examples are used to work through commonly experienced research problems (from sampling designs to the ethical consideration) and to explain how they may be approached, solved or prevented with statistical means. The importance of statistical science in agriculture is obvious, where the collection, analysis and interpretation of numerical data are concerned. Statistical principles apply in all areas of experimental work and they have a very important role in agricultural experiments. Statistics plays an important role in experimentation. While many scientific problems could be solved by different statistical procedures. Furthermore, some statistical softwares knowledge will be provided to the students to improve their analytical skills. These activities are further supports the student’s research.

Contents

1. Importance of Statistics in agriculture research.
2. Selection of statistical tools based on scale of measurements.
3. Analysis of Count and Frequency data.
4. Measures of central tendency and dispersion.
5. Some concepts of hypothesis testing. T, Z, Chi-square and F tests. Contingency Tables.
6. Diversity Indices.
7. Concept of ANOVA and its types.
8. Correlation Analysis: Simple correlation, multiple correlation, and Partial correlation.
9. Regression Analysis: Simple and multiple regression.
10. Generalized linear models: logistic regression, Poisson regression, Gamma regression, Inverse Gaussian regression.
11.  Non-linear regression. 
12. Dose Response Curves.

Recommended Texts

1. Montgomery, D. C. (2017). Design and analysis of experiments (9th ed). New York: John Wiley & Sons.
2. Rao, G. N. (2007). Statistics for agricultural sciences (2nd ed). Devon: BS Publication.

Suggested Readings

1. Lawal, B. (2014). Applied statistical methods in agriculture, health and life sciences. Berlin: Springer.
2. Sahu, P. K. (2016). Applied statistics for agriculture, veterinary, fishery, dairy and allied fields. Berlin: Springer.
3. Gbur, E. E., Stroup, W. W., McCarter, K. S., Durham, S., Young, L. J., Christman, M., West, M., & Kramer, M. (2012). Analysis of generalized linear mixed models in the agricultural and natural resources sciences. Wisconsin-Madison: Soil Science Society of America.

Distribution of Marks:

Mid Exam:           30
Final exam:         50
Sessional (Assignment,Presentation,Participation,Attendance,Quizes)    20

Scheduled on:

MS Food and Technology:             Monday (01:00pm-04:00pm)