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| Statistics ICAR E course Free PDF Book Download e krishi shiksha |
Statistics ICAR E course PDF Book was developed by ICAR e Krishi Shiksha and TNAU. You Can easily Download it from our website IARI Toppers by clicking on the below given link.We have provided the latest version of Statistics ICAR Ecourse PDF Book but some mistakes can be found. If you found any mistake, error in this PDF book than feel free to comment below.
Course Content of Statistics ICAR E course Free PDF Book Download e krishi shiksha:
- Data – definition – Collection of data – Primary and secondary data – Classification of data – Qualitative and quantitative data
- Diagrammatic representation of data – uses and limitations – simple, Multiple, Component and percentage bar diagrams – pie chart
- Graphical representation – Histogram – Frequency polygon and Frequency curve
- Measures of averages – Mean – median – mode – geometric mean – harmonic mean – computation of the above statistics for raw and grouped data – merits and demerits – measures of location – percentiles – quartiles – computation of the above statistics for raw and grouped data
- Measures of dispersion – Range, Variance -Standard deviation – co-efficient of variation – computation of the above statistics for raw and grouped data
- Probability – Basic concepts-trial- event-equally likely- mutually exclusive – independent event, additive and multiplicative laws. Theoretical distributions discrete
and continuous distributions, Binomial distributions-properties - Poisson Distributions – properties, Normal Distributions- properties
- sampling-basic concepts- sampling vs complete enumeration parameter and statistic-sampling methods-simple random sampling and stratified random sampling
- Test of significance – Basic concepts – null hypothesis – alternative hypothesis – level of significance – Standard error and its importance – steps in testing
- T-test – definition – assumptions – test for equality of two means-independent and paired t test
- Attributes- Contingency table – 2×2 contingency table – Test for independence of attributes – test for goodness of fit of mendalian ratio
- Correlation – definition – Scatter diagram -Pearson’s correlation co-efficient – properties of correlation coefficient
- Regression – definition – fitting of simple linear regression equation – testing the significance of the regression coefficient
- Design of experiments – basic concepts – treatment – experimental unit – experimental error – basic principle – replication, randomization and local control.
- Completely randomized design – description – layout – analysis – advantages and disadvantages
- Randomized blocks design – description – layout – analysis – advantages and disadvantages
- Latin square design – description – layout – analysis – advantages and disadvantages
- Factorial experiments – factor and levels – types – symmetrical and asymmetrical – simple, main and interaction effects – advantages and disadvantages
- 22 Factorial Experiments in RBD – lay out – analysis
- 23 factorial experiments in RBD – lay out – analysis
- Split plot design – layout – ANOVA Table
- Strip plot design – layout – ANOVA Table
- Long term experiments – ANOVA table – guard rows – optimum plot size – determination methods.
P1. Diagrammatic and graphic representation – simple, multiple, component and percentage bar diagram – pie chart – histogram. Frequency polygon, frequency curve
P2. Measures of central tendency – mean median, mode, geometric mean, harmonic mean for raw data
P3. Measures of central tendency – mean, median, mode, geometric mean and harmonic mean for grouped data
P4. Measures of dispersion – variance, standard deviation and coefficient of variation for raw data
P5. Measures of dispersion – variance, standard deviation and coefficient of variation for grouped data
P6. Selection of simple random sampling using lottery method and random numbers
P7. Students’s t test – paired and independent t test
P8. Chi square test – test for association and goodness of fit
P9. Calculation of Karl Pearson’s correlation coefficient
P10. Fitting of simple linear regression of y on x
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