This course offers a mechanism to understand data, descriptive statistics, statistical computer package; Probability: distributions, expectation, variance, covariance, portfolios, central limit theorem; statistical inference of univariate data; Statistical inference for bivariate data: inference for intrinsically linear simple regression models. This course will have a business focus.
- DESCRIPTION OF STATISTICS
- Descriptive Statistics: What is Statistics? Importance of Statistics. What is Biostatistics? Application of Statistics in Biological and Pharmaceutical Sciences. How samples are selected?
- Variables, Quantitative and Qualitative Variables, Univariate Data, Bivariate Data, Random Variables, Frequency Table, Diagrams, Pictograms, Simple Bar Charts, Multiple Bar Charts, Histograms.
- The Mean, the Median, the Mode, the Mean Deviation, the Variance and Standard Deviation, Coefficient of Variation.
- Fitting a Straight Line. Fitting of Parabolic or High Degree Curve.
- Definitions, Probability Rules, Probability Distributions (Binomial & Normal Distributions).
- Introduction. Simple Linear Regression Model. Correlation co-efficient.
- Statistical Hypothesis. Level of Significance. Test of Significance. Confidence Intervals, Test involving Binomial and Normal Distributions.
- Test of Significance based on “t”, “F” and Chi-Square distributions.
- One-way Classification, Two-way Classification, Partitioning of Sum of Squares and Degrees of Freedom, Multiple Compression Tests such as LSD, The analysis of Variance Models.
- An understanding of data analysis by using different statistical tests using various statistical software’s like SPSS, Minitab, Statistica etc.