Introduction Definition of Statistics, Population & sample, Descriptive and Inferential Statistics, Observations, Discrete and continuous variables, collection of primary and secondary data and sources, Introduction of SPSS(opening/creating/editing SPSS data file. Summarizing Data in Tables and Graphs. Construction of a frequency distribution(discrete & continuous variables),Stem and Leaf Plot, Graphs and their construction, Bar Charts, Pie Charts, Histogram, Frequency Polygon and Frequency Curve, Historigram , Types of frequency Curves. Exercise on SPSS. Measures Of Central Tendency Introduction, Different types of Averages, Mean, Median and Mode, Quantiles, Empirical relation between Mean, Median and Mode, Merit and Demerits of various Averages, Box and Whisker Plot, Definition of outliers and their detection. Exercise on SPSS. Measures Of Dispersion Introduction, Absolute and relative measures, Range, Variance and Standard Deviation, Interpretation of Standard Deviation, Co-efficient of Variation, Standardized Variables, Skewness and Kurtosis. Exercise on SPSS. Probability And Probability Distribution Concept of discrete and continuous distribution, Binomial distribution, Normal distribution. Exercise on SPSS. Sampling And Sampling Distribution Introduction, sample design and sample frame, bias, sampling and non-sampling errors, sampling with and without replacement, probability and non-probability sampling, Simple Random sampling, Stratified Random Sampling, Systematic and cluster sampling, Judgment sampling, Quota sampling, Snowball sampling, Sampling Distribution for single mean and difference between means (only application). Hypothesis Testing Introduction, null and alternative hypothesis, Type-I and Type-II Errors, One-tailed and two-tailed tests, Testing hypothesis for single mean and difference between two means. Exercise on SPSS. Testing Of Hypothesis –Independence Of Attributes Introduction of attributes, Contingency Tables, Testing of Hypothesis about the independence of attributes. Exercise on SPSS. Regression And Correlation Introduction, cause and effect relationships and examples, simple linear regression, estimation of parameters and their interpretation, r and R2. Correlation, coefficient of linear correlation, its estimation and interpretation, Multiple regression and interpretation of its parameters. Exercise on SPSS.

• Using Statistics in SPSS to analyze data. • To get knowledge about the descriptive & inferential statistics and how to apply these tools in the field work of natural sciences.

• Students should have understanding of broad set of fields like surveys and data analysing techniques. • Applying the tools in their research work.

No Information Yet

No Information Yet

No Information Yet