# Statistics & SPSS: Course Outline

Statistics is the study of the collection, analysis, interpretation, presentation, and organization of data. In applying statistics to, e.g., a scientific, industrial, or societal problem, it is conventional to begin with a statistical population.

## Course Outline

Introduction of Statistics

• Background and definition of Statistics
• Descriptive and inferential statistics
• Variable and its type
• Measurement scale and types
• Statistical data
• Source of data
• Collection of data
• Management of data by tabulation
• Classification
• Graph, Charts & Histogram
• Pie chart, Scatter diagram, Box plot and Line chart

Sampling and Sampling Techniques

• Concept of population and sample
• Reasons to use sampling
• Random number and their application
• Sampling techniques
• Random (simple random sampling, Systematic, stratified random sampling, Cluster sampling) and Non-random sampling (judgment sampling/purposive sampling, Convenience, Quota and snowball sampling)
• Determination of sample size
• Error, Sources of error and bias

Measures of Location and Dispersion

• Measure of central tendency and its types
• Arithmetic mean
• Median, Mode and Geometric mean
• Absolute and relative dispersion
• Range & mean deviation
• Quartile deviation
• Standard deviation
• Variance & significance of standard deviation
• Coefficient of variation
• Symmetry, Skewness and its measure

Probability and Probability Distribution

• Introduction of probability and its uses in psychology
• Venn diagram
• Concept of random experiment, definition of probability and its measurement
• Laws of probability
• Binomial distribution
• Poisson distribution
• Normal distribution and its application

## Course Outline

Statistical Inference

• Introduction of inference & its types
• Estimation and testing of Hypothesis
• Sampling distribution of mean
• Standard error of mean
• Point estimation
• Confidence interval of population mean
• Null and alternative hypothesis, Type I & II error
• Test of significance based on Z, t, F, χ2 distributions
• Concept of p-value
• Test of mean for small and large sample
• Test for independent and paired observations
• Inference regarding correlation and regression coefficient
• ANOVA (Analysis of variance)

Regression and Correlation Analysis

• Introduction to relationship
• Scatter diagram
• Regression analysis
• Simple linear regression line
• Least square method for fitting regression line
• Simple concept of multiple regression correlation analysis
• Simple linear correlation and correlation coefficient
• Coefficient of determination
• Interpretation of correlation coefficient & association
• Measure of association
• Contingency table
• Test of independence

Non Parametric Tests

• Introduction to distribution free procedures
• Different types of non parametric tests like sign test
• Wilcoxon signed rank test
• Mann-Whitney U test
• Kruskal-Wills (H-test) test

Data Analysis & SPSS

• Preparation of data for analysis by using computer software SPSS