Main Page > BS Journalism Studies (Morning and Self-Supporting) > Statistics for Social Sciences
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Statistics for Social Sciences
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This course aims to introduce the students to the subject of statistics as a science of data. Data abounds in this information age; extracting helpful knowledge and gaining a sound understanding of complex data sets has been more of a challenge. Course Objectives 1. In this course, students focus on the fundamentals of statistics, broadly described as the techniques to collect, clarify, summarize, organize, analyze, and interpret numerical information. 2. This course will begin with a brief overview of statistics and will then quickly focus on descriptive statistics, introducing graphical methods of describing data. 3. It aims to teach students about combinatorial probability and random distributions. 4. It will focus on both estimation and hypothesis testing issues. 5. It will also examine the techniques to study the relationship between two or more variables. Learning Outcomes By the end of the course, students will be able to 1. Perform exploratory data analysis 2. Understand key principles of sampling 3. Select appropriate tests of significance for multiple contexts 4. Gain the foundational skills that prepare them to pursue more advanced topics in statistical thinking 5. Gain a sound understanding of what statistics represent, how to use statistics to organize and display data, and how to draw valid inferences based on data by using appropriate statistical tools. 6. Learn use of Excel and SPSS Course Outline ● Introduction to Statistics ● Statistical Measures: Measures of central tendency; measures of variability; measures of shape. ● Probability Concepts: Sample space; simple and compound events; probability laws; random variables. ● Statistical Distributions: Discrete distribution; Continuous distribution; Binomial, Normal and other distributions and their characteristics. ● Sampling Theory: Sampling distributions; central limit theorem. ● Estimation: Point and interval estimates; confidence intervals; significance level. ● Tests of Hypothesis: Null and alternative hypotheses; sample size; type I and type II errors. Inference about a population; Inference about comparing two populations. ● Regression & Correlation: Introduction to Regression & Correlation Analysis, Clustering, Classification, Natural language processing ● Use of Excel and SPSS |
| Credit hours/ Marks:- 3 |
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