This course will review topics in statistics studied in core for data analysis. Introduction to SPSS for statistical computing, analysis and graphical interpretation would be done using software skills.
Course |
Course Outcomes |
Learning and teaching strategies |
Assessment Strategies |
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Paper Code |
Paper Title |
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SSTT 301/501 |
Data Analysis using SPSS (Practical) |
CO 1. Apply advanced graphical representation techniques to effectively visualize and interpret complex datasets. CO 2. Apply measures of central tendency to analyze and interpret data to draw meaningful conclusions from data. CO 3. Evaluate measures of dispersion demonstrating advanced understanding of data variability and spread. CO 4. Demonstrate proficiency in fitting polynomials, exponential curves, and plotting probability distributions to model data distributions accurately. CO 5: Apply hypothesis testing methods to make inferences and draw conclusions from data samples. CO6: Contribute effectively in course-specific interaction |
Approach in teaching Interactive Lectures, Group Discussion, Classroom Assignment Problem Solving Sessions
Learning activities for the students: Assignments Seminar Presentation Subject based Activities |
Software based Assignments, Class Test, Individual Presentation, Record, Viva-voice |
Graphical representation of data by histograms, frequency polygon, Pie chart, ogives, boxplot and stem-leaf.
Measures of central tendency: Mean, Median, Mode.
Measures of dispersion: Range, Quartile Deviation, Mean Deviation, Standard Deviation.
Fitting of polynomials, exponential curves and plotting of probability distributions. Correlation and regression.
Testing of hypothesis: Chi-Square, t and F.
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