This course will enable the students to –
Course Outcomes:
Course |
Course outcome (at course level) |
Learning and teaching strategies |
Assessment Strategies |
|
Paper Code |
Paper Title |
|||
MTM 228 |
Seminar Presentation and Viva Voce |
CO 96Analyze current trends, issues and researches. CO 97Apply Descriptive statistics and machine learning using statistical tools SPSS/ Orange. CO 98Investigate a research problem based on primary data. |
Approach in teaching: Lab class, regular interaction with Supervisor Learning activities for the students: SPSS exercises, Orange exercises ,Presentations |
Viva and Presentation |
Content: Each student will choose a topic in the beginning of the semester. They will be required to prepare a primary research report. 30 hours lab sessions are provided for hands on training on SPSS and systematic review of literature to the students as follows for covering the following:
Unit |
Contents |
No of Lectures |
I |
Regression- Simple Linear Model, Linear Model with several Predictors, Model estimation, Assessing Goodness of Fit, R and R square, Assessing individual Predictors Bias in Regression Model- Unusual cases, Generalizing the Model, Sample size in Regression, Assumptions, What if assumptions are violated Interpreting Regression Model –.ptive, Summary of Model, Model Parameters, Excluded variables, Assessing Multicollinearity, Logistic Regression. |
15 |
II |
Moderation and mediation of variables Exploratory Factor Analysis- Discovering Factors, Running the analysis, Interpreting output from SPSS, Reliability Analysis, How to report Factor analysis. |
15 |
Apart from the 30 hrs.lab sessions, students are required to devote 2 hrs. per week under the supervision of their respective supervisors on regular basis for guidance on report.
• IBM SPSS Statistics 20 Core System User’s Guide
• IBM SPSS Modeler 18.0 User's Guide
• G N Prabhakara, Synopsis Dissertation And Research To Pg Students, Jaypee Brothers Medical Publishers; second edition (2016)