Course Objectives:
Course Outcomes (COs):
Course |
Course outcome (at course level) |
Learning and teaching strategies |
Assessment Strategies |
|
Paper Code |
Paper Title |
CO 81.Awareness of current trends, issues and researches. CO 82.Apply Descriptive statistics and machine learning using statistical tools SPSS/ Orange. CO 83.Prepare a report based on primary or secondary data. |
Approach in teaching: Lab class, regular interaction with Supervisor
Learning activities for the students: SPSS exercises, Orange exercises ,Presentations |
Viva and Presentation |
MAM 228
|
Seminar Presentation and Viva voce
|
Course Contents: Each student will choose a topic or capstone project 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 covering the following:
Apart from the 30 hrs. lab sessions, students are required to devote 4 hrs. per week under the supervision of their respective supervisors on regular basis for guidance on report.
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 – Descriptives, Summary of Model, Model Parameters, Excluded variables, Assessing Multicollinearity,
Logistic Regression Analysis
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 hrs
• 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)