DATA ANALYSIS LAB

Paper Code: 
GBCA 202B
Credits: 
3
Periods/week: 
3
Max. Marks: 
100.00
Objective: 

The course will enable the students to

  1. Understand the role of statistics in data analysis.
  2. Apply statistical techniques to research data for analyzing and interpreting data carefully.

 

Course Outcomes (COs):

Course Outcome (at course level)

Learning and teaching strategies

Assessment Strategies

The students will:

CO83. Use statistical tool for analyzing and interpreting data.

CO84. Effectively use statistical software to perform statistical computations and display numerical and graphical summaries of data sets.

CO85. Compute and interpret the coefficient of correlation for bivariate data.

CO86. Model and analyze measurement data using the appropriate distribution, e.g. normal, binomial, chi- square.

CO87. Perform               sensitivity analysis on data.

Approach in teaching: Interactive                  Lectures, Discussion, Tutorials, Reading assignments, Demonstration,

 

Learning    activities    for    the students:

Self-learning         assignments, Effective                   questions,

Simulation,                   Seminar presentation, Giving tasks.

Class test, Semester end examinations, Quiz, Solving problems in tutorials, Assignments, Presentation, Individual and group projects

Note: Students should be given hands-on experiences to use appropriate software packages for selected statistical analysis.

 

The following test should be performed using appropriate software packages

  1. Formation of frequency distribution table (inclusive and exclusive)
  2. Graphical representation- histogram, frequency polygon, ogives
  3. Measures of Central Tendency- Mean, Median and Mode
  4. absolute and relative Measures of Dispersion- range, Quartile Deviation, Mean Deviation, Standard Deviation
  5. Coefficient of correlation- karlpearson and spearmens rank
  6. Fitting of Regression lines and prediction.
  7. Normal Distribution-area under the curve
  8. Chi-square tests- Goodness of fit, Independence of Attributes 2x2 and RXC contingency tables, testing of single variance
  9. Application of Student’s t-test for small samples- test of significance of single mean, difference in means, independent and paired T test.
  10. F-test for two sample variances.
  11. Analysis of Variance- one-way classification, two-way classification
ESSENTIAL READINGS: 

E-RESOURCES:

  1. https://www.w3schools.com/EXCEL/index.php
  2. https://support.microsoft.com/en-us/office/excel-video-training-9bc05390-e94c-46af-a5b3-d7c22f6990bb
  3. https://www.tutorialspoint.com/advanced_excel/index.html
  4. https://www.coursera.org/learn/excel-advanced
Academic Year: 
2023-24
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