APPLIED STATISTICS (PRACTICAL)

Paper Code: 
HFN 226
Credits: 
4
Contact Hours: 
60.00
Max. Marks: 
100.00
Objective: 

Course Objectives:

This course will enable the students to –

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

Course  Outcomes (COs):

Course

Learning outcomes

(at course level)

Learning and teaching strategies

Assessment

Strategies

Paper Code

Paper Title

 

 

 

 

 

 

 

 

HFN  226

Applied Statistics

 (Practical)

The students will be able to –

COFN49: The Students are able to Enter, Organize, and Save data in a SPSS

COFN 50 : Learn to Create variables and Enter data for Analysis in SPSS.

COFN 51 : Students will be able to describe data using Descriptive Statistics & Graphs in SPSS.

COFN 52 : Able to Examine Relationships between variables by using Correlation technique.

COFN 53 : Able to carry out a Statistical Analysis that can test hypotheses.

COFN 54: From Testing of Hypothesis Portion Students will learn to Compare groups to determine if there are significant differences between those groups or not.

 

 

Approach in teaching:

Interactive Lectures, Discussion, Power Point Presentations, Informative videos, group discussion

 

Learning activities for the students:

Self learning assignments, Effective questions, presentations, Field trips

Quiz, Poster Presentations,

Power Point Presentations, Individual and group projects,

Open Book Test, Semester End Examination, discussion, demonstration

 

CONTENTS

The following test should be perform on computer

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

 

Essential Readings: 
  • Gupta, S.P.: Statistical Methods, Sultan Chand and Company, New Delhi.
  • Elhane,D.N.: Fundamentals of Statistics, Kitab Mahal,Allahbad.

 

 

Academic Year: 
2021-2022
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