Statistical Software Package — DU Skill Enhancement Courses Previous Year Question Papers

STATISTICAL SOFTWARE PACKAGE CREDIT DISTRIBUTION, ELIGIBILITY AND PRE-REQUISITES OF THE COURSE Course title Credits Credit distribution of the course Eligibility Pre-requisite of Lecture Tutorial Practical/ criteria the course 2 0 0 2 Class XII Basic course in Learning Objectives The Learning Objectives of this course are as follows:  To familiarize students with data analysis using a statistical software package like SPSS or any other equivalent.  To provide skills for research analysis and increaseemployability.  To lay a foundation for advance data analysis work and higher education. Learning outcomes The Learning Outcomes of this course are as follows:  After studying this course, students will be able to understand basic functions of statistical software package for managing variables and generate descriptive statistics to describe the data and analyze data through graphs and charts.  After studying this course, students will be able to test differences in sample means.  After studying this course, students will be able to identify relationships between variables and develop models for predicting dependentvariables on the basis of independent variables.  After studying this course, students will be able to understand data structures and identify clusters in data.  After studying this course, students will be able to identify principal components that are relevant from a host of variables. Unit 1: Getting started with the Software (16 hours) Introduction: Data Entry, Storing and Retrieving Files, Generating New Variables; Managing Data – Listing cases, replacing missing values, computing new variables, recoding variables, selecting cases, sorting cases, merging files, Graphs – Creating and editing graphs and charts; Descriptive Statistics Procedures: Frequencies, Descriptive, Explore, Cross Tabulation. Unit 2: Hypothesis Testing for Means (12 hours) T-tests: One sample test, Independent samples and paired samples t-test; ANOVA – One-way analysis of variance with post hoc analysis, Two-way analysis of variance. Unit 3: Testing for Association between Variables (16 hours Chi-square Test of Independence; Bivariate Correlation Analysis: Simple Scatter Plot; Correlation Coefficient: Pearson, Spearman Rho and Kendall Tau Coefficient. Factor analysis. Unit 4: Regression Analysis (16 hours) Linear Regression: Simple Linear Regression, Multiple regression analysis with matrix scatterplot. Multiple Regression: Standard (Enter) and Stepwise Method. Binary Logistic Essential/recommended readings SPSS for Windows Step by Step A Simple Guide and Reference, Darren George and Paul Malley Suggestive Readings Examination scheme and mode: Evaluation scheme and mode will be as per the guidelines notified by the University of Delhi.