Data Analysis in Management with SPSS Software

Dr. J.P.Verma is a professor of statistics at the Lakshmibai National University of Physical Education, Gwalior and has experience of more than three decades of teaching applied statistics to the students of different disciplines. Professor Verma possesses three masters degree in statistics, psychology and computer application besides a PhD degree. He has the unique distinction of holding Ist position in MSc (Statistics) from Kanpur university and Ist position in MA (Psychology) from Jiwaji University Gwalior. He has been a visiting faculty in many of the well known institutes of management such as Indian Institute of Information Technology and Management, Indian Institute of Travel and Tourism Management, Prestige Institute of Management etc. He has guided six PhD candidates successfully and has four books in the area of statistics & research, and 28 research papers/articles to his credit.   Prof. Verma has acted as an advisor for the Union Public Service Commission (UPSC), New Delhi for various assignments since 2005. He served as an officer on special duty to the Vice Chancellor for two and half years and looked after the Administration, Planning and Implementation. He was a visiting fellow in the University of Sydney in 2002. He has made academic visits in Australia, Poland, Scotland, Bangkok and Nepal and has conducted many workshops in the area of applied statistics and research methodology.

Chapter 1: Data Management.- Chapter 2: Descriptive Analysis.- Chapter 3: Chi-Square test and its Application.- Chapter 4: Correlation Matrix and Partial Correlation - Explaining Relationships.- Chapter 5: Regression Analysis and Multiple Correlations -  For Estimating a Measurable Phenomenon.- Chapter 6: Hypothesis testing for decision making.- Chapter 7: One Way ANOVA - For testing the variability among group Means.- Chapter 8: Two Way Analysis of Variance – For Understanding the Causes of Variations.- Chapter 9:  Analysis of Covariance- To study the role of covariate in Experimental Research.- Chapter 10: Cluster Analysis- For segmenting the population.- Chapter 11: Application of Factor Analysis – To study the Factor Structure among Variables.- Chapter 12: Application of Discriminant Analysis – For developing a classification model.- Chapter 13: Logistic Regression – Developing a model for Risk Analysis.- Chapter 14: Multidimensional Scaling for product positioning.