Business Statistics with SPSS-II (Inferential)
Course Overview
Faculty : Dr. Sanjoli Jain (Ph.D. MBA)
Business Statistics deals with the application of statistical tools in the area of marketing, production, finance, research and development, and manpower planning to extract relevant information for the purpose of decision making in the real world. There are major domains of Statistics—Descriptive statistics and Inferential statistics. This course will cover Inferential Statistics while using SPSS as a tool for Business analysis. Statistical inference is the process of using data analysis to infer properties of an underlying distribution of probability. Inferential statistical analysis infers properties of a population, for example by testing hypotheses and deriving estimates.
Course Learning Outcomes
At the end of the course, the students should be able to:
- Apply the concepts and theories of statistics for business decision making
- Use data-based decisions to solve managerial problems
- Apply SPSS as a tool for Visualization and Interpretation of Data.
- Apply SPSS on various statistical tools.
Learning Resources
There is no fixed textbook prescribed for this course. The concepts and theories used in the course will have to be learned from various sources, including youtube videos, online learning platforms, and internet resources. Data will be obtained from various authentic sources and the same will be used for analysis and estimation using SPSS and Excel .
However, students may like to consult the following textbooks to enhance their knowledge of economic principles and their applications:
- STATISTICS FOR MANAGEMENT: by Levin Rubin Rastogi Siddiqui
- BUSINESS STATISTICS: by JK Sharma
- DISCOVERING STATISTICS USING IBM SPSS: by Andy Field
Student Responsibility
It is the responsibility of every student to be aware of the requirements for this course and to understand the specific details included in this document. It is emphasized that this course requires a significant commitment outside of formal class contact. The learning tasks in this course may include classes (lectures or seminars), required reading, the preparation of answers to set questions, exercises, and problems, Downloading SPSS, and self-study. It is advisable that the student maintains a separate NoteBook/Folder for this course which can be used for keeping class notes, library notes, and notes of other readings. It is important to develop the habit of writing notes of classroom discussions and any readings that the students come across.
Late Submission
Assessment tasks submitted after the due date, without prior approval/arrangement, will not be accepted. Requests for an extension of time must be made based on special circumstances and it will be the sole discretion of the instructor whether to provide the extension.
Plagiarism
Plagiarism is looked at as the presentation of the expressed thought or work of another person as though it is one's own without properly acknowledging that person. In case of plagiarism, marks will be deducted or put to zero. It is also advisable that students must not allow other students to copy their work and must take care to safeguard against this happening. In cases of copying, normally all students involved will be penalized equally; an exception will be if the student can demonstrate the work is their own and they took reasonable care to safeguard against copying.
Session plan - Dr. Sanjoli Jain
Each session will be of 90 minutes. There will be a mix of theory and hands-on data-based discussion. There will be practice sessions and exercises.
Class No. | Topics |
---|---|
1 | Introduction to Inferential Statistics |
2 | Sampling and Sampling distribution |
3 | Same as above |
4 | Same as above |
5 | Probability and Probability Distribution |
6 | Same as above |
7 | Same as above |
8 | Hypothesis testing |
9 | Same as above |
10 | Same as above |
11 | Z test ,T test |
12 | Same as above |
13 | F test ,ANOVA,MANOVA |
14 | Same as above |
15 | Same as above |
16 | Chi-Square test |
17 | Same as above |
18 | Same as above |
19 | Non Parametric test |
20 | Same as above |
21 | Same as above |
22 | Index number |
23 | Same as above |
24 | Same as above |
25 | Time series analysis |
26 | Same as above |
27 | Same as above |
28 | Statistical modeling |
29 | Same as above |
30 | Same as above |