COURSE INFORMATION Course Title: STATISTICS II

Code	Course Type	Regular Semester	Theory	Practice	Lab	Credits	ECTS
BUS 202	B	4	4	0	0	4	5

Academic staff member responsible for the design of the course syllabus (name, surname, academic title/scientific degree, email address and signature)	NA
Main Course Lecturer (name, surname, academic title/scientific degree, email address and signature) and Office Hours:	M.Sc. Egla Mansi emansi@epoka.edu.al , Thursday 12:00-15:00
Second Course Lecturer(s) (name, surname, academic title/scientific degree, email address and signature) and Office Hours:	NA
Teaching Assistant(s) and Office Hours:	NA
Language:	English
Compulsory/Elective:	Compulsory
Study program: (the study for which this course is offered)	Bachelor in Business Informatics (3 years)
Classroom and Meeting Time:
Code of Ethics:	Code of Ethics of EPOKA University Regulation of EPOKA University "On Student Discipline"
Attendance Requirement:	75%
Course Description:	Statistics II: The aim of the courses is that inference making in Business. The objective of the course is to help students to understand theoretical characteristics of statistical methods and develop practical knowledge and skills to analyze the business data.
Course Objectives:	The major objective of this course is to increase the student data analysis skills using different statistical tools, both from descriptive and inferential statistics.

BASIC CONCEPTS OF THE COURSE

1	Nonparametric
2	Confidence Interval
3	Sampling Distribution
4	Hypothesis Testing
5	Linear Models
6	Anova

COURSE OUTLINE

Week	Topics
1	Introduction to syllabus
2	Chapter 6 Distributions of Sample Statistics page 244: Statistical analysis requires that we obtain a proper sample from a population of items of interest that have measured characteristics. If we do not have a proper sample, then our statistical methods do not work correctly.
3	Chapter 6 Distributions of Sample Statistics page 244: Statistical analysis requires that we obtain a proper sample from a population of items of interest that have measured characteristics. If we do not have a proper sample, then our statistical methods do not work correctly.
4	Chapter 7 Confidence Interval Estimation: One Population 284: In this chapter we address these and other types of situations that require an estimate of some population parameter. Inferential statements concerning estimates of a single population parameter, based on information contained in a random sample are presented.
5	Chapter 7 Confidence Interval Estimation: One Population 284: In this chapter we address these and other types of situations that require an estimate of some population parameter. Inferential statements concerning estimates of a single population parameter, based on information contained in a random sample are presented.
6	Chapter 8 Confidence Interval Estimation: Further Topics 328: In this chapter we address these and other types of situations that require an estimate of some population parameter. Inferential statements concerning estimates of a single population parameter, based on information contained in a random sample are presented.
7	Additional Handouts on Simulation/Bootstrapping + Review
8	Midterm
9	Chapter 9-10 Hypothesis Tests of a Single Population 346: In this chapter we develop hypothesis-testing procedures that enable us to test the validity of some conjecture or claim by using sample data. This form of inference contrasts and complements the estimation procedures developed in Chapters 7 and 8.
10	Chapter 11-13 Regression Models page 417-600: In this chapter we extend our analysis to relationships between variables. Our analysis builds on the descriptive relationships using scatter plots and covariance/correlation coefficients
11	Chapter 14 Introduction to Nonparametric Statistics 602: We introduce nonparametric tests, which are often the appropriate procedure needed to make statistical conclusions about qualitative data (nominal or ordinal data) or numerical data when the normality assumption cannot be made about the probability distribution of the population.
12	Chapter 15 Analysis of Variance 645: In modern business applications of statistical analysis, there are a number of situations that require comparisons of processes at more than two levels. An important tool for organizing and analyzing the data from this experiment is called analysis of variance, the subject of this chapter.
13	Project Presentations
14	Presentation of projects and review for final exam

Prerequisite(s):	Statistics I
Textbook(s):	• Hogg, Tanis and Zimmerman "Probability and Statistical Inference". 10th edition. • Paul Newbold, William L. Carlson and Betty Thorne “Statistics for Business and Economics” latest edition • Anderson, Sweeney, Williams "STATISTICS FOR BUSINESS AND ECONOMICS" • For statistics lovers Casella and Berger "Statistical Inference"
Additional Literature:	Additional handouts
Laboratory Work:	Yes
Computer Usage:	Yes
Others:	No

COURSE LEARNING OUTCOMES

1	To understand and apply the concept of hypothesis testing which includes developing and interpreting the results of the hypothesis testing.
2	To understand and be able to make comparisons between means, experimental design and analysis of variance
3	To understand and be able to make comparisons involving proportions and test of independence
4	To understand and be able to do simple linear regression and all of its analysis techniques
5	All of the above mentioned topics and objective to be applied through practical case studies by using the corresponding statistical software.

COURSE CONTRIBUTION TO... PROGRAM COMPETENCIES (Blank : no contribution, 1: least contribution ... 5: highest contribution)

No	Program Competencies	Cont.
Bachelor in Business Informatics (3 years) Program
1	Identify activities, tasks, and skills in management, marketing, accounting, finance, and economics.	3
2	Apply key theories to practical problems within the global business context.	3
3	Demonstrate ethical, social, and legal responsibilities in organizations.	4
4	Develop an open minded-attitude through continuous learning and team-work.	2
5	Integrate different skills and approaches to be used in decision making and data management.	3
6	Combine computer skills with managerial skills, in the analysis of large amounts of data.	5
7	Provide solutions to complex information technology problems.	5
8	Recognize, analyze, and suggest various types of information-communication systems/services that are encountered in everyday life and in the business world.	5

COURSE EVALUATION METHOD

Method	Quantity	Percentage
Midterm Exam(s)	1	30
Project	1	20
Quiz	2	5
Final Exam	1	40
	Total Percent:	100%

ECTS (ALLOCATED BASED ON STUDENT WORKLOAD)

Activities	Quantity	Duration(Hours)	Total Workload(Hours)
Course Duration (Including the exam week: 16x Total course hours)	16	4	64
Hours for off-the-classroom study (Pre-study, practice)	16	2	32
Mid-terms	1	17	17
Assignments			0
Final examination	1	12	12
Other			0
Total Work Load:			125
Total Work Load/25(h):			5
ECTS Credit of the Course:			5

CONCLUDING REMARKS BY THE COURSE LECTURER

If a student has a misbehavior report then automatically that student gets zero points for that exam. The same rule goes if the projects they submit have high plagiarism