Statistics 2103 - Statistical Business Analytics: Syllabus and Study Guide

Statistics 2103 - Statistical Business Analytics

Course Overview

This course provides a comprehensive introduction to the fundamentals of data description, data analysis, and statistical methods with a focus on business applications. Students will develop proficiency in using Excel for data analysis and will learn to apply statistical reasoning to solve real-world business problems.

• Key Topics: Data description, graphical methods, probability distributions, estimation, hypothesis testing, regression, and forecasting.

• Applications: Emphasis on business decision-making, including marketing, operations, and finance.

Course Objectives & Learning Goals

• Demonstrate business knowledge: Apply statistical methods to make informed business decisions.

• Critical thinking: Use problem-solving skills to analyze and interpret data.

• Ethical and social responsibility: Recognize the ethical implications of data analysis in business.

• Quantitative analysis: Use quantitative methods to analyze business problems and recommend solutions.

• Communication: Present statistical findings and business recommendations effectively.

Major Topics and Subtopics

Descriptive Statistics

Descriptive statistics summarize and organize data to make it understandable. This includes measures of central tendency, variability, and graphical representations.

• Measures of Central Tendency: Mean, median, and mode.

• Measures of Dispersion: Range, variance, standard deviation.

• Graphical Methods: Bar charts, histograms, pie charts, scatterplots.

• Example: Using Excel to create a histogram of sales data to identify patterns.

Probability and Probability Distributions

Probability theory provides the foundation for making inferences about populations based on sample data. Probability distributions describe how probabilities are distributed over values of a random variable.

• Key Terms: Random variable, probability distribution, expected value.

• Common Distributions: Binomial, normal, and sampling distributions.

• Formula (Normal Distribution):

$f(x) = \frac{1}{\sigma \sqrt{2\pi}} e^{-\frac{(x-\mu)^2}{2\sigma^2}}$

• Example: Calculating the probability of a certain number of defective products in a shipment using the binomial distribution.

Inferential Statistics

Inferential statistics allow us to make conclusions about a population based on sample data. This includes estimation and hypothesis testing.

• Confidence Intervals: Range of values used to estimate a population parameter.

• Hypothesis Testing: Procedure to test assumptions about a population parameter.

• p-value: Probability of obtaining a result at least as extreme as the observed result, assuming the null hypothesis is true.

• Formula (Confidence Interval for Mean):

$\bar{x} \pm z^* \frac{\sigma}{\sqrt{n}}$

• Example: Testing whether a new marketing strategy increases average sales using a t-test.

Regression and Forecasting

Regression analysis examines the relationship between variables and is used for prediction and forecasting in business.

• Simple Linear Regression: Models the relationship between two variables.

• Multiple Regression: Models the relationship between one dependent variable and two or more independent variables.

• Formula (Simple Linear Regression):

$y = \beta_0 + \beta_1 x + \epsilon$

• Example: Predicting future sales based on advertising expenditure.

Excel Skills for Statistical Analysis

Excel is used extensively for data analysis in this course. Students will learn to use Excel functions and tools for statistical calculations and graphical representation.

• Key Excel Functions: AVERAGE, STDEV, NORM.DIST, BINOM.DIST, CONFIDENCE.T, T.TEST.

• Data Visualization: Creating charts and graphs to summarize data.

• Example: Using Excel to compute a confidence interval for the mean sales of a product.

Course Structure and Assessment

Item	Course Objective	Group/Individual	Weight
Class Activities	2 – 8	Individual/Group	15%
MyStatLab Excel Projects	2.3 – 3.8	Individual	15%
Homework	1 – 8	Individual	10%
Online Quizzes (2)	2 – 8	Individual	10%
Midterm Exams (2)	1 – 8	Individual	40%
Final Exam	1 – 8	Individual	25%

Grading Scale

Grade	Percentage
A	93 – 100
A-	90 – 92
B+	87 – 89
B	83 – 86
B-	80 – 82
C+	77 – 79
C	73 – 76
C-	70 – 72
D+	67 – 69
D	63 – 66
D-	60 – 62
F	Below 60

Course Calendar: Major Topics by Week

Week	Topics	Excel Skills	Assignments/Exams
1	Statistics introduction, types of data, collecting data		Homework
2	Describing data graphically (charts, graphs, histograms)	Graphs (bar charts, pie charts, histograms, scatterplots)	Homework
3	Measures of central tendency and dispersion	Numeric measures, Descriptive Statistics	Homework, Class Activity
4	Binomial and normal distributions	BINOM.DIST(), NORM.DIST()	Excel Project, Homework
5	Central limit theorem, sampling distributions	NORM.DIST()	Homework
6	Review & Exam 1		Exam 1
7	Confidence intervals for the mean	CONFIDENCE.NORM(), CONFIDENCE.T()	Class Activity, Homework, Excel Project
8	Hypothesis testing for means, p-values	Compute tests for means in Excel, P-value calculation	Homework
9	Confidence intervals and hypothesis tests for proportions	Compute tests for proportions in Excel, P-value calculation	Class Activity, Quiz 2, Homework

Academic Integrity and Policies

• Plagiarism: Submitting another person's work as your own is strictly prohibited and will result in disciplinary action.

• Exam Policies: Only Excel and course materials are permitted during exams. Unauthorized aids or applications are not allowed.

• Attendance: Regular attendance and participation are required for success in this course.

Support and Resources

• Tutoring: Free tutoring is available at the Student Success Center.

• Health and Safety: Follow university guidelines for illness and attendance.

• Canvas & TUMAIL: Course materials and announcements will be distributed via Canvas and TUMAIL.

Important Dates

• Classes begin: Monday, August 25

• Last day to drop a course: Tuesday, September 8

• Midterm Exam 1: Friday, October 17

• Thanksgiving Break: Monday, November 24 – Friday, November 28

Additional info: The course emphasizes the use of Excel for statistical analysis, and students are expected to bring a working laptop to class. The syllabus also highlights the importance of academic integrity and provides resources for student support and success.