Statistics for Managers: Course Syllabus and Study Guide

Statistics for Managers: Course Syllabus and Study Guide

Course Overview

This course introduces students to the fundamental concepts and applications of statistics in business management. It emphasizes the use of statistical methods for informed decision-making, interpretation of data, and communication of results in practical business contexts.

• Course Credit: 0.25 credits

• Prerequisites: None

• Professor: Amar Benaissa, BSc, MSc, PhD Candidate

Course Description

Techniques for using data to make informed use of statistics. Topics include applications, interpretation and limitations of results, sampling, descriptive statistics, probability concepts, estimation, and testing of hypotheses and regression, using practical business situations.

Course Rationale

This course focuses on making informed use of statistics. Students will learn to apply basic statistical methods, communicate analyses, and think critically about statistical data in a business context. The course supports better-informed decision-making by understanding the basics and limitations of statistical analysis.

Course Objectives

• CO1: Demonstrate the underlying mechanisms in basic statistics

• CO2: Determine how to communicate and display statistical information

• CO3: Evaluate fundamental data analysis results

• CO4: Recognize the assumptions and limitations underlying basic statistical methods

Main Topics

Probability and Statistical Storytelling

Probability is the foundation of statistical inference and decision-making in business. Understanding probability helps managers assess risk and make predictions based on data.

• Definition: Probability measures the likelihood of an event occurring, ranging from 0 (impossible) to 1 (certain).

• Key Concepts: Sample space, events, independent and dependent events.

• Formula: $P(A) = \frac{\text{Number of favorable outcomes}}{\text{Total number of possible outcomes}}$

• Example: The probability of drawing an ace from a standard deck of cards is $P(\text{Ace}) = \frac{4}{52} = 0.077$

Data Collection and Description

Data is the raw material for statistical analysis. Proper collection and description of data are essential for valid results.

• Types of Data: Qualitative (categorical) and quantitative (numerical).

• Descriptive Statistics: Methods for summarizing and describing data, including measures of central tendency and variability.

• Key Measures:

  • Mean: $\bar{x} = \frac{1}{n} \sum_{i=1}^{n} x_i$

  • Median: The middle value when data are ordered.

  • Mode: The most frequently occurring value.

  • Standard Deviation: $s = \sqrt{\frac{1}{n-1} \sum_{i=1}^{n} (x_i - \bar{x})^2}$

• Example: A manager summarizes monthly sales figures using the mean and standard deviation to assess performance consistency.

Sources and Sampling

Sampling is the process of selecting a subset of individuals from a population to estimate characteristics of the whole population.

• Sampling Methods: Simple random sampling, stratified sampling, cluster sampling.

• Importance: Proper sampling reduces bias and improves the reliability of statistical conclusions.

• Formula for Sample Mean: $\bar{x}_{\text{sample}} = \frac{1}{n} \sum_{i=1}^{n} x_i$

• Example: A company surveys a random sample of customers to estimate overall satisfaction.

Communicating and Summarizing Data

Effective communication of statistical results is crucial for business decision-making. Data can be summarized using tables, charts, and graphs.

• Tabular Presentation: Frequency tables, cross-tabulations.

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

• Example: A manager uses a bar chart to present sales by region to stakeholders.

Relationships: Correlation and Regression

Statistical relationships help managers understand how variables are related, which is essential for forecasting and strategic planning.

• Correlation: Measures the strength and direction of a linear relationship between two variables.

  • Formula: $r = \frac{\sum (x_i - \bar{x})(y_i - \bar{y})}{\sqrt{\sum (x_i - \bar{x})^2 \sum (y_i - \bar{y})^2}}$

• Regression: Predicts the value of one variable based on another.

  • Simple Linear Regression Equation: $y = a + bx$

• Example: A business uses regression analysis to forecast future sales based on advertising expenditure.

Predictions and Statistical Inference

Statistical inference allows managers to make predictions and decisions based on sample data, including estimation and hypothesis testing.

• Estimation: Using sample statistics to estimate population parameters.

• Confidence Interval Formula: $\bar{x} \pm z^* \frac{s}{\sqrt{n}}$

• Hypothesis Testing: Assessing claims about a population using sample data.

  • Example: Testing whether a new process improves average production time.

Course Map

Module	Topic	Reading	Activity	Deliverables
1	What's the story? Probability	Chapter 1, Chapter 8 (1.1 to 8.4)	Practice Problems	Discussion
2	Data	Chapter 2	Practice Problems	Quiz
3	Sources and sampling	Chapter 3	Practice Problems	Quiz
4	Communicating and summarizing (categorical)	Chapter 4	Practice Problems	Quiz
5	Summarizing and communicating (quantitative)	Chapter 5	Practice Problems	Quiz, Assignment
6	Relationships	Chapter 6	Practice Problems	Quiz
7	"Predictions"	Chapter 7	Practice Problems	Quiz, Assignment

Grading Scheme

Activity Type	Percent of Total Grade
Discussion	6%
6 Quizzes (Modules 2-7)	66%
2 Assignments	28%
Total	100%

Letter Grade Conversion

Percentage	Final Grade
90 - 100	A+
85 - 89	A
80 - 84	A-
77 - 79	B+
73 - 76	B
70 - 72	B-
67 - 69	C+
63 - 66	C
60 - 62	C-
57 - 59	D+
53 - 56	D
50 - 52	D-
0 - 49	F

Contribution to Program Learning Goals

MBA Learning Goal	Not Covered	Introduced	Taught but Not Assessed	Taught and Assessed
MB1 Leadership and Collaboration		✓
MB2 Communication			✓
MB3 Critical Thinking and Problem Solving				✓
MB4 Functional Knowledge
MB5 Global Business
MB6 Ethical Reasoning		✓

Additional Information

• Materials: Required textbook: Sharpe, De Veaux, Velleman & Wright (2021), Business Statistics, 4th Canadian Edition.

• Online Environment: The course is fully online and asynchronous, requiring digital skills and access to a computer.

• Late Deliverables: Penalties apply for late assignments; extensions considered for exceptional circumstances.

• Academic Integrity: Students must adhere to university policies regarding academic honesty.

• Support: Student Academic Support and Equity & Inclusion resources are available.