Course Overview

This course introduces students to the foundations of statistical reasoning, including probability, the use of large-scale data, and inferential statistics. It is designed for business students and covers both descriptive and inferential statistical methods.

Course Goals and Objectives

Course Outline

Data

• Chapter 1: An Introduction to Statistics – Overview of statistics, its role in business, and key terminology.

• Chapter 2: Data – Types of data (qualitative vs. quantitative), levels of measurement.

• Chapter 3: Surveys and Sampling – Sampling methods, survey design, sources of bias.

• Chapter 4: Displaying and Describing Categorical Data – Frequency tables, bar charts, pie charts.

• Chapter 5: Displaying and Describing Quantitative Data – Histograms, boxplots, measures of central tendency and dispersion.

• Chapter 6: Relationships, Associations and Correlation – Scatterplots, correlation coefficients.

Probability Distributions

• Chapter 7: Randomness and Probability – Basic probability rules, events, sample spaces.

• Chapter 8: Random Variables and Probability Distributions – Discrete and continuous random variables, probability mass and density functions.

Inference

• Chapter 9: Sampling Distributions – Central Limit Theorem, sampling variability.

• Chapter 10: Confidence Intervals for Proportions – Constructing and interpreting confidence intervals for population proportions.

• Chapter 11: Testing Hypotheses About Proportions – Hypothesis testing framework, p-values, significance levels.

• Chapter 12: Confidence Intervals and Hypothesis Tests for Means – Confidence intervals and hypothesis tests for population means.

Key Statistical Concepts

Descriptive Statistics

Descriptive statistics summarize and describe the main features of a dataset.

• Mean: The average value of a dataset.

• Median: The middle value when data are ordered.

• Mode: The most frequently occurring value.

• Standard Deviation: Measures the spread of data.

Probability

Probability quantifies the likelihood of events occurring.

• Probability of an event:

• Complement Rule:

• Addition Rule:

Random Variables and Distributions

• Discrete Random Variable: Takes on countable values (e.g., number of sales).

• Continuous Random Variable: Takes on any value within an interval (e.g., time, weight).

• Binomial Distribution: Probability of successes in independent Bernoulli trials.

• Normal Distribution: Symmetrical, bell-shaped distribution.

Sampling and Inference

• Central Limit Theorem: The sampling distribution of the sample mean approaches normality as sample size increases.

• Confidence Interval for Mean:

• Hypothesis Testing: Procedure to test claims about population parameters.

Course Resources

• Textbook: Business Statistics, Fourth Canadian Edition.

• Software: Excel and Stata for data analysis and visualization.

• Prism Resources: Access to training, financial lab, and member services.

Assessment Structure

Excel and Stata Training

Excel

• Basic Excel definitions

• General information

• Keyboard shortcuts

• Formatting and working with charts

• Formulas: SUM, AVERAGE, COUNTIF, etc.

• Working with tables and pivot tables

Stata

• Navigation and file types

• Basic data input

• Importing data

• Creating graphics

Course Policies

• Attendance and participation are expected.

• Missed midterms are handled by transferring weight to the final exam.

• Academic integrity is strictly enforced.

• Electronic devices are restricted during exams except for accessibility reasons.

Additional Info

• Students are encouraged to use Prism resources for additional support.

• Lecture slides and tutorials are posted online for review.

• Data collection for accreditation purposes is outlined in the syllabus.