Course Overview

This course, QMS210: Applied Statistics for Business, introduces students to both descriptive and inferential statistics, focusing on their application in business decision-making. The curriculum covers foundational statistical concepts, data summarization, probability theory, sampling, estimation, hypothesis testing, and regression analysis, with practical use of statistical software such as SPSS and calculators.

Course Learning Outcomes

• Present and Describe Information: Use numerical and graphical descriptive summary measures to interpret and compare data sets.

• Apply Probability Concepts: Utilize probability distributions (e.g., Binomial, Poisson, Normal) to quantify uncertainty and assess risk.

• Draw Conclusions from Samples: Estimate population parameters, conduct hypothesis tests (including ANOVA and regression), and make recommendations based on statistical analysis.

• Use Statistical Software: Employ SPSS and calculators for data analysis and presentation in business contexts.

Topics and Course Schedule

Key Concepts and Definitions

Types of Data and Measurement Scales

• Qualitative Data: Non-numeric data representing categories or labels (e.g., gender, brand).

• Quantitative Data: Numeric data representing counts or measurements (e.g., sales, age).

• Measurement Scales: Nominal, Ordinal, Interval, and Ratio scales, each with increasing levels of quantitative meaning.

Tabular and Visual Summarization

• Frequency Distribution: Table showing the number of observations in each category or interval.

• Histogram: Bar graph representing the frequency distribution of quantitative data.

• Stem-and-Leaf Plot: Visual tool for displaying the shape of a data set while retaining original values.

• OGIVE: Cumulative frequency graph.

Numerical Descriptive Measures

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

• Measures of Variability: Range, variance, standard deviation, and interquartile range (IQR).

• Skewness: Describes the asymmetry of a distribution.

Probability and Probability Distributions

• Basic Probability: Likelihood of an event occurring, with values between 0 and 1.

• Discrete Distributions: Binomial and Poisson distributions for count data.

• Continuous Distributions: Normal distribution, characterized by mean (μ) and standard deviation (σ).

Sampling Distributions and Central Limit Theorem

• Sampling Distribution: Probability distribution of a statistic (e.g., sample mean) based on all possible samples.

• Central Limit Theorem: For large samples, the sampling distribution of the mean approaches normality, regardless of the population's distribution.

Confidence Interval Estimation

• Confidence Interval (CI): Range of values within which a population parameter is expected to lie, with a specified probability.

• Formula for CI for Mean (σ known):

• Formula for CI for Mean (σ unknown):

• Formula for CI for Proportion:

Hypothesis Testing

• Null Hypothesis (H0): Statement of no effect or difference.

• Alternative Hypothesis (H1): Statement indicating the presence of an effect or difference.

• Type I Error (α): Rejecting H0 when it is true.

• Type II Error (β): Failing to reject H0 when it is false.

• Test Statistic: Calculated value used to decide whether to reject H0.

• p-value: Probability of observing a test statistic as extreme as, or more extreme than, the observed value under H0.

Two-Sample Tests

• Independent Samples: Comparing means or proportions from two unrelated groups.

• Pooled Variance t-test: Used when variances are assumed equal.

• Separate Variance t-test: Used when variances are not assumed equal.

• F-test: Used to compare two variances.

Analysis of Variance (ANOVA)

• One-Way ANOVA: Tests for differences among means of three or more groups.

• Test Statistic:

Regression Analysis

• Simple Linear Regression: Models the relationship between two variables using the equation:

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

Course Materials and Tools

• Textbook: Business Statistics, 15th custom edition for Toronto Metropolitan University (e-textbook with MyLab Statistics access).

• Calculator: CASIO fx-9750GIII (or similar model).

• Software: SPSS (available free through the university).

Assessment and Evaluation

Academic Integrity and Policies

• All submitted work must be original and reflect the student's own understanding.

• Use of generative AI is restricted to idea generation or study aid, not for submitted work.

• Strict adherence to university policies on academic integrity, accommodations, and copyright is required.

Support and Resources

• Access to library research help, student learning support, and academic accommodation services.

• Mental health and wellbeing resources are available for all students.

Note: The course schedule and policies are subject to change. Students are responsible for staying informed via D2L and university announcements.