Statistical Measurements, Analysis & Research: Course Syllabus and Study Guide

General Course Information

Course Overview

This course, Statistical Measurements, Analysis & Research, provides students with quantitative and qualitative techniques for analyzing marketing data. The course is designed for graduate students in integrated marketing and covers a variety of statistical methods used in marketing research, including sampling, hypothesis testing, regression, and modeling.

• Instructor: Jeffrey Baliban

• Schedule: Tuesdays, 3:00 PM - 5:30 PM (Fall 2025)

• Location: Midtown Center

• Modality: In-Person | Synchronous

Course Description

Purpose and Scope

The course equips marketers with statistical tools to develop consumer insights, determine market potential, and assess marketing strategies. Students will learn to apply statistical techniques to real-world marketing problems, including:

• Sampling techniques and market segmentation

• Hypothesis testing for product and service evaluation

• Regression analysis for predicting outcomes

• Conjoint analysis and choice modeling

• Graphical representation of marketing data

Emphasis is placed on measures of central tendency, dispersion, and distributional properties of data.

Prerequisites

• Restriction: Integrated Marketing GCDMMS Plan

Learning Outcomes

Skills and Competencies

Upon successful completion, students will be able to:

1. Evaluate qualitative and quantitative data to maximize customer relationships for program planning.

2. Apply statistical techniques to forecast profitability and customer acquisition.

3. Create statistically sound marketing tests to determine ROI and profitability.

4. Design market research surveys to assess product and service success.

5. Analyze syndicated research for segment and audience targeting.

6. Determine campaign effectiveness using statistical modeling.

Course Structure and Modality

Weekly Organization

The course is structured around weekly sessions, each including:

• PREPARE: Assigned readings, videos, and articles.

• DEMONSTRATE: Assignments and assessments to apply knowledge.

• EXPLORE: Optional materials for deeper understanding.

Class meetings are held once a week for 2.5 hours. Sessions begin with current events in statistics as applied to marketing, followed by lectures and discussions.

Course Technology

Required and Recommended Tools

• Pearson MyLab: Online homework and exam system integrated with the textbook.

• SPSS: Statistical software available to students for $50 via NYU Hub.

• Microsoft Excel: Used for data analysis and assignments.

• Statdisk: Free online statistical software by Mario Triola.

Students will also have access to LinkedIn Learning for Excel and SPSS tutorials.

Textbooks and Course Materials

• Main Text: Triola Elementary Statistics, 14th Edition, Pearson (2025)

• Additional: SPSS tutorials, Excel training, and Statdisk access

Assessment and Grading

Components

• Homework: Practice problems and assignments via MyLab and Brightspace

• Midterm Exam: Covers first half of course content

• Final Exam: Comprehensive assessment

• Class Participation: Active engagement in discussions (5%)

Assignments must be submitted on time; late submissions may incur penalties. Attendance is required but does not count as participation.

Course Outline

Weekly Topics

Week	Main Topic	Key Concepts
1	Introduction to Statistics	Statistical concepts, measures of central tendency, graphical data representation
2	Data Types & Collection	Qualitative vs. quantitative data, histograms, data sources
3	Descriptive Statistics	Mean, median, mode, dispersion
4	Probability Concepts	Basic probability, probability distributions
5	Sampling & Sampling Distributions	Sampling methods, sample size estimation
6	Standard Normal Distribution	Z-scores, normal curve applications
7	Midterm Exam	Review and assessment
8	Estimation & Confidence Intervals	Population mean estimation, confidence intervals
9	Hypothesis Testing	Formulating and testing hypotheses
10	Regression Analysis	Simple and multiple regression, correlation
11	Advanced Topics	Conjoint analysis, choice modeling
12	Marketing Applications	Market share, consumer perceptions
13	Comprehensive Review	Preparation for final exam
14	Final Exam	Comprehensive assessment

Key Statistical Concepts

Definitions and Examples

• Mean: The average value of a dataset. $\text{Mean} = \frac{\sum x_i}{n}$

• Median: The middle value when data are ordered.

• Mode: The most frequently occurring value in a dataset.

• Standard Deviation: A measure of data dispersion. $s = \sqrt{\frac{\sum (x_i - \bar{x})^2}{n-1}}$

• Z-score: Standardized value indicating how many standard deviations a data point is from the mean. $z = \frac{x - \mu}{\sigma}$

• Confidence Interval: Range of values likely to contain the population parameter. $\bar{x} \pm z^* \frac{\sigma}{\sqrt{n}}$

• Regression Equation: Predicts the value of a dependent variable based on independent variables. $y = \beta_0 + \beta_1 x + \epsilon$

Course Policies

Attendance and Participation

• Attendance is required; participation is graded separately.

• Students must be respectful, engaged, and collaborative.

• Absences must be communicated in advance; documentation may be required for medical absences.

Academic Integrity

• All assignments must be original work.

• University policies on academic honesty and accommodations apply.

Additional info:

• Some details inferred from context and standard statistics syllabi, such as the inclusion of regression, hypothesis testing, and confidence intervals as core topics.

• Software training (Excel, SPSS, Statdisk) is emphasized for practical data analysis skills.

• Course is designed for graduate students in marketing, but statistical concepts are broadly applicable.