Statistics Syllabus and Course Structure: Rutgers University

Course Overview

Introduction to Applied Statistics

This course provides a comprehensive introduction to applied statistics, focusing on the analysis of statistical inference. Students will learn about confidence interval estimation, hypothesis testing, and the use of both parametric and non-parametric methods. The course emphasizes practical applications in various fields and the use of internet-based statistical tools.

• Course Credits: 3

• Department: Statistics, Rutgers University

• Prerequisite: Level I Statistics (960:201 or 960:285 or equivalent)

• Mathematical or Formal Reasoning (QR) and Quantitative Information (QQ)

Course Learning Objectives

By the end of this course, students should be able to:

• Understand the "what, why, and how" of Normal Approximation Statistics

• Know assumptions needed to apply these statistical methods

• Construct and interpret confidence intervals and hypothesis tests

• Compute and interpret measures of mean, standard deviation, correlation, regression, ANOVA

• Apply goodness of fit and contingency statistics

• Utilize statistical software for homework and tests

Course Materials

Required Texts and Resources

• Textbook: Introductory Statistics, 10th edition, Neil Weiss, Pearson Publishers (ISBN: 9780136937797)

• CANVAS Website: https://rutgers.instructure.com/courses/538436

• MYLAB Website: https://mylabstats.pearson.com/northamerica/

• STATCRUNCH Website: https://www.statcrunch.com

• PEARSON-RESPONDUS LOCKDOWN BROWSER: Required for exams

Technology Requirements

• CANVAS/MYLAB/Pearson-Respondus Lockdown Browser

• Zoom compatible computers and webcams for remote lectures

Course Structure

Online Learning

• Lectures and notes are posted weekly on CANVAS

• Homework and exams are completed online through MYLAB and STATCRUNCH

• Recorded lectures and class notes are accessible via CANVAS modules

Assessment and Grading

Grading Breakdown

• Weighted average of homework and exams is used

• Homework (given weekly) constitutes 20% of the grade

• Exams (3 open-book tests) constitute 80% of the grade

• Each assignment is given equal weight

Grade	Final Score (%)
A	≥ 90.0
B+	85.0 - <90.0
B	80.0 - <85.0
C+	75.0 - <80.0
C	70.0 - <75.0
D	60.0 - <70.0
F	<60.0

Assessment Policy

Homework

• Assigned for each chapter of the textbook

• Students will usually have 2-3 weeks to complete assignments

• Extra credit available for some assignments

• Homework plays a key role in learning

Online Tests

• Two tests: MID CLASS and END OF CLASS TEST

• Each test is 2 hours in duration

• There is a 2-day window to start the test

• Exam problems resemble those from homework

Academic Integrity

• Cheating, plagiarism, and unauthorized collaboration are strictly prohibited

• Do not use Artificial Intelligence to answer questions on assignments or tests

• Violations will be reported to the Office of Student Conduct

Sources for Academic Help

• Instructor and TA office hours

• Discussion Board on CANVAS

• Rutgers Learning Centers: http://rlc.rutgers.edu

Key Statistical Concepts Covered

Statistical Inference

Statistical inference involves drawing conclusions about populations based on sample data. This includes estimation and hypothesis testing.

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

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

• Parametric Methods: Assume underlying statistical distributions (e.g., normal distribution).

• Non-parametric Methods: Do not assume specific distributions.

Descriptive Statistics

• Mean ($\bar{x}$): Average value of a dataset.

• Standard Deviation ($s$): Measure of data spread.

• Correlation ($r$): Measure of linear relationship between two variables.

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

• ANOVA (Analysis of Variance): Compares means across multiple groups.

Statistical Software

• StatCrunch: Used for homework and tests.

• MYLAB: Platform for assignments and exams.

Example Formulas

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

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

• Confidence Interval for Mean (Normal): $\bar{x} \pm z^* \frac{s}{\sqrt{n}}$

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

Additional info:

• Students are expected to use online platforms for all coursework and assessments.

• Course emphasizes both theoretical understanding and practical application of statistics.