Applied Statistics MA2000-91330: Syllabus and Study Guide

Course Overview

Introduction to Applied Statistics

Applied Statistics is a foundational course designed to develop the statistical literacy of students from diverse academic backgrounds. The course emphasizes organizing and summarizing data, applying statistical techniques, and interpreting the results of statistical analyses. Students will learn to draw inferences from mathematical models and apply statistical reasoning to real-world problems in fields such as education, sociology, life and physical sciences, economics, and business.

• Textbook: Essentials of Statistics Plus MyStatLab with eText, 7th Edition, Mario F. Triola

• Prerequisite: Grade of C or higher in MA1010 Powertrack Math or Placement level of College Algebra-MA1020 or higher

Course Objectives

Learning Outcomes

Upon completion of this course, students will be able to:

• Become proficient in organizing and summarizing data using statistical tests and interpreting the results of these tests.

• Interpret and draw inferences from mathematical models such as graphs, tables, and summaries.

• Approach statistical information problematically, visually, numerically, and verbally.

• Employ quantitative methods to solve problems.

• Estimate and check mathematical results for reasonableness.

• Recognize the limits of mathematical and statistical methods.

Course Policies

Attendance and Punctuality

• Class attendance and punctuality are required; a record of attendance will be kept.

• If a class is missed, students are responsible for learning missed material and announcements.

Respect for All

• No cellphone use in class unless permission is granted.

Course Evaluation & Grading

Assessment Components

• Project: 15%

• Quizzes: 10%

• Homework: 5%

• Tests (3-4): 20%

• Midterm Exam: 20%

• Final Exam (Departmental): 30%

Total: 100%

Grading Scale

Grade	Score Range
A	94-100
A-	90-93
B+	87-89
B	84-86
B-	80-83
C+	77-79
C	74-76
C-	70-73
D+	67-69
D	64-66
D-	60-63
F	0-59

Accommodations for Students with Special Needs

Students with physical, psychological, medical, or learning disabilities should contact the Office of Services for Students with Disabilities (OSSD) for accommodations. All services are free and confidential.

Topics to Be Covered

1. Introduction to Statistics

• Statistical Thinking and Critical Thinking: Understanding the role of statistics in decision-making and scientific inquiry.

• Types of Data: Differentiating between qualitative and quantitative data.

• Collecting Sample Data: Methods for obtaining representative samples.

2. Exploring Data with Tables and Graphs

• Frequency Distributions: Organizing data into tables that show the frequency of each value.

• Histograms: Graphical representation of data distribution.

• Graphs that Enlighten and Graphs that Deceive: Identifying misleading visualizations.

• Scatterplots, Correlation, and Regression: Visualizing relationships between variables.

3. Statistics for Describing, Exploring, and Comparing Data

• Measures of Center: Mean, median, and mode.

• Measures of Variation: Range, variance, and standard deviation.

• Measures of Relative Standing & Boxplots: Percentiles, quartiles, and graphical summaries.

4. Probability

• Basic Concepts of Probability: Understanding likelihood and chance.

• Addition Rule and Multiplication Rule: Calculating probabilities for combined events.

• Complements, Conditional Probability: Probability of the complement and dependent events.

Key Formula:

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

• Addition Rule: $P(A \cup B) = P(A) + P(B) - P(A \cap B)$

• Multiplication Rule: $P(A \cap B) = P(A) \times P(B|A)$

5. Discrete Probability Distributions

• Probability Distributions: Assigning probabilities to discrete outcomes.

• Binomial Probability Distributions: Probability of a fixed number of successes in repeated trials.

• Parameters for Binomial Distributions: Mean and standard deviation for binomial variables.

• Poisson Probability Distribution: Modeling the number of events in a fixed interval.

Key Formula:

• Binomial Probability: $P(X = k) = \binom{n}{k} p^k (1-p)^{n-k}$

• Poisson Probability: $P(X = k) = \frac{\lambda^k e^{-\lambda}}{k!}$

6. Normal Probability Distributions

• The Standard Normal Distribution: Properties and applications of the normal curve.

• Real Applications of Normal Distributions: Using normal models in real-world contexts.

• Sampling Distribution: Distribution of sample statistics.

• The Central Limit Theorem: The foundation for inferential statistics.

Key Formula:

• Standard Normal: $Z = \frac{X - \mu}{\sigma}$

7. Estimating Parameters and Determining Sample Sizes

• Estimating Population Mean: Using sample data to estimate population parameters.

Key Formula:

• Confidence Interval for Mean: $\bar{x} \pm z^* \frac{\sigma}{\sqrt{n}}$

8. Hypothesis Testing

• Basics of Hypothesis Testing: Formulating and testing statistical hypotheses.

• Testing a Claim about a Mean: Using sample data to test population claims.

Key Formula:

• Test Statistic: $z = \frac{\bar{x} - \mu}{\sigma / \sqrt{n}}$

9. Inferences from Two Samples

• Two Means: Independent Samples: Comparing means from two groups.

• Two Dependent Samples (Matched Pairs): Analyzing paired data.

10. Correlation and Regression

• Correlation: Measuring the strength and direction of relationships between variables.

• Regression: Modeling the relationship between dependent and independent variables.

Key Formula:

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

• Regression Line: $y = a + bx$

Student Support and Resources

Tutorials

• Drop-in tutorial is available in the Mathematics Learning Center during the Fall and Spring semesters.

Basic Needs and Counseling

• Panther Community Care Center and Panther Food Pantry provide support for students experiencing difficulties.

• Counseling and Psychological Wellness Services are available for mental health support.

Title IX, Sexual Discrimination, Harassment, and Violence

• SUNY Old Westbury prohibits sexual discrimination, harassment, and violence. Confidential resources and support are available.

Additional info: This syllabus provides a comprehensive overview of the course structure, policies, grading, and topics. Students are encouraged to refer to the textbook and utilize campus resources for academic and personal support.