Course Overview

Introduction to Statistics

This course provides an introduction to statistical thinking and processes, focusing on methods and concepts for discovery and decision-making using data. Students will learn to analyze data, interpret statistical results, and apply statistical techniques to real-world problems across various disciplines.

• Topics include: Descriptive statistics, probability, sampling distributions, statistical inference, correlation, regression, analysis of variance, chi-squared tests, t-tests, and the use of technology for statistical analysis.

• Applications: Students will apply statistical methods to data from a broad range of fields, interpreting the relevance of statistical findings.

Course Structure & Components

Required Materials

• Textbook: Statistics: Informed Decisions Using Data, 7th Edition by Michael Sullivan (eText and MyLab access required).

• StatCrunch: Web-based software for statistical analysis, embedded in MyLab.

• Scientific Calculator: Required for calculations.

Course Components

• Lecture Notes & PowerPoint Presentations: Review chapter concepts and access additional resources via MyLab Multimedia Library.

• Projects & Lab Activities: Apply statistical techniques to real-world data and decision-making scenarios.

• Online Homework & Quizzes: Complete assignments in MyLab with immediate feedback and tutorial resources. Multiple attempts allowed for quizzes.

• Exams: Three regular exams and one comprehensive final, all proctored online via Yuja.

Key Topics in Statistics

Descriptive Statistics

Descriptive statistics summarize and describe the main features of a data set.

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

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

• Data Visualization: Histograms, bar charts, and box plots.

• Example: Calculating the mean and standard deviation of exam scores.

Formula for Mean:

Formula for Standard Deviation:

Probability & Sampling Distributions

Probability theory underpins statistical inference, describing the likelihood of events.

• Probability: The measure of the chance that an event will occur.

• Sampling Distribution: The probability distribution of a statistic (e.g., mean) from repeated samples.

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

• Example: Calculating the probability of drawing a red card from a deck.

Formula for Probability:

Statistical Inference

Statistical inference involves making conclusions about populations based on sample data.

• Confidence Intervals: Range of values within which a population parameter is likely to fall.

• Hypothesis Testing: Procedure to test claims about a population using sample data.

• Types of Tests: t-tests, chi-squared tests, ANOVA.

• Example: Testing whether the average height of students differs from a national average.

Formula for Confidence Interval (mean, known ):

Formula for Hypothesis Test (z-test):

Correlation & Regression

Correlation measures the strength and direction of a linear relationship between two variables. Regression predicts the value of one variable based on another.

• Pearson Correlation Coefficient (): Quantifies linear association.

• Simple Linear Regression: Models the relationship between two variables.

• Example: Predicting exam scores based on hours studied.

Formula for Pearson Correlation:

Formula for Regression Line:

where and

Analysis of Variance (ANOVA) & Chi-Squared Tests

ANOVA tests for differences among group means; chi-squared tests assess relationships between categorical variables.

• ANOVA: Used to compare means across three or more groups.

• Chi-Squared Test: Tests independence or goodness-of-fit for categorical data.

• Example: Testing if exam scores differ by major; testing if gender and major are independent.

Formula for ANOVA F-statistic:

Formula for Chi-Squared Statistic:

Student Learning Outcomes

• Critically analyze descriptive statistics by reading charts, graphs, and results of statistical analyses.

• Choose appropriate statistical techniques and use technology to perform necessary calculations.

• Interpret results for inferential statistical calculations, including confidence intervals, hypothesis testing, and regression analysis.

Grading Breakdown

Grade Distribution

Course Policies & Resources

Attendance & Participation

• Regular completion of online assignments and checking announcements/emails is required.

• Students are responsible for dropping the class if they stop attending.

Academic Integrity

• Use of AI tools is allowed for review and practice, but not for graded exams unless explicitly permitted.

• Cheating includes unauthorized material use, taking exams for others, or altering graded work.

Support & Tutoring

• Free tutoring available via LAMC LRC Math Center and Canvas resources.

• Online tutorials and tech demo assignments provided.

Tools

• Scientific calculator required.

• StatCrunch and Tables & Formulas sheet allowed on exams.

Important Dates

• Last date to enroll: 09/10/2025

• Final exam: 10/24/2025

• Other key dates for dropping/withdrawing are listed in the syllabus.

Additional info:

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

• Technology (StatCrunch, MyLab) is integrated throughout for analysis and assignments.