Course Overview

Introduction to STA 2023: Introductory Statistics

This course provides a comprehensive introduction to statistics, focusing on both descriptive and inferential methods. Students will learn to visualize, summarize, and interpret data, apply probability concepts, analyze random variables, and use statistical inference to make decisions based on sample data. The course is suitable for students across various disciplines and emphasizes practical applications using technology, such as the TI-84 calculator and statistical software.

Required Materials

Textbook and Technology

• Textbook: Essential Statistics (Pearson Education, 3rd Edition) by Robert Gould, Rebecca Kimmae Wong, Colleen Nooter Ryan

• Calculator: TI-83 or TI-84 graphing calculator with statistical features

• Computer: Internet access, compatible browser (Google Chrome), webcam, and necessary software/plugins for Canvas and Pearson MyStatLab

Course Outcomes

Learning Objectives

• Visualize and summarize data using descriptive statistics

• Apply basic probability concepts to draw reasonable conclusions

• Employ concepts of random variables, sampling distributions, and the Central Limit Theorem

• Choose appropriate inferential methods, including confidence intervals and hypothesis testing

• Model linear relationships using correlation and regression

Assessment Structure

Grading Breakdown

The course grade is determined by a combination of homework, quizzes, tests, and a final exam. Extra credit opportunities may be available.

Grade Scale

Course Outline and Topics

Weekly Topics and Chapter Coverage

• Week 1: Introduction, terminology, classifying data, types of studies (observational vs. controlled experiment)

• Week 2: Graphing data – Bar charts, Pareto diagrams, pie charts, histograms, dot plots, stem-and-leaf plots

• Week 3: Numerical summaries – Center, spread, shape; mean, median, mode, range, standard deviation, interquartile range, boxplots, z-scores, Empirical Rule

• Week 4: Test 1 (Chapters 1–3)

• Week 5: Correlation and simple linear regression – Scatterplots, correlation coefficient, regression line, slope, y-intercept, predictions, extrapolation, coefficient of determination

• Week 6: Probability – Union, intersection, conditional probability, independent/dependent events

• Week 7: Discrete and continuous random variables – Normal distribution, probability, inverse Normal problems

• Week 8: Test 2 (Chapters 4–6)

• Week 9–10: Sampling distributions, expected value, standard error, Central Limit Theorem, probabilities involving sample proportion

• Week 10–11: Confidence intervals for population proportion, inference, margin of error

• Week 11: Hypothesis tests for one and two population proportions, test statistics, p-values, conclusions

• Week 12–13: Sampling distribution for sample mean, confidence intervals, hypothesis tests for population mean

• Week 14: Test 3 (Chapters 7–9)

• Final Exam Week: Cumulative final exam (Chapters 1–9)

Key Statistical Concepts

Descriptive Statistics

Descriptive statistics involve methods for summarizing and visualizing data. Common measures include:

• Mean: The average value of a data set.

• Median: The middle value when data are ordered

• Mode: The most frequently occurring value

• Range: Difference between maximum and minimum values

• Standard Deviation: Measures spread around the mean.

• Interquartile Range (IQR): Difference between the 75th and 25th percentiles

• Boxplot: Visual representation of the five-number summary (minimum, Q1, median, Q3, maximum)

Probability and Random Variables

Probability quantifies the likelihood of events. Random variables can be discrete (countable outcomes) or continuous (measurable outcomes).

• Probability of an event:

• Conditional Probability:

• Normal Distribution: Bell-shaped curve described by mean and standard deviation

• Empirical Rule: For normal distributions, about 68% of values fall within , 95% within , and 99.7% within

Sampling Distributions and Central Limit Theorem

Sampling distributions describe the distribution of a statistic (e.g., mean, proportion) from repeated samples. The Central Limit Theorem states that the sampling distribution of the sample mean approaches a normal distribution as sample size increases, regardless of the population's distribution.

• Standard Error:

• Central Limit Theorem: For large , is approximately normal with mean and standard error

Confidence Intervals

Confidence intervals estimate population parameters with a specified level of confidence (e.g., 95%).

• Confidence Interval for Mean:

• Margin of Error:

Hypothesis Testing

Hypothesis testing evaluates claims about population parameters using sample data. Key steps include:

• State null () and alternative () hypotheses

• Choose significance level ()

• Calculate test statistic

• Find p-value and draw conclusion

Correlation and Regression

Correlation measures the strength and direction of linear relationships between variables. Regression models predict values using the least squares method.

• Correlation Coefficient: ranges from -1 to 1

• Regression Line:

• Coefficient of Determination: measures proportion of variance explained

Student Success Tips

• Participate actively and apply concepts to real-world situations

• Practice interpreting numerical and graphical results

• Review textbook, lesson videos, and take detailed notes

• Work through homework problems by hand

• Seek help from instructor, Learning Commons, or library as needed

Additional info:

• All images included are directly relevant to course structure, materials, and grading.

• Formulas and equations are provided in LaTeX format for clarity and exam preparation.

• Tables are recreated in HTML for accessibility and study reference.