Introduction to Statistics
This collection contains the Affordable Learning Solution for the Southern University System (AL$4SUS) list of peer-reviewed textbooks for Introduction to Statistics (common course-id MATH 110), one of the top 50 college courses identified for inclusion in the Southern University Online Library for Education (SUOLforEd).
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General Course Description: MATH 110
The use of probability techniques, hypothesis testing, and predictive techniques to facilitate decision-making. Topics include descriptive statistics; probability and sampling distributions; statistical inference; correlation and linear regression; analysis of variance, chi-square and t-tests; and application of technology for statistical analysis including the interpretation of the relevance of the statistical findings. Applications using data from disciplines including business, social sciences, psychology, life science, health science, and education.
Minimum Units: 3.0
Any rationale or comment: Typically satisfies general education quantitative reasoning requirement (CSU GE B4).
Prerequisite(s): Intermediate Algebra
Upon successful completion of the course, students will be able to:
- Distinguish among different scales of measurement and their implications;
- Interpret data displayed in tables and graphically;
- Apply concepts of sample space and probability;
- Calculate measures of central tendency and variation for a given data set;
- Identify the standard methods of obtaining data and identify advantages and disadvantages of each;
- Calculate the mean and variance of a discrete distribution;
- Calculate probabilities using normal and student’s t-distributions;
- Distinguish the difference between sample and population distributions and analyze the role played by the Central Limit Theorem;
- Construct and interpret confidence intervals;
- Determine and interpret levels of statistical significance including p-values;
- Interpret the output of a technology-based statistical analysis;
- Identify the basic concept of hypothesis testing including Type I and II errors;
- Formulate hypothesis tests involving samples from one and two populations;
- Select the appropriate technique for testing a hypothesis and interpret the result;
- Use linear regression and ANOVA analysis for estimation and inference, and interpret the associated statistics; and
- Use appropriate statistical techniques to analyze and interpret applications based on data from disciplines including business, social sciences, psychology, life science, health science, and education.
- Homework or projects where students demonstrate their mastery of the learning objectives and their ability to devise, organize and present complete solutions to problems.
Online Statistics Education: An Interactive Multimedia Course of Study (Open (Access) Textbook)Developed by Rice University, University of Houston at Clear Lake, and Tufts University, this open textbook is a public... More