# Texas A&M University Central Texas Math 311 Probability and Statistics Online

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1 Texas A&M University Central Texas Math 311 Probability and Statistics Online Instructor: Mienie de Kock (Ph.D) Office: Warrior Hall Room 412- B Phone: (903) Office Hours: M 4pm 6pm Graduate Assistant in Warrior Hall Room 420 B face- to- face and online T 1:30pm 7:30pm Dr. De Kock in Warrior Hall Room 420- B face- to- face and online R 3pm 5pm online Blackboard access: ( Cengage access: UNILERT Emergency Warning System for Texas A&M University Central Texas UNILERT is an emergency notification service that gives Texas A&M University- Central Texas the ability to communicate health and safety emergency information quickly via , text message, and social media. All students are automatically enrolled in UNILERT through their myct account. Connect at to change where you receive your alerts or to opt out. By staying enrolled in UNILERT, university officials can quickly pass on safety- related information, regardless of your location.

2 1.0 Course Overview and description: COURSE INFORMATION Math 311 (Probability and Statistics) is designed to prepare students for the Probability and Statistics domain on the TExES Math 7-12 exam. The class aims to equip students with the requisite knowledge and skills that an entry- level educator in this field in Texas public schools must possess. The class also incorporates Microsoft Excel as a tool for statistical analysis techniques. 2.0 Course Objective: 2.1 Student Learning Outcomes Students will become thoroughly proficient in the following areas: Data and Statistics Descriptive Statistics Probability Discrete Probability distributions Continuous Probability distributions Simple linear regression Multiple regression Regression analysis (model building) 2.2 Competency Goals Statements The class focuses on the following competencies on the probability and statistics domain for the Texes Math 7-12 certification exam as outlined by the Texas Education Agency: The student understands how to use appropriate graphical and numerical techniques to explore data, characterize patterns and describe departures from patterns. Competency 15: 1. Selects and uses an appropriate measurement scale (i.e., nominal, ordinal, interval, ratio) to answer research questions and analyze data. 2. Organizes, displays and interprets data in a variety of formats (e.g., tables, frequency distributions, scatter plots, stem-and-leaf plots, box-and-whisker plots, histograms, pie charts). 3. Applies concepts of center, spread, shape and skewness to describe a data distribution. 4. Understands measures of central tendency (i.e., mean, median, mode) and dispersion (i.e., range, interquartile range, variance, standard deviation). 5. Applies linear transformations (i.e., translating, stretching, shrinking) to convert data and describes the effect of linear transformations on measures of central tendency and dispersion. 6. Analyzes connections among concepts of center and spread, data clusters and gaps, data outliers and measures of central tendency and dispersion. 7. Supports arguments, makes predictions and draws conclusions using summary statistics and graphs to analyze and interpret one-variable data.

3 Competency 016: The teacher understands concepts and applications of probability. The student: 1. Understands how to explore concepts of probability through sampling, experiments and simulations and generates and uses probability models to represent situations. 2. Uses the concepts and principles of probability to describe the outcomes of simple and compound events. 3. Determines probabilities by constructing sample spaces to model situations. 4. Solves a variety of probability problems using combinations and permutations. 5. Solves a variety of probability problems using ratios of areas of geometric regions. 6. Calculates probabilities using the axioms of probability and related theorems and concepts such as the addition rule, multiplication rule, conditional probability and independence. 7. Understands expected value, variance and standard deviation of probability distributions (e.g., binomial, geometric, uniform, normal). 8. Applies concepts and properties of discrete and continuous random variables to model and solve a variety of problems involving probability and probability distributions (e.g., binomial, geometric, uniform, normal). Competency 017: The teacher understands the relationships among probability theory, sampling and statistical inference and how statistical inference is used in making and evaluating predictions. The beginning teacher: 1. Applies knowledge of designing, conducting, analyzing and interpreting statistical experiments to investigate real-world problems. 2. Analyzes and interprets statistical information (e.g., the results of polls and surveys) and recognizes misleading as well as valid uses of statistics. 3. Understands random samples and sample statistics (e.g., the relationship between sample size and confidence intervals, biased or unbiased estimators). 4. Makes inferences about a population using binomial, normal and geometric distributions. 5. Describes and analyzes bivariate data using various techniques (e.g., scatterplots, regression lines, outliers, residual analysis, correlation coefficients). 6. Understands how to transform nonlinear data into linear form to apply linear regression techniques to develop exponential, logarithmic and power regression models. 7. Uses the law of large numbers and the central limit theorem in the process of statistical inference. 8. Estimates parameters (e.g., population mean and variance) using point estimators (e.g., sample mean and variance).

4 3.0 Required Reading and Textbook(s): Students need to purchase an access code to mindtap on Cengage Learning s website. Instructions are below:

6 COURSE REQUIREMENTS 4.0 Course Requirements: All assignments will be posted on mindtap. Students are expected to complete all the activities and assignments via a learning path that might consist of reading assignments, lecture videos, homework, quizzes and excel worksheets. Assignments are due as indicated on mindtap and the weekly schedule. NO late assignments will be accepted. Online discussions: Project: Two online discussions will be available on blackboard. One project in excel to illustrate the application of theory learned in class. Midterm: The Midterm is a proctored exam and will cover Chapters 1-6. The student needs to schedule a time to take the Midterm at a testing center and provide the proctor s contact information to the instructor. The student is allowed to use a calculator on the midterm, but no notes. Final: The final will be cumulative (Chapters 1-6, 14-16) and is proctored. The student needs to schedule a time to take the Midterm at a testing center and provide the proctor s contact information to the instructor. The student is allowed to use a calculator on the midterm, but no notes. Make- ups will only been given if student has a university approved excuse and can provide documentation for it. COURSE OUTLINE AND CALENDAR

7 eek 1 Jan 19 Martin Luther King Jr. Day Jan 20 Get registered for course on Cengage learning mindtap Jan 21 Jan 22 eek 2 Jan 26 Chapter 1 and 2 due. eek 3 Feb 2 Chapters 3 and 4 due. eek 4 Feb 9 Chapters 5 and Discussion 1 due. Jan 27 Jan 28 Jan 29 Feb 3 Feb 4 Feb 5 Feb 10 Feb 11 Feb 12 eek 5 Feb 16 Chapter 6 due Feb 17 Feb 18 Review (Face- to- face) Feb 19 MIDTERM (PROCTORED) On Chapters 1-6 eek 6 Feb 23 Online discussion 2 due. eek 7 Mar 2 Chapter 14 due Feb 24 Feb 25 Feb 26 Mar 3 Mar 4 Mar 5 Project due eek 8 Mar 9 Chapter 15 due Mar 10 Chapter 16 due. Review Mar 11 Proctored, comprehensive final on Chapters 1-6, Mar 12

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