This course is an introduction to descriptive statistics, probability and its applications, statistical inference and hypothesis testing, predictive statistics and linear regression.
Prerequisites
Upon successful completion of the course, students should be able to demonstrate the following knowledge or skills:
- Determine appropriate methods to compute various probabilities
- Identify, analyze, and describe statistical distributions
- Use statistics to make population-level inferences
- Use linear regression to identify correlations and draw inferences
IO2: Students will be able to reason mathematically.
- Types of variables (numerical, categorical, explanatory, response, confounding, etc.)
- Sampling strategies
- Measures of center and spread
- Graphical displays of data
- Basic probability, including sample space and simple probabilities, disjoint and independent events, complementary events, multiplication rules/addition rules, marginal and conditional probabilities, expected value
- Normal and Binomial distributions
- Central Limit Theorem
- Hypothesis testing for
- 1 and 2 parameters
- Proportions, means, goodness-of-fit/independence
- ANOVA
- Correlation and simple linear regression
Optional Topics:
Simpson’s paradox
Study/Experimental design and its implications
Hypothesis testing for paired data
Poisson distribution
Bayes’ Theorem
In order to give the instructor the greatest flexibility in assigning a grade for the course, grades will be based on various instruments at the instructors' discretion. However, to maintain instructional integrity there must be at least three class exams and a statistical project designed to show the student the application side of statistics. At least 60% of the grade will be based on quantifiable work (exams, homework, quizzes, etc.). The remaining portion of the grade may be based on quantifiable work, attendance, projects, journal work, etc., at the instructor's discretion.
The following is a compilation of acceptable grading instruments: In class exams and a final, attendance, homework or quizzes, research paper, modeling projects on the calculator or computer. Other projects or assignments as deemed appropriate at the instructor's discretion.
For students who are concurrently enrolled in JIT 071
- Students who only pass JIT 071 and do not pass MATH& 146 will not earn any placement adjustment.
- Students who do not pass JIT 071 will automatically not pass MATH& 146 too. It is advised they drop both courses if this happens.
- Students who pass both JIT 071 and MATH& 146 will earn the SQR credit and receive a placement score allowing them to take any non-math class that lists MATH 098 as a prerequisite.
This course will also assess PO5: Students will be able to solve problems by gathering, interpreting, combining, and/or applying information from multiple sources.