MATH& 146 Introduction to Statistics

5 credits

This course covers the organization and graphical representation of data, measures of central tendency and variation, basic probability theory, discrete probability distributions and the binomial formula, the normal distribution, hypothesis testing, confidence intervals, correlation, and linear regression.

This course meets the Quantitative Reasoning general education distribution requirement.

Prerequisites: MATH 98 (recommended) or MATH 99 (or placement into MATH& 146)

Course Outcomes
Upon successful completion of this course the students will be able to:

Organize and graph data using scatterplots, histograms, and boxplots
Calculate mean, standard deviation, z-scores, and percentile values
Solve problems using the basic elements of probability theory, such as sample spaces, the addition rule, and the multiplication rule
Calculate the mean and standard deviation of a probability distribution
Calculate expected value
Calculate probabilities using the binomial probability formula
Calculate probabilities and find percentile values using the normal distribution and the central limit theorem
Construct and interpret confidence intervals
Perform hypothesis tests
Measure and test for correlation
Use regression equations to make predictions

General Education Distribution Area Outcomes
Students who successfully complete courses in the Quantitative Reasoning distribution area will be able to:

Gather, organize, and interpret data using multiple approaches
Develop and use mathematical models to describe and evaluate physical situations
Communicate problem-solving strategies and mathematically justify solutions
Select and utilize appropriate technology to analyze mathematical problems

Total Hours: 50 Theory (Lecture) Hours: 50