Dec 08, 2023
 HELP 2015-2016 Undergraduate Academic Catalog [ARCHIVED CATALOG] Print-Friendly Page (opens a new window) Add to Portfolio (opens a new window)

# MA 315 - Nursing Statistics

3 lecture hours 0 lab hours 3 credits
Course Description
This course considers both visual and calculational aspects of statistics. The major portion of the course deals with the analysis of data, including medical data. Calculational topics include the estimation of population parameters, tests of hypotheses, and tests for goodness of fit. Note: this course is open only to students in the School of Nursing. (prereq: MA 125  or equivalent)
Course Learning Outcomes
Upon successful completion of this course, the student will be able to:
• Understand basic statistical terminology
• Produce data through sampling and experimental design
• Classify data by type
• Produce several methods of visually displaying data
• Compute measures of central tendency and measures of dispersion
• Understand the meaning, calculation and interpretation of linear regression and correlation results
• Have a basic understanding of the normal distribution and its application to appropriate statistical situations
• Have a basic understanding of the concepts of sampling error and sampling distributions
• Have an understanding as to the construction of confidence intervals for the population mean and the importance of the Student-t distribution to the construction of such confidence intervals
• Have an understanding concerning the performance of hypothesis tests for the mean of a single population.
• Have an understanding relating to inferences for the comparison of two population means
• Have a basic understanding with respect to the use of the chi-square distribution in goodness of fit and tests for independence calculations

Prerequisites by Topic
• Simplification of algebraic expressions containing fractions, exponents and radicals
• Factoring
• Cartesian coordinate system
• Systems of equations

Course Topics
• Descriptive and inferential statistics introduction and discussion (5 classes)
• Linear regression (2 classes)
• The normal distribution and its use in statistics (3 classes)
• The Central Limit Theorem and its importance to statistics (2 classes)
• Confidence intervals for the population mean (3 classes)
• Types of statistical errors
• Hypothesis testing (8 classes)
• Chi-square situations (2 classes)
• Analysis of variance (~2 classes)
• Exams (2 classes)

Coordinator
Ronald Jorgensen

Add to Portfolio (opens a new window)