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    Milwaukee School of Engineering
   
 
  Dec 17, 2017
 
 
    
2017-2018 Undergraduate Academic Catalog
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MA 262 - Probability and Statistics

3 lecture hours 0 lab hours 3 credits
Course Description
This course provides a basic introduction to the laws of probability needed to perform statistical analyses. Both descriptive and inferential statistics are considered. Probability distributions, the Central Limit Theorem, confidence intervals, hypothesis testing, and analysis of variance are considered in depth. Note: students cannot receive credit for both MA 262 and MA 3611 . (prereq: MA 137 )
Course Learning Outcomes
Upon successful completion of this course, the student will be able to:
  • Be familiar with the terminology and nomenclature of both probability and statistics
  • Know the difference between a parameter and a statistic
  • Know the difference between a population and a sample
  • Understand the basic concepts and properties of probability
  • Understand the meaning and significance of the standard deviation
  • Calculate the mean and variance of probability distributions
  • Be familiar with, and able to calculate probabilities of, the binomial, Poisson, Normal, Student-t, Chi-square, and F distributions
  • Construct appropriate confidence intervals for population parameters
  • Have a basic familiarity with the Central Limit Theorem and realize that it affects the calculations of test values and confidence intervals
  • Perform hypothesis tests concerning the means, variances, and proportions of one or two populations
  • Perform hypothesis tests concerning the comparison of means of more than two populations

Prerequisites by Topic
  • Algebra
  • Trigonometry
  • Differentiation of algebraic and transcendental functions
  • Integration of algebraic and transcendental functions

Course Topics
  • Measures of central tendency and dispersion
  • Introduction to probability and the laws of probability
  • Discrete probability distributions: binomial and Poisson
  • Introduction to the Central Limit Theorem
  • Continuous probability distributions: normal, t, chi-square, and F
  • One-sample hypothesis testing and statistical inference
  • One-sample confidence intervals and statistical inference
  • Two-sample confidence intervals and statistical inference
  • Two-sample hypothesis testing and statistical inference
  • Analysis of variance

Coordinator
Ron Jorgensen



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