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• MATH 350: History of Mathematics

  3.00 Credits

  Indiana University of Pennsylvania-Main Campus

  
  Development of the central concepts of mathematics from ancient times up to the development of calculus in the 17th century. Mathematical concepts are placed in historical perspective. The use of the history of mathematics as a pedagogical tool and its relationship to other sciences is addressed. Prerequisite:    ( MATH 122 or MA 122 or MATH 124 or MA 124 or MATH 126 )
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• MATH 353: Theory of Numbers

  3.00 Credits

  Indiana University of Pennsylvania-Main Campus

  
  Divisibility, congruences, primitive roots, number theoretic functions, Diophantine equations, continued fractions, quadratic residues. Prerequisite:    ( MATH 271 or MA 271 )
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• MATH 355: Foundations of Geometry I

  3.00 Credits

  Indiana University of Pennsylvania-Main Campus

  
  Studies various groups of transformations and geometries associated with these groups in the Euclidean plane. The Euclidean Similarity and affine and projective groups of transformations are studied. Prerequisite:    ( MATH 171 or MA 171 ) and ( MATH 271 or MA 271 )
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• MATH 363: Mathematical Statistics I

  3.00 Credits

  Indiana University of Pennsylvania-Main Campus

  
  Probability theory necessary for an understanding of mathematical statistics is developed. Applications of set theory to models, combinations and permutations, binomial, Poisson and normal distributions, expected values, and moment generating functions.May Be: (may be taken concurrently) Prerequisite:    ( MATH 225 or MA 122 or MATH 124 or MA 225 ) and ( MATH 216 or MA 216 )
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• MATH 364: Mathematical Statistics II

  3.00 Credits

  Indiana University of Pennsylvania-Main Campus

  
  Multivariate distributions, change of variable technique, chi-square distribution, estimation, confidence intervals, hypothesis testing, contingency tables, goodness of fit. Practical applications are used to aid in the development. Prerequisite:    ( MATH 363 or MA 363 )
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• MATH 371: Linear Algebra

  3.00 Credits

  Indiana University of Pennsylvania-Main Campus

  
  Vector spaces and linear transformations are studied in a theoretical setting. Also, canonical forms and multilinear algebra are studied. Prerequisite:    ( MATH 171 or MA 171 ) and ( MATH 271 or MA 271 )
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• MATH 400: Topics in Analysis and Topolgy

  3.00 Credits

  Indiana University of Pennsylvania-Main Campus

  
  Examines topics in analysis and topology. Explores topological topics, restricted to the real line, including open/closed sets, interior and boundary points, metrics, compactness and connectedness. Investigates topics in analysis including convergence of sequences, convergence of functions, continuity and uniform continuity, differentiation, and Riemann integration.
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• MATH 405: Tpcs Abstrat Algebra Numbr Thr

  3.00 Credits

  Indiana University of Pennsylvania-Main Campus

  
  Examines topics in Abstract Algebra and Number Theory. Explores, beginning with Number Theory, topics including division algorithm, Fundamental Theorem of Arithmetic, Euclidean algorithm, and the Chinese Remainder Theorem. Considers concepts in Abstract Algebra, including groups, rings, and fields.
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• MATH 411: Univariate Data Analysis

  3.00 Credits

  Indiana University of Pennsylvania-Main Campus

  
  Focuses on descriptive statistics, statistical inference procedures, regression analysis, analysis of variance, and analysis of deviance. Develops inference procedures for population means and proportions. Uses simple linear regression, multiple linear regression, one-way ANOVA, two-way ANOVA, Poisson regression, and logistic regression models to analyze data. Emphasizes the applied aspects of these statistical models and uses computer software for data analysis.
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• MATH 412: Multivariate Statistics

  3.00 Credits

  Indiana University of Pennsylvania-Main Campus

  
  An applied statistics course that focus on multivariate statistical methods. Research procedures on the relationship among variables, significance of group differences, prediction of group membership, and structure exploration are introduced. Factorial analysis of variance, analysis of covariances, multivariate analysis of variance and covariance, path analysis, factor analysis, and discriminate analysis are introduced and used to analyze data. Emphasizes the applied aspects of these statistical methods and uses computer software for data analysis.
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