• Distinguished Lectures in Geometric Analysis

    The second in our series of Distinguished Lectures in Geometric Analysis will be given on October 31 and November 2, 2018, by Professor Bo Berndtsson from Chalmers University of Technology in Sweden.  The topic will be Complex Brunn-Minkowski theory. Read More
  • College Welcomes 19 New Faculty Members this Fall

    The College of Computer, Mathematical, and Natural Sciences welcomed 19 new tenured/tenure-track faculty members to the University of Maryland this fall. The brief introductions to the new faculty members can be read on CMNS website. Read More
  • In memory of our colleague David C. Lay

    Our long-time colleague Professor Eneritus David Lay passed away October 12, 2018. David earned his BA at Aurora College in 1962, and his PhD at UCLA in 1966. He then came to Maryland where he rose through the ranks to Professor in 1977. A gifted teacher, he won the campus Read More
  • The Canadian Journal of Statistics Award 2018, to Victor de Oliveira and Benjamin Kedem

    The Canadian Journal of Statistics Award is presented each year by the Statistical Society of Canada to the author(s) of an article published in the journal, in recognition of the outstanding quality of the methodological innovation and presentation. This year’s winner is the article entitled “Bayesian analysis of a density ratio Read More
  • Outstanding Director of Graduate Studies Award

    Professor Konstantina Trivisa has been selected for the Outstanding Director of Graduate Studies (DGS) Award for 2018.  Directors of Graduate Studies are critical partners of the Graduate School in shaping graduate education and ensuring the success of graduate students.  The Outstanding Director of Graduate Studies Award recognizes exceptional contributions made Read More
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Description

STAT 100 introduces the basic concepts of statistical reasoning and modern computer based techniques for organizing and interpreting data. Students will learn how to summarize data, how to interpret variability in data in terms of probability, and how to apply statistical methods to examples. Real world applications from the social, behavioral and biological sciences are used to illustrate the usefulness of statistical techniques. The MINITAB software package is used throughout the course, providing powerful and easy to use tools for statistical analysis. Computer exercises involving data reduction, graphics, simulation and statistical analysis will be assigned throughout the semester.

Prerequisites

Permission of Mathematics Department based on satisfactory score in Math Placement Exam or MATH 110 or MATH 115. Not open to students who have completed MATH 111 or any who have completed MATH or STAT course with a prerequisite of MATH 141.

Topics

Populations, samples and data description; MINITAB for data analysis.
Discrete probability, axioms.
Random variables, expected value, variance, standard deviation.
Binomial and normal probability laws.
Statistics and sampling distributions, behavior of averages, central limit theorem.
Estimating means, variances and proportions in large samples, hypothesis testing, confidence limits.
Inference in small samples, Student's t distribution.
Comparing means: paired comparisons, two independent samples, analyze bivariate data and see if a linear model is appropriate.

Categorical data: frequency tables, chi-squared tests for goodness of fit.

  • William E. Kirwan Hall, home of the Mathematics Department

    William E. Kirwan Hall, home of the Mathematics Department

  • The Experimental Geometry Lab explores the structure of low dimensional space

    The Experimental Geometry Lab explores the structure of low dimensional space

  • Maryland mathematicians help to investigate the inner workings of E_8

    Maryland mathematicians help to investigate the inner workings of E_8

  • Hyperbolic Space Tiled with Dodecahedra

    Hyperbolic Space Tiled with Dodecahedra

  • Isotropoic Gaussian random field with Matern correlation

    Isotropoic Gaussian random field with Matern correlation

  • Part of the proof of the Peter-Weyl theorem

    Part of the proof of the Peter-Weyl theorem