Aug 16, 2022  
OHIO University Undergraduate Catalog 2022-23 
    
OHIO University Undergraduate Catalog 2022-23
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MATH 2500 - Statistics and Probability


A course in statistics and probability with focus on techniques and use of statistical software for organization of univariate and bivariate data, central tendency and dispersion, correlation, designed experiments, probability, random variables, binomial and normal distributions, sampling distributions, inferences from small and large samples, estimation, confidence intervals and hypothesis testing. The course introduces the language and mathematics underlying statistical reasoning. It emphasizes how the same mathematical ideas can be deployed in different areas of inquiry, and how one can combine these techniques with suitable technology to quantify the uncertainty present whenever one makes predictions based on empirical data, regardless of the field of study.

Requisites: (MATH 1060 or 1200 or 1250 or 1260 or 1321 or 1500) or Math placement 2 or higher and WARNING: Not COMS 3520 or ECON 3810 or GEOG 2710 or ISE 3040 or ISE 3200 or or ET 2450 or PSY 2110 or QBA 2010
Credit Hours: 4
OHIO BRICKS Arch: Constructed World
General Education Code (students who entered prior to Fall 2021-22): 1M
Repeat/Retake Information: May be retaken two times excluding withdrawals, but only last course taken counts.
Lecture/Lab Hours: 4.0 lecture
Grades: Eligible Grades: A-F,WP,WF,WN,FN,AU,I
College Credit Plus: Level 1
Learning Outcomes:
  • Students will be able to select and produce appropriate graphical, tabular, and numerical methods to summarize qualitative and quantitative data, univariate and bivariate data, and interpret and summarize the information into verbal descriptions.
  • Students will be able to identify the characteristics of a well-defined study and critically evaluate various aspects of the study, recognize the limitations of the study and also recognize common sources of bias in surveys.
  • Students will be able to compute and interpret various measures of central tendency (mean, median, partition values, etc.) and dispersion (standard deviation, variance, etc.).
  • Students will be able to investigate and describe the relationships or associations between two variables using caution in interpreting correlation and association, compute and interpret correlation coefficient and regression lines.
  • Students will be able to construct and model a random phenomenon using outcomes, events, and the assignment of probabilities, use addition and multiplication probability rules, and also compute conditional probabilities.
  • Students will be able to obtain and describe probability distributions, differentiate between discrete and continuous distributions, compute expected gain/loss, and make inferences based on these computations.
  • Students will be able to compute probabilities using theoretical probability distributions (binomial, normal, etc.) and interpret z-scores.
  • Students will be able to obtain and describe sampling distributions of mean, proportion, difference of means, difference of proportions, etc. and use the Central Limit Theorem.
  • Students will be able to estimate parameters, construct confidence intervals, compute and interpret margin of error, compute sample size for a given margin of error, and determine the effect of changing the sample size or confidence level.
  • Students will be able to formulate null and alternative hypotheses for a given research problem and describe the logic and framework of the inference of hypothesis testing.
  • Students will be able to perform a hypothesis test for a mean, proportion, difference of means, difference of proportions, etc. for large and small samples using p-value and critical (z and t) values and interpret statistical and practical significance.
  • Students will be able to perform chi-square test for hypotheses testing, analyze the results, and interpret these results.
  • Students will be able to use appropriate technology to carry out descriptive and inferential analysis of data and to perform statistical computations.
  • Students will be able to make judgments and draw appropriate conclusions based on the quantitative analysis of data while recognizing the limits of this analysis.
  • Students will be able to make and evaluate important assumptions in estimation, modeling, and data analysis.
  • Students will be able to express quantitative evidence in support of the argument or purpose of the work (in terms of what evidence is used and how it is formatted, presented, and contextualized).
  • Students will be able to critically state, describe, and consider an issue or problem.
  • Students will be able to use information from source(s) with enough interpretation/evaluation to develop a comprehensive analysis or synthesis.
  • Students will be able to systematically and methodically analyze assumptions and carefully evaluate the relevance of contexts when presenting a position.
  • Students will be able to state a specific position (i.e., perspective, thesis, or hypothesis) that is thoughtful, recognizes complexities, and acknowledges limitations.
  • Students will be able to state conclusions and related outcomes (consequences and implications) logically and in a priority order.



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