Nov 28, 2021
 HELP OHIO University Undergraduate Catalog 2021-22 Print-Friendly Page (opens a new window)

# MATH 1500 - Introductory Statistics

An introductory course on conceptual understanding of statistical methods and techniques, including descriptive statistics, correlation and regression, elementary probability, estimation, confidence intervals, hypothesis testing, and the use of software. The course emphasizes the reasoning skills necessary for understanding and critically evaluating statistical information. No credit if taken after MATH 2500 or PSY 2110 or QBA 2010 or ISE 3040 or ISE 3200 or COMS 3520 or Econ 3810 or GEOG 2710. Students cannot earn credit for MATH 1500 and PSY 1110 (first course taken deducted).

Requisites: MATH placement level 1 or MATH D004 or MATH D005 and no credit if taken after COMS 3520, ECON 3810, GEOG 2710, ISE 3040 or 3200, MATH 2500, PSY 1110 or 2110, or QBA 2010
Credit Hours: 3
OHIO BRICKS Foundations: Quantitative Reasoning
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: 3.0 lecture
Course Transferability: OTM course: TMM010 Introductory Statistics
College Credit Plus: Level 1
Learning Outcomes:
• Students will be able to summarize univariate and bivariate data, quantitative and qualitative data by employing appropriate graphical, tabular, and numerical methods and interpret the information from these graphs.
• Students will be able to describe the attributes of the data or the relationships between the data and distinguish between quantitative and qualitative datasets, univariate and bivariate datasets.
• 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 variation (standard deviation, variance, etc.).
• Students will be able to compute and interpret correlation coefficient and regression lines from a given bivariate dataset.
• Students will be able to model random phenomenon and assign probabilities, compute probabilities, and conditional probabilities.
• Students will be able to obtain and describe probability distributions, compute expected gain/loss, and make inferences based on these computations.
• Students will be able to compute probabilities using normal distributions.
• Students will be able to identify the statistic and the parameter in a research problem, explain the difference between them, and describe the sampling distribution.
• Students will be able to construct confidence intervals for mean and proportion, 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 in the case of research problems involving mean and proportions, test the significance of null and alternative hypotheses using critical and p-values and interpret the results.
• Students will be able to use appropriate technology such as spreadsheets or statistical software to perform statistical computations.
• Students will be able to interpret statistical results and information in news stories and journal articles and apply the concepts learned in the course to their discipline of study or a related area.