Oct 27, 2020
MATH 2500 - Introduction to Statistics
An introductory course in applied statistics. Organization of data, central tendency and dispersion, descriptive bivariate data, correlation, designed experiments, probability, random variables, binomial and normal distributions, distributions, inferences from large samples, estimation, confidence intervals and hypothesis testing. Students cannot earn credit for MATH 2500 and any of the following: COMS 3520, ECON 3810, GEOG 2710, ISE 3040, ISE 3200, PSY 1110, PSY 2110, QBA 2010.
Requisites: (MATH 1200 or 1250 or 1260 or 1321) or Math placement 2 or higher and WARNING: Not COMS 3520 or ECON 3810 or GEOG 2710 or ISE 3040 or ISE 3200 or PSY 1110 or PSY 2110 or QBA 2010
Credit Hours: 4
General Education Code: 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
Course Transferability: OTM Course: TMM010 Introductory Statistics
College Credit Plus: Level 1
- Carry out a hypothesis test for a mean or proportion. Interpret statistical and practical significance in this setting.
- Compute conditional probabilities in the context of two-way tables.
- Construct a model for a random phenomenon using outcomes, events, and the assignment of probabilities. Use the addition rule for disjoint events and the multiplication rule for independent events.
- Determine the appropriate sample size for a specific margin of error and confidence level.
- Estimate a population mean or proportion using a point estimate and confidence intervals, and interpret the confidence level and margin of error.
- Given a research question involving a single population, formulate null and alternative hypotheses. Describe the logic and framework of the inference of hypothesis testing.
- Introduce the concept of a sampling distribution. Discuss the distribution of the sample mean and sample proportion under repeated sampling (Central Limit Theorem).
- Investigate and describe the relationships or associations between two variables using caution in interpreting correlation and association.
- Make a decision using a p-value and draw an appropriate conclusion. Interpret statistical significance.
- Perform interval estimation and hypotheses testing for two-sample problems (e.g., difference of two means or proportions and chi-square test of independence).
- Select and produce appropriate graphical, tabular, and numerical summaries of the distributions of variables in a data set. Summarize such information into verbal descriptions.
- Students should be expected to simulate or generate sampling distributions to observe, empirically, the Central Limit Theorem.
- Summarize relationships in bivariate data using graphical, tabular, and numerical methods including scatter plots, two-way tables, correlation coefficients, and least squares regression lines.
- Understand how the type of data collection can affect the types of conclusions that can be drawn.
- Understand the dependence of margin of error on sample size and confidence level.
- Understand the principles of observational and experimental studies including sampling methods, randomization, replication, and control.
- Use the normal distribution to interpret z-scores and compute probabilities.
Add to Portfolio (opens a new window)