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Summer 2022

Mathematics Courses:

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MAT129 - Statistics

Credits: 3

Catalog Description: Studies descriptive data with graphical and numerical techniques, binomial and normal probability distributions, estimation and sampling, hypothesis testing, and linear regression models. Emphasis is on practical applications that include the use of statistical software that must be approved by the full-time faculty course coordinator. (Examples include MINITAB and StatCrunch.). Prerequisite: MAT 092, minimum grade of C in MAT 108, or higher; or by placement; or by permission of instructor.

Lecture: 3 hrs.

Course Learning Outcomes (CLOs):
Upon the successful completion of this course, a student will be able to:
1. Generate a graphical display for a set of data (frequency distribution, histogram, dot plot, stem and left, boxplot)
2. Compute the basic measures of central tendency for a data set (mean, median, mode).
3. Compute the basic measures of dispersion for a data set (range, variance, standard deviation).
4. Apply Empirical Rule or Chebyshev's Theorem to describe the distribution of data and identify the existence of any outliers.
5. Compute simple theoretical or empirical probabilities, applying the Addition, Multiplication, or Complement Rule as needed.
6. Find probabilities from a discrete probability distribution (including the binomial distribution).
7. Use the standard normal distribution table to find probabilities or cut-off values for non-standard normal distributions.
8. Find probabilities associated with the sample mean by applying the Central Limit Theorem.
9. Identify a confidence interval estimate for a population mean or population proportion.
10. Conduct a test of hypothesis for a population mean or population proportion.
11. Find the line of best fit and correlation coefficient for a set of bivariate data.

Effective Term: Fall 2019