MTH 160 - Statistics I
An introduction to descriptive and inferential statistics intended to give an understanding of statistical techniques and applications in a wide variety of disciplines. Topics include measures of central tendency; dispersion and position; correlation and regression; probability and probability distributions, including binomial and normal; the Central Limit Theorem; parameter estimation and hypothesis testing. Students critically analyze data, acknowledge limitations such as perspective and bias, and develop well-reasoned arguments to form conclusions. Statistical software is used.
Prerequisite(s): MTH 096 with a grade of B- or better; or any of the following with a grade of C or better: MTH 104, MTH 140, MTH 141, MTH 152, MTH 165 (or higher); or MCC Level 7 mathematics placement.
New SUNY General Education:
SUNY - Critical Thinking and Reasoning Competency
SUNY - Mathematics (and Quantitative Reasoning)
MCC General Education: MCC-CT - Critical Thinking (MCT), MCC-QL - Quantitative Literacy (MQL), MCC-TL - Technological Literacy (MTL)
Course Learning Outcomes
1. Determine a variety of descriptive statistics which may include any of the following measures: central tendency, dispersion, or position.
2. Analyze data using descriptive measures.
3. Produce graphs of data which may include any of the following: histograms, boxplots, or scatterplots.
4. Analyze statistical graphs.
5. Calculate quantitative values for various random variables which may include any of the following types: discrete, binomial, or normal.
6. Interpret probabilities for various probability distributions which may include any of the following types: discrete, binomial, or normal.
7. Use the Central Limit Theorem for applications involving the sampling distribution of the sample mean.
8. Determine confidence intervals for the population mean or the population proportion.
9. Analyze confidence intervals for the population mean or the population proportion.
10. Perform hypothesis tests for the population mean or the population proportion.
11. Determine the linear correlation coefficient for bivariate data.
12. Determine the equation of the least-squares line for bivariate data.
13. Analyze bivariate data.
14. Interpret statistical output.
Course Offered Fall, Spring
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Fall Semester 2023
Summer Session 2023