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. Minitab statistical software is used. Three class hours. MTH 160 is an appropriate elective for most programs. (SUNY-M)
Prerequisite(s): MTH 096 with a grade of B- or better, MTH 104, MTH 140, MTH 141, MTH 165 (or higher) with a grade of C or better, or MCC level 8 mathematics placement.
Course Learning Outcomes
1. Determine measures of central tendency, dispersion, and position for data.
2. Describe data using measures of central tendency, dispersion, and position.
3. Produce graphs of data such as histograms or box-and-whisker displays.
4. Analyze graphs using shape, measures of central tendency, and measures of dispersion.
5. Produce a scatter diagram of bivariate data.
6. Determine the linear correlation coefficient for bivariate data.
7. Determine the equation of the least-squares regression line for bivariate data.
8. Interpret bivariate data using linear correlation and linear regression.
9. Calculate probabilities of events using relative frequency and appropriate rules.
10. Construct a discrete probability distribution.
11. Use a discrete probability distribution to calculate probabilities and means.
12. Classify a random variable as binomial.
13. Use the binomial probability distribution to calculate probabilities, means, and standard deviations.
14. Explain the properties of the normal probability distribution including its relationship to the Empirical Rule.
15. Calculate probabilities for normal random variables using the Standard Normal Distribution.
16. Analyze normal random variables using the Standard Normal Distribution.
17. Create a sampling distribution of the sample mean. 18. Use the Central Limit Theorem to describe the sampling distribution of the sample mean.
18. Produce confidence intervals for various parameters. 20. Interpret confidence intervals for various parameters.
19. Generate hypothesis tests for various parameters. 22. Interpret hypothesis tests for various parameters.
20. Interpret Minitab output for any of the following: graphs, descriptive measures, linear correlation and linear regression results, and confidence intervals and hypothesis tests.
Course Offered Fall and Spring