Course Descriptions

MTH 162 - Statistics for the Social Sciences

4 Credits

An introduction to descriptive and inferential statistics intended to give an understanding of statistical techniques and applications used in the social sciences. Topics include: measures of central tendency, dispersion and position; correlation and regression; probability and probability distributions, including binomial and normal; parameter estimation and hypothesis testing; two-sample analysis; chi-square test of independence; one-way analysis of variance. Statistical software will be used. This course is intended for, but not limited to Social Science majors. Students who have completed MTH 160 or MTH 161 may not receive additional credit for this course. Four class hours.(SUNY-M)

Prerequisite(s): MTH 096 with a grade of B- or better, or 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. Interpret data graphically using measures of central tendency, dispersion, and position
2. Interpret data numerically using measures of central tendency, dispersion, and position
3. Create a scatter diagram to represent bivariate data
4. Analyze the relationship between variables using linear correlation and linear regression
5. Perform significance tests for linear correlation
6. Classify a random variable as binomial
7. Use the binomial probability distribution to compute probabilities, means, and standard deviations
8. Explain the properties of the normal probability distribution and its parameters
9. Compute probabilities for normal variables as areas, probabilities, or proportions
10. Use the Central Limit Theorem to describe the sampling distribution of the sample mean
11. Produce confidence intervals using both the z and t probability distributions
12. Interpret confidence intervals using both the z and t probability distributions
13. Generate hypothesis tests about mu and p, using both the z and t probability distributions
14. Deduce conclusions about mu and p, using both the z and t probability distributions
15. Perform a two-sample analysis using independent and dependent samples
16. Interpret the results of two-sample analyses using independent and dependent samples
17. Calculate effect size for different samples
18. Interpret the results of effect size for different samples
19. Complete chi-square tests of independence
20. Interpret the results of a chi-square test for independence
21. Execute a one-way analysis of variance
22. Interpret the results of a one-way ANOVA as the effect of a factor on the response variable
23. Use statistical software to produce statistical graphs
24. Use statistical software to compute statistical measures
25. Use statistical software to create estimates of statistical measures
26. Test hypotheses using statistical software to generate necessary values
27. Interpret results obtained using statistical software

Course Offered Fall and Spring

Use links below to see if this course is offered:
Fall Semester 2017
Intersession 2018
Spring Semester 2018