# 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.

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.

**SUNY General Education:** SUNY-M - Mathematics (SMAT)

**MCC General Education:** MCC-CT - Critical Thinking (MCT), MCC-QL - Quantitative Literacy (MQL), MCC-TL - Technological Literacy (MTL)

**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

**Use links below to see if this course is offered:**

Spring Semester 2022

Summer Session 2022