Introduction to Statistics

Course: PSY230First Term: 1996 Fall
Final Term:
Current
Final Term:
2016 Summer |
Lecture 3 Credit(s) 3 Period(s) 3 Load
Credit(s) Period(s)
Load
AcademicLoad Formula: S |

General Education Designation: Computer/Statistics/Quantitative Applications - [CS]

MCCCD Official Course Competencies | |||
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1. Prepare frequency and cumulative frequency distributions for grouped and ungrouped data. (I)
2. Prepare frequency and cumulative frequency polygons for grouped and ungrouped data. (I) 3. Compute mean, median, mode, and percentiles for grouped and ungrouped data. (I) 4. Identify the most appropriate measure of central tendency for given data. (I) 5. Compute variance, standard deviation, and estimate of population standard deviation from a sample for grouped and ungrouped data. (I) 6. Compute and interpret z-scores. (I) 7. Compute Pearson r and Spearman rho for grouped and ungrouped data. (I) 8. Interpret the significance of Pearson r and Spearman rho for given data. (I) 9. Compute area under the normal curve for given z-scores or standard deviations. (II) 10. Explain the concept of sampling distributions and the Central Limit Theorem. (II) 11. Compute and interpret the significance of a t-test for the hypothesized mean for a single distribution. (II) 12. Compute and interpret the significance of a t-test of the difference between two means from matched or independent samples. (II) 13. Compute and interpret one and two factor ANOVAs. (II) 14. Compute and interpret chi-square analyses. (III) | |||

MCCCD Official Course Outline | |||

I. Descriptive statistics
A. Organization of data 1. Frequency destribution 2. Graphic B. Measures of central tendency 1. Mean 2. Median 3. Percentiles 4. Mode C. Measures of variability 1. Variance 2. Standard deviation 3. Estimated measures 4. Z-scores D. Correlation 1. Pearson r 2. Spearman rho 3. Others II. Inferential statistics A. Normal curve B. Testing hypothesized mean for single distribution C. Testing difference between means from two distributions 1. Matched distributions 2. Independent distributions D. Analysis of variance 1. One factor ANOVAS 2. Two factor ANOVAS III. Non-parametric statistics A. Chi-square analysis B. Other | |||

MCCCD Governing Board Approval Date:
3/28/1995 |

All information published is subject to change without notice. Every effort has been made to ensure the accuracy of information presented, but based on the dynamic nature of the
curricular process, course and program information is subject to change in order to reflect the most current information available.