Introduction to Statistics with Lab

Course: PSY230WLFirst Term: 2023 Fall
Final Term:
Current
Final Term:
9999 |
Lec + Lab 4.0 Credit(s) 5.0 Period(s) 5.0 Load
Credit(s) Period(s)
Load
AcademicLoad Formula: T- Lab Load |

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

MCCCD Official Course Competencies | |||
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1. Organize data for entry or analysis using appropriate tools (e.g., calculator, statistical software). (I, II)
2. Explain how statistics are used within the scientific method. (I, II, III, IV) 3. Use an appropriate scale of measurement to operationalize a construct of interest. (I, II, III, IV) 4. Evaluate the data collection, statistical analyses, interpretation, and presentation of research results in a variety of contexts. (I, II, III, IV) 5. Use the appropriate statistical test based on the research design (research question, data type, and number of variables). (I, II, III, IV) 6. Assess the practical implications and ethical considerations of statistical analysis in a variety of contexts beyond the classroom. (I, II, III, IV) 7. Communicate statistical concepts and research results using both technical (American Psychological Association [APA] format) and non-technical language. (I, II, III, IV) 8. Assess accuracy of data (including consideration of missing data and outliers) using an appropriate tool (e.g., calculator, graph, statistical software). (I, II, IV) 9. Verify potential violation of assumptions of a statistical test to determine the validity and reliability of results. (I, II, IV) 10. Compute descriptive statistics (including frequency distributions, measures of central tendency, measures of variability, and z-scores) and inferential statistics (including t-tests, analysis of variance, correlation/regression, and chi-square) both by hand and using statistical software. (II, IV) 11. Interpret descriptive statistics (including frequency distributions, measures of central tendency, measures of variability, and z-scores) and inferential statistics (including t-tests, analysis of variance, correlation/regression, and chi-square) using appropriate resources (e.g., results and output). (II, IV) 12. Explain the role of probability as the cornerstone of statistics. (III) 13. Utilize effect size, confidence intervals, power, and p-values to explain the statistical and practical significance of a statistical analysis. (IV) | |||

MCCCD Official Course Outline | |||

I. Fundamentals of statistics
A. Population and samples B. Sampling C. Research designs D. Variables E. Evaluating sources 1. Introduction to statistical software 2. Organizing data 3. Statistical test selection 4. Data entry 5. Data analysis 6. Assessing data accuracy F. Ethics II. Descriptive statistics A. Organization and display of data 1. Frequency distributions (tables and graphs) 2. Shapes of distributions B. Measures of central tendency 1. Mean 2. Median 3. Mode C. Measures of variability 1. Variance 2. Standard deviation D. Z-scores E. Percentiles III. Probability A. Central limit theorem B. Relationship between probability and inferential statistics IV. Inferential statistics A. Relationship between the normal distribution and z-scores B. Hypothesis testing C. Test statistics 1. Z-test 2. Single-sample t-test 3. Independent t-test 4. Dependent t-test 5. Analysis of variances 6. Correlation/Regression 7. Chi-square D. Interpreting statistical results 1. Statistical significance (p-value) 2. Practical significance a. Effect size b. Power c. Confidence intervals | |||

MCCCD Governing Board Approval Date: May 24, 2022 |

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.