MCCCD Program Description

Elements of Statistics

Course: MAT206

First Term: 2019 Summer

Lecture 3.0 Credit(s) 3.0 Period(s) 3.0 Load
Subject Type: Academic
Load Formula: S - Standard Load

Description: Basic concepts and applications of statistics, including data description, estimation and hypothesis tests.

MCCCD Official Course Competencies
1. Identify the difference between descriptive and inferential statistics. (I) 2. Distinguish between a population and a sample. (II) 3. Group a set of data and present the grouping in graphical form. (II) 4. Determine the mean, median, mode and standard deviation of data set and find the z-score for a data piece. (III) 5. Define random variable and the probability distribution of a random variable. (IV) 6. Find probabilities for normal random variables by using the standard normal distribution. (V) 7. Construct random samples. (VI) 8. Graph the sampling distribution of the mean for all sample sizes and all populations. (VI) 9. Find point and interval estimates of population means and proportions. (VII) 10. Describe the logic of hypothesis testing emphasizing the role of probability distributions and types of error. (VIII) 11. Perform inferences about one mean or proportion in the case of normal populations or large sample size. (VIII) 12. Perform inferences about two means or proportions in the case of normal populations or large sample size. (IX) 13. Use the Chi-square goodness-of-fit test to determine if two populations have the same shape. (X) 14. Use the Chi-square independence test to determine whether two characteristics of a population are associated (dependent). (X) 15. Identify the best-fitting regression line for a set of data points. (XI) 16. Partition the total sum of squares for a set of data points to find measures of regression line fit and linear relationship. (XI)
MCCCD Official Course Competencies must be coordinated with the content outline so that each major point in the outline serves one or more competencies. MCCCD faculty retains authority in determining the pedagogical approach, methodology, content sequencing, and assessment metrics for student work. Please see individual course syllabi for additional information, including specific course requirements.

MCCCD Official Course Outline
I. Nature of statistics A. Inferential vs. descriptive statistics B. Classification of statistical studies C. Development of inferential statistics II. Organizing data A. Types of data B. Grouping data C. Stem-and-leaf diagrams D. Misleading graphs III. Descriptive measures A. Measures of central tendency B. Summation notation: the sample mean C. Measures of dispersion: the sample standard deviation D. Interpretation of the standard deviation: z-scores E. Percentiles: box-and-whisker diagrams IV. Probability concepts A. Introduction: classical probability B. Discrete random variables: probability distributions V. Normal distribution A. Standard normal curve B. Normal curves C. Normally distributed populations D. Normally distributed random variables VI. Sampling distributions A. Sampling: random samples B. Sampling error: the need for sampling distributions C. The mean and standard deviation of the sample mean D. The sampling distribution of a Mean E. The sampling distribution of a proportion VII. Inferential statistics - estimation - one population A. Estimating a population mean B. Estimating a population proportion C. Confidence intervals for one population mean D. Confidence intervals for one population proportion E. Sample size considerations F. Confidence intervals for a normal population VIII. Inferential statistics - hypothesis testing - one population A. The nature of hypothesis testing B. Terms, errors, and hypotheses C. Large sample hypothesis tests for a population mean D. Large sample hypothesis tests for a population proportion E. Hypothesis tests for a normal population IX. Inferential statistics - two populations A. Confidence intervals concerning two population means B. Confidence intervals concerning two population proportions C. Hypothesis tests concerning two population means D. Hypothesis tests concerning two population proportions E. Independent samples F. Paired samples X. Chi-square procedures A. The Chi-square distribution B. Chi-square goodness-of-fit test C. Chi-square independence test XI. Methods in linear regression A. Linear equations with one dependent variable B. Regression equation C. Coefficient of determination D. Linear correlation E. Inferences in correlation

MCCCD Governing Board Approval Date: January 22, 2019

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.