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General Information

Course ID (CB01A and CB01B)
MATHD010H
Course Title (CB02)
Introductory Statistics - HONORS
Course Credit Status
Credit - Degree Applicable
Effective Term
Fall 2023
Course Description
This course is an introduction to data analysis making use of graphical and numerical techniques to study patterns and departures from patterns. The student studies randomness with an emphasis on understanding variation, collects information in the face of uncertainty, checks distributional assumptions, tests hypotheses, uses probability as a tool for anticipating what the distribution of data may look like under a set of assumptions, and uses appropriate statistical models to draw conclusions from data. The course introduces the student to applications in engineering, business, economics, medicine, education, social sciences, psychology, the sciences, and those pertaining to issues of contemporary interest. The use of technology (computers or graphing calculators) will be required in certain applications. Where appropriate, the contributions to the development of statistics by men and women from diverse cultures will be introduced. MATH 10 and therefore Math 10H is a required lower-division course for students majoring or minoring in many disciplines such as data science, nursing, business, and others. Because this is an honors course, the students will be expected to complete extra assignments to gain deeper insight into probability and statistics.
Faculty Requirements
Course Family
Not Applicable

Course Justification

This is an introductory Statistics course offering students diverse, relevant, and contemporary applications predicated on topics in Intermediate Algebra. This is a general education course which meets CSU upper-division transfer admission requirements for one course in college-level mathematics (CSU area B4), and the UC requirement for one course in mathematical concepts and quantitative reasoning (IGETC area 2A). MATH 10 is a requirement in the °®¶¹´«Ã½ Political Science AA certificate. This course is the honors version of MATH D010. and as a result, includes more advanced assignments and assessments.

Foothill Equivalency

Does the course have a Foothill equivalent?
Yes
Foothill Course ID
MATH F010.

Course Philosophy

Course Philosophy
The primary goal and philosophy for this course is to focus on an investigatory approach, emphasizing meaning and interpretation of applications and less reliance on lengthy formulas and programming. The approach that the department has taken in its statistics classes has been motivated by work in the Quantitative Literacy and the Activity Based Statistics projects. Laboratory assignments provide students the opportunity to explore the principles of statistics through the use of technology. Examples and homework problems may be based on real data taken from a variety of contemporary resources, including current articles from the newspaper, web or other print media. These examples often have themes that are of contemporary societal or cultural concern.

Formerly Statement

Course Development Options

Basic Skill Status (CB08)
Course is not a basic skills course.
Grade Options
  • Letter Grade
  • Pass/No Pass
Repeat Limit
0

Transferability & Gen. Ed. Options

Transferability
Transferable to both UC and CSU
°®¶¹´«Ã½ GEArea(s)StatusDetails
2GA3°®¶¹´«Ã½ GE Area A3 - Critical ThinkingApproved
CSU GEArea(s)StatusDetails
CGB4CSU GE Area B4 - Mathematics/Quantitative ReasoningApproved
IGETCArea(s)StatusDetails
IG2XIGETC Area 2 - Mathematical Concepts and Quantitative ReasoningApproved
C-IDArea(s)StatusDetails
MATHMathematicsApprovedC-ID MATH 110

Units and Hours

Summary

Minimum Credit Units
5.0
Maximum Credit Units
5.0

Weekly Student Hours

TypeIn ClassOut of Class
Lecture Hours5.010.0
Laboratory Hours0.00.0

Course Student Hours

Course Duration (Weeks)
12.0
Hours per unit divisor
36.0
Course In-Class (Contact) Hours
Lecture
60.0
Laboratory
0.0
Total
60.0
Course Out-of-Class Hours
Lecture
120.0
Laboratory
0.0
NA
0.0
Total
120.0

Prerequisite(s)

Intermediate algebra or equivalent (or higher), or appropriate placement beyond intermediate algebra

Corequisite(s)

Advisory(ies)

ESL D272. and ESL D273., or ESL D472. and ESL D473., or eligibility for EWRT D001A or EWRT D01AH or ESL D005.

Limitation(s) on Enrollment

  • (Not open to students with credit in the non-Honors related course.)
  • (Admission into this course requires consent of the Honors Program Coordinator.)

Entrance Skill(s)

General Course Statement(s)

(See general education pages for the requirements this course meets.)

Methods of Instruction

Lecture and visual aids

Discussion of assigned reading

Discussion and problem solving performed in class

In-class exploration of Internet sites

Quiz and examination review performed in class

Homework and extended projects

Guest speakers

Collaborative learning and small group exercises

Collaborative projects

Activities which involve students in formal exercises of data collection and analysis

Problem solving and exploration activities using applications software

Problem solving and exploration activities using courseware

Assignments

  1. Required readings from the text and other (optional) sources
  2. Problem solving exercises that include written explanations of concepts and justification of conclusions. These exercises may be based upon real data.
  3. Technology based projects/activities that include written descriptions of methods and results, and justification of conclusions. These technology based projects/activities may be based upon real, simulated or collected data.
  4. Collaborative activities requiring conversation in small groups.
  5. Two hour comprehensive final examination composed of both computational and concept based questions which will require the student to demonstrate ability in integrating the methods, ideas and techniques learned in class. Questions may also require the student to communicate ideas and conclusions in short essay format.
  6. In addition, the honors project assignment should include completion of additional sets of advanced problems that require a deeper understanding of the topics and/or a written research report (10 to 15 pages).

Methods of Evaluation

  1. A minimum of two one hour examinations composed of both computational and concept based questions which will require the student to demonstrate ability in integrating the methods, ideas and techniques learned in class. Questions may also require the student to communicate ideas and conclusions in short essay format.
  2. A minimum of three technology based projects/activities that make use of graphing calculators or computers addressing randomness, variation, and simulation will be evaluated for accuracy, completeness, and proper use of techniques and methods discussed in class. Questions may also require the student to communicate ideas and conclusions in short essay format. For examples, see applicable activities in the Scheaffer book listed in Supporting References.
  3. Two hour comprehensive final examination composed of both computational and concept based questions which will require the student to demonstrate ability in integrating the methods, ideas and techniques learned in class. Questions may also require the student to communicate ideas and conclusions in short essay format.
  4. Problem solving exercises (homework) and/or quizzes will be evaluated for accuracy and completion in order to assess student’s comprehension of material covered in lecture and to provide feedback to students on their progress. Questions may also require the student to communicate ideas and conclusions in short essay format.
  5. Classroom participation and interaction in the discussion of the subject matter in small groups. This may include discussion of real-world statistics applications addressing contemporary social issues.
  6. The honors advanced problems and research report would be evaluated for accuracy of response, depth of analysis, critical thinking skills, and a comprehensive discussion of the research topic.

Essential Student Materials/Essential College Facilities

Essential Student Materials: 
  • Graphing calculator (at discretion of instructor)
Essential College Facilities:
  • Computer lab

Examples of Primary Texts and References

AuthorTitlePublisherDate/EditionISBN
OpenStax College, Introductory Statistics, openstaxcollege.org, 2013
Brase/Brase,"Understandable Statistics: Concepts and Methods", 12th Ed., Brooks Cole, Cengage Learning Systems, 2018.
Geraghty, Maurice. "Inferential Statistics and Probability - A Holistic Approach", Licensed under a Creative Commons-Attribution-ShareAlike 4.0, 2018.
Navidi and Monk, "Elementary Statistics", 2nd Ed., McGraw Hill, 2015.
Soler, Frank. Statistics. "Understanding Uncertainty". 4th ed. Associated Research Consultants, Cupertino 2017.

Examples of Supporting Texts and References

AuthorTitlePublisher
Bluman, "Elementary Statistics, A Step by Step Approach, A Brief Version" 6th ed. McGraw Hill 2013.
Devore, Jay L. "Probability and Statistics for Engineering and the Sciences". 9th ed. Cengage 2016.
Larson and Farber. "Elementary Statistics Picturing the World". 6th ed. Pearson 2014.
Packel, Edward. "The Mathematics of Games and Gambling" 2nd ed. The Mathematical Association of America, 2006.
Peck, R., et al. "Statistics: A .Guide to the Unknown" 4th ed. Cengage 2006.
Scheaffer, Richard L. "Activity Based Statistics" 2nd ed. Wiley eBook 2009.
Stigler,Stephen M. "The History of Statistics, The Measurement of Uncertainty before 1900". Belknap Press, 1990.
Sullivan III, Michael. "Statistics: Informed Decisions Using Data". 5th ed. Pearson 2017.
Tintle, Rossman, Chance, et al, "Introduction to Statistical Investigations", 16th ed, Wiley, 2018.
Triola, Mario F. "Elementary Statistics", 13th edition, Pearson, 2017.
http://nebula2.deanza.edu/~stats - °®¶¹´«Ã½ College Math 10 Curriculum - Supporting Internet references

Learning Outcomes and Objectives

Course Objectives

  • Explore statistical techniques and process statistical information in order to make decisions about the reliability of a statement, claim or "fact"; Identify the standard methods of obtaining data and identify advantages and disadvantages of each.
  • Examine the nature of uncertainty and randomness and set up data collection methods that are free of bias; Distinguish among different scales of measurement and their implications.
  • Organize, display, summarize, and interpret data using graphical and statistical techniques; Interpret data displayed in tables and graphically; Calculate measures of central tendency and variation for a given data set.
  • Use probability to model and understand randomness; Apply concepts of sample space and probability.
  • Examine distributions of data using graphical and analytical methods; Calculate the mean and variance of a discrete distribution.
  • Describe data distribution through the study of sampling distributions; Distinguish the difference between sample and population distributions and analyze the role played by the Central Limit Theorem.
  • Estimate parameters by constructing point estimates and confidence intervals; Calculate probabilities using normal and t-distributions; Construct and interpret confidence intervals.
  • Compose probability statements about how confident one can be about making decisions based on data and construct the Type I and Type II error probabilities based on this decision; Determine and interpret levels of statistical significance including p-values; Interpret the output of a technology-based statistical analysis; Identify the basic concept of hypothesis testing including Type I and II errors; Formulate hypothesis tests involving samples from one and two populations; Select the appropriate technique for testing a hypothesis and interpret the result.
  • Compose conjectures about bivariate and ANOVA theoretical models; Use linear regression analysis and ANOVA for estimation and inference, and interpret the associated statistics.
  • Use appropriate statistical techniques to analyze and interpret applications based on data from disciplines including business, social sciences, psychology, life sciences, health sciences and education.
  • Explore in depth advanced topics of probability and statistics through problem-solving and/or projects.

CSLOs

  • Organize, analyze, and utilize appropriate methods to draw conclusions based on sample data by constructing and/or evaluating tables, graphs, and numerical measures of characteristics of data.

  • Identify, evaluate, interpret and describe data distributions through the study of sampling distributions and probability theory.

  • Collect data, interpret, compose and defend conjectures, and communicate the results of random data using statistical analyses such as interval and point estimates, hypothesis tests, and regression analysis.

Outline

  1. Explore statistical techniques and process statistical information in order to make decisions about the reliability of a statement, claim or "fact"; Identify the standard methods of obtaining data and identify advantages and disadvantages of each.
    1. Recognize that statistics is an applied branch of mathematics and a unique discipline.
    2. Use proper statistical techniques for gathering data
    3. Access published statistical information in a variety of formats
    4. Understand how statistics uses mathematical logic to measure uncertainty
    5. Understand the major components of statistics: descriptive and inferential
    6. Statistical analysis using technology such as SPSS, EXCEL, Minitab, or graphing calculators.
  2. Examine the nature of uncertainty and randomness and set up data collection methods that are free of bias; Distinguish among different scales of measurement and their implications.
    1. The origins of randomness in antiquity (see http://faculty.deanza.fhda.edu/mathiosdiane/) and its difference from deterministic models
    2. The need to model uncertainty
    3. Data and sampling methods
    4. Distinguish among different scales of measurement and their implications
    5. Statistical analysis using technology such as SPSS, EXCEL, Minitab, or graphing calculators.
  3. Organize, display, summarize, and interpret data using graphical and statistical techniques; Interpret data displayed in tables and graphically; Calculate measures of central tendency and variation for a given data set.
    1. Graphical techniques for data: stem leaf, histogram, box plots
    2. Descriptions of the shape of data: symmetrical or skewed
    3. Descriptions of the center of data: mean, median, mode
    4. Descriptions of variation of the data: range, variance, standard deviation
    5. Descriptions of the location of data: quartile, percentile, z score, interquartile range
    6. Identify outliers
    7. Statistical analysis using technology such as SPSS, EXCEL, Minitab, or graphing calculators.
  4. Use probability to model and understand randomness; Apply concepts of sample space and probability.
    1. The historical origins of probability theory in 17th century Europe (optional)
    2. Modeling random outcomes
      1. Sample spaces
      2. Events
      3. Tree diagrams and (optional) Venn diagrams
      4. Compound events: the use of "and" and "or" (multiplication and addition rules)
      5. Complements
    3. Independent and mutually exclusive events
    4. Conditional probabilities
    5. Bayes' Theorem (optional)
    6. Statistical analysis using technology such as SPSS, EXCEL, Minitab, or graphing calculators.
  5. Examine distributions of data using graphical and analytical methods; Calculate the mean and variance of a discrete distribution.
    1. Discrete Probability Distributions
      1. Discrete random variables: expected value and variance
      2. Binomial distribution
        1. Properties of the Binomial Distribution
        2. The origins of the Binomial Distribution in the arithmetic triangle of China and Pascal and the historical development of binomial probabilities by the Bernoulli family (optional)
      3. Conceptual understanding of other discrete probability distributions (optional):
        1. Poisson Distribution
        2. Hypergeometric Distribution
        3. Geometric distribution
        4. Negative Binomial (Pascal) Distribution
    2. Continuous Probability Distributions
      1. Continuous random variables: probability is equal to area
      2. Area (probability) and percentile computations
      3. Normal (Gaussian) Distribution
        1. The normal random variable
        2. Standard normal distribution
        3. Its historical development by Carl Friedrich Gauss in the 19th century (optional)
        4. The normal approximation to the binomial (optional)
      4. Additional continuous probability distributions (optional)
        1. Uniform distribution
        2. Exponential distribution
        3. Linear distribution
    3. Statistical analysis using technology such as SPSS, EXCEL, Minitab, or graphing calculators.
  6. Describe data distribution through the study of sampling distributions; Distinguish the difference between sample and population distributions and analyze the role played by the Central Limit Theorem.
    1. Creating patterns through simulation
    2. The Central Limit Theorem for averages and (optional) sums
    3. The historical origins of the Central Limit Theorem in the early 19th century (optional)
    4. The Law of Large Numbers (optional)
    5. Statistical analysis using technology such as SPSS, EXCEL, Minitab, or graphing calculators.
  7. Estimate parameters by constructing point estimates and confidence intervals; Calculate probabilities using normal and t-distributions; Construct and interpret confidence intervals.
    1. Point estimation
    2. Confidence intervals for population means (population standard deviation known)
    3. The Student t distribution
      1. The historical origins of the Student-t distribution by William Gosset in the early 20th century, including its use in small sample sizes (optional)
      2. Population standard deviation not known
      3. General assumptions about the Student-t distribution
    4. Confidence intervals for population means (population standard deviation unknown)
    5. Confidence intervals for population proportions
    6. Statistical analysis using technology such as SPSS, EXCEL, Minitab, or graphing calculators.
  8. Compose probability statements about how confident one can be about making decisions based on data and construct the Type I and Type II error probabilities based on this decision; Determine and interpret levels of statistical significance including p-values; Interpret the output of a technology-based statistical analysis; Identify the basic concept of hypothesis testing including Type I and II errors; Formulate hypothesis tests involving samples from one and two populations; Select the appropriate technique for testing a hypothesis and interpret the result.
    1. The nature of hypothesis testing; Z-tests and t-tests for one population.
      1. Formulating the null and alternate hypotheses
      2. The p-value approach
      3. The decision approach given a fixed significance
      4. The four decisions
        1. The Type I error probability
        2. The Type II error/Power probability concept (calculations are optional)
        3. Determining when statistical significance really matters
    2. The Chi Square Distribution
      1. Developing and checking distributional assumptions
      2. Major uses
      3. Contingency Tables: independence and homogeneity
      4. At least one of the following:
        1. Goodness of Fit
        2. Single variance
    3. Testing Multiple Population Parameters
      1. Two sample means: matched pairs and independent groups
      2. At least one of the following:
        1. Two proportions
        2. Two variances and the F distribution
    4. Statistical analysis using technology such as SPSS, EXCEL, Minitab, or graphing calculators.
  9. Compose conjectures about bivariate and ANOVA theoretical models; Use linear regression analysis and ANOVA for estimation and inference, and interpret the associated statistics.
    1. Scatterplots
    2. Correlation
    3. Outliers and influential points
    4. Least squares regression
      1. Historical origins of the least squares method in the early 19th century (optional)
      2. Overview of method of least squares
    5. Prediction
      1. Meaning and interpretation
      2. Confidence intervals (optional)
      3. Inference (optional)
    6. Checking assumptions
    7. The One Way Analysis of Variance (ANOVA)
      1. Multiple means
      2. Checking assumptions
    8. Statistical analysis using technology such as SPSS, EXCEL, Minitab, or graphing

      calculators.
  10. Use appropriate statistical techniques to analyze and interpret applications based on data from disciplines including business, social sciences, psychology, life sciences, health sciences and education.
    1. Typical examples may include
      1. Decisions and Risk
        1. Shipping and quality control decisions based on probabilities
        2. Testing claimed percent of rape victims
        3. Testing claimed percent of female suicide victims
        4. Comparison of percents of various ethnic groups at community colleges
        5. Comparison of percents of various ethnic groups in foster care
        6. Comparing return on investment (ROI) on investment portfolios: maximizing expected returns, minimizing variance and volatility
        7. Analyzing overbooking in airline reservations
        8. Determining guarantee periods and analyzing the effect of changes in guarantee periods
        9. Determining which location of a firehouse to close
      2. Games of Chance
        1. Modeling games such as Vietnamese "Lucky Dice" using discrete distributions
        2. Using simulation as a tool to understand probabilities and analyze strategy in games such as the "Monty Hall" Problem
      3. Estimation and Inference
        1. Distribution of AIDS cases in Santa Clara county by ethnicity
        2. Distribution of percents of ethnic groups in San Francisco compared to observed percents
        3. Collecting data to use hypothesis testing to challenge established beliefs
        4. Analyzing medical treatments to compare effectiveness or safety of treatment vs placebo, or comparing more than one treatment
        5. Analyzing the effectiveness of welfare or other social programs
      4. Descriptive Statistics:
        1. Heights and weights of male and female athletes
        2. AIDS factors and drug use comparisons for males and females
        3. Comparisons of percentage of persons below the poverty level for males and females
        4. Racial profiling (test of independence)
        5. Ethnic and gender distribution of °®¶¹´«Ã½ students
        6. Language spoken at home by Santa Clara County, CA and the U. S.
        7. Discrimination in mortgage lending (DASL)
        8. Literacy rates by gender, nation, and/or ethnicity
        9. Percent of people who smoke by educational status
        10. Demographic statistics such as life expectancy, teenage birth rates, poverty rates, attained educational level, unemployment, income, etc, internationally, nationally, or regionally, by gender, age, ethnicity, or geographic region
    2. Use statistical knowledge to recognize and discuss provocative inferences and conclusions reported by the media, especially in regards to controversial current events issues, e.g. presidential and political elections, educational reform and trends, nutritional claims, and census sampling vs. counting
    3. Recognize some contemporary contributors to the field of statistics

      (optional - see web site http://faculty.deanza.fhda.edu/mathiosdiane/ for references)
    4. Statistical analysis using technology such as SPSS, EXCEL, Minitab, or graphing calculators.
  11. Explore in depth advanced topics of probability and statistics through problem-solving and/or projects.
    1. Typical problem solving topics may include any of the following:
      1. Solving discrete probability problems more advanced than those described or required in Section E1 using combinatoric methods.
      2. Finding probabilities and moments of continuous random variables using calculus.
    2. Typical project topics may include any of the following:
      1. Modeling by simulation discrete and continuous random variables.
      2. Statistical inference using advanced linear models such as multiple regression or multivariate analysis.
      3. Statistical inference using non-parametric methods such as the Wilcoxen or the Mann-Whitney tests.
      4. Conduct hypothesis tests using Bayesian methods as an alternative to the traditional Frequentist methods.
      5. Explore multiple comparisons in one factor, two factor and repeated measures ANOVA models using Tukey and/or Bonferroni Tests.
      6. Modeling Odds-Ratio and Logistic Regression using the Chi-square distribution.
      7. Conduct in-depth data analysis by merging multiple viewpoints of descriptive statistics with a variety of tests of statistical inference.
      8. Conduct data analysis and research across the curriculum in other disciplines.
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