Course Outline for Statistics C1000
Introduction to Statistics

Effective: Fall 2025
SLO Rev:

Catalog Description:

STAT C1000 - Introduction to Statistics

4.00 Units

This course is an introduction to statistical thinking and processes, including methods and concepts for discovery and decision-making using data. Topics include descriptive statistics; probability and sampling distributions; statistical inference; correlation and linear regression; analysis of variance, chi-squared, and t-tests; and application of technology for statistical analysis including the interpretation of the relevance of the statistical findings. Students apply methods and processes to applications using data from a broad range of disciplines. Descriptive statistics, including measures of central tendency and dispersion; elements of probability; tests of statistical hypotheses (one and two populations); correlation and regression; ANOVA; applications in various fields. Introduction to the use of computer software package to complete both descriptive and inferential statistics problems. Formerly MTH 43. May not receive credit if Mathematics 35 has been completed.
Placement as determined by the college’s multiple measures assessment process or completion of a course taught at or above the level of intermediate algebra. Strongly Recommended: ENGL C1000 (Formerly ENGL 1), MTH 53 or MTH 55.
CB03: TOP Code 1701.00 - Mathematics, General
CIP Code 27.0101 - Mathematics, General.
Course Grading: Letter Grade Only
Type Units Inside of Class Hours Outside of Class Hours Total Student Learning Hours
Lecture 4.00 72.00 144.00 216.00
Laboratory 0.00 18.00 0.00 18.00
Total 4.00 90.00 144.00 234.00

Measurable Objectives:

Upon completion of this course, the student should be able to:
  1. assess how data were collected and recognize how data collection affects what conclusions can be drawn from the data;
  2. identify appropriate graphs and summary statistics for variables and relationships between them and correctly interpret information from graphs and summary statistics;
  3. describe and apply probability concepts and distributions;
  4. demonstrate an understanding of, and ability to use, basic ideas of statistical processes, including hypothesis tests and confidence interval estimation;
  5. identify appropriate statistical techniques and use technology-based statistical analysis to describe, interpret, and communicate results;
  6. evaluate ethical issues in statistical practice;

    EXPANDED COURSE OBJECTIVES:

  7. distinguish among different scales of measurement and their implications;
  8. interpret data displayed in tables and graphically;
  9. apply concepts of sample space and probability;
  10. calculate the mean, median, mode, variance and standard deviation for a given data set;
  11. identify the standard methods of obtaining data and identify advantages and disadvantages of each;
  12. identify the sample(s) and population(s) in a data set description;
  13. describe the basic principles of experimental design;
  14. calculate probabilities of various independent or dependent events;
  15. calculate the mean and variance of a discrete distribution;
  16. calculate probabilities using normal and t-distributions;
  17. describe the nature of the binomial distribution and normal distribution, as well as properties of the normal probability curve;
  18. distinguish the difference between sample and population distributions and analyze the role played by the Central Limit Theorem;
  19. construct and interpret confidence intervals;
  20. determine and interpret levels of statistical significance including p-values;
  21. interpret the output of a technology-based statistical analysis;
  22. identify the basic concept of hypothesis testing including Type I and II errors;
  23. formulate hypothesis test involving samples from one and two populations;
  24. select the appropriate technique for testing a hypothesis and interpret the result;
  25. use linear regression and ANOVA analysis for estimation and inference, and interpret the associated statistics;
  26. use appropriate statistical techniques to analyze and interpret applications based on data from disciplines including business, social sciences, psychology, life science, health science, physical science, engineering and education.

Course Content:

Part 1: Required Topics for Common Course Numbering

  1. Introduction to statistical thinking and processes

  2. Technology-based statistical analysis

  3. Applications using data from four or more of the following disciplines: administration of justice, business, economics, education, health science, information technology, life science, physical science, political science, psychology, and social science

  4. Units (subjects/cases) and variables in a data set, including multivariable data sets

  5. Categorical and quantitative variables

  6. Sampling methods, concerns, and limitations, including bias and random variability 

  7. Observational studies and experiments

  8. Data summaries, visualizations, and descriptive statistics

  9. Probability concepts

  10. Probability distributions (e.g., binomial, normal)

  11. Sampling distributions and the Central Limit Theorem

  12. Estimation and confidence intervals

  13. Hypothesis testing, including t-tests for one and two populations, Chi-squared test(s), ANOVA; and interpretations of results

  14. Regression, including correlation and linear regression equations

 

Part 2: Expanded or Additional Topics at Chabot College

  1. Introduction to statistical thinking and processes

  2. Technology-based statistical analysis

  3. Applications using data from four or more of the following disciplines: administration of justice, business, economics, education, health science, information technology, life science, physical science, political science, psychology, and social science

  4. Units (subjects/cases) and variables in a data set, including multivariable data sets

  5. Categorical and quantitative variables

    1. Levels/scales of measurement

    2. Discrete vs continuous variable

  6. Sampling methods, concerns, and limitations, including bias and random variability 

  7. Observational studies and experiments

    1. Association vs causation

    2. Elements of an experiment

  8. Data summaries, visualizations, and descriptive statistics

    1. Sample vs population data

    2. Numerical summaries

      1. Measures of central tendency

        1. Mean

        2. Median

      2. Measures of dispersion

        1. Range

        2. Standard deviation

        3. Interquartile range

      3. Measures of location

        1. Five-number summaries

        2. Percentiles

      4. Frequency and relative frequency distributions

      5. Two-way tables

    3. Graphs

      1. Frequency and relative frequency histograms

      2. Boxplots

      3. Scatterplots

      4. Shape and mode

    4. Empirical rule

  9. Probability concepts

    1. Events and sample spaces

    2. Probability laws

    3. Independent and dependent events

    4. Random variables

      1. Expected value

      2. Variance and standard deviation

  10. Probability distributions (e.g., binomial, normal)

    1. Uniform

    2. Binomial

    3. Normal

    4. Student t

    5. Chi-square

  11. Sampling distributions and the Central Limit Theorem

  12. Estimation and confidence intervals

    1. One proportion z-interval

    2. One mean t-interval

  13. Hypothesis testing, including t-tests for one and two populations, Chi-squared test(s), ANOVA; and interpretations of results

    1. Type I and II errors

    2. Statistical vs practical significance

    3. One population proportion z-test

    4. One population mean t-test

    5. Two population difference of mean t-test

    6. Mean of difference paired t-test

    7. Chi-square tests

    8. Analysis of variance (ANOVA)

  14. Regression, including correlation and linear regression equations

    1. Correlation

    2. Coefficient of determination

    3. Least squares regression line

Methods of Instruction:

  1. Lecture/Discussion
  2. Class and group discussions
  3. Written assignments
  4. Group Activities
  5. Laboratory exercises
  6. Presentation of audio-visual materials
  7. Computer-based interactive curriculum
  8. Simulations
  9. Online Assignments
  10. Group Presentations
  11. Distance Education
  12. Problem solving
  13. Student participation
  14. Videos

Assignments and Methods of Evaluating Student Progress:

1. Typical Assignments
  1. Determine the range and sample standard deviation of the tornado occurrence data in Exercise 3.43. Discuss one major drawback to the standard deviation as a measure of variation.
  2. Enter the data on test scores into a statistical analysis package. Create relevant summary statistics, histogram, and boxplot of the data. Write a brief analysis of the data based on these graphical and numerical summaries.
  3. Using the data provided, compare the rates of depression between those firefighters who participated in 9/11 rescue and those who did not. Is the difference statistically significant? Include in your response any output you obtain from technology.
2. Methods of Evaluating Student Progress
  1. Quizzes
  2. Homework
  3. Midterm Examination
  4. Final Examination
  5. Projects
  6. Practical Examination
  7. Lab Activities
3. Student Learning Outcomes
Upon the completion of this course, the student should be able to:
  1. critically analyze mathematical problems critically using a logical methodology;
  2. communicate mathematical ideas, understand definitions, and interpret concepts;
  3. increase confidence in understanding mathematical concepts, communicating ideas and thinking analytically.

Textbooks (Typical):

  1. Illowsky, B., S. Dean (2024). Introductory Statistics (2e/e). OpenStax https://openstax.org/details/books/introductory-statistics-2e.
  2. Open Learning Initiative (2024). Probability & Statistics v5.0 Open Learning Initiative https://oli.cmu.edu/jcourse/webui/guest/join.do?section=probstat.
  3. Diez, D., Barr, C., Cetinkay-Rundel, M. (2020). Introductory Statistics with Randomization and Simulation OpenIntro https://www.openintro.org/book/isrs/.
  1. Moore, D., W. Notz, M. Fligner (2021). The Basic Practice of Statistics (9th). Macmillan.
  2. Lock, R., P. Lock, K. Morgan, E. Lock, D. Lock (2021). Statistics: Unlocking the Power of Data (3rd). Wiley.
  3. Tintle, N., Chance, B., Cobb, G.,Rossman, A., Roy, S., Swanson, T., Vanderstoep, J. (2020). Introduction to Statistical Investigations (2e). Wiley.
  4. CourseKata (2023). Introductory Statistics with R: A Modeling Approach National Center for Civic Innovations. Inc..
  1. Common Online Data Analysis Platform. The Concord Consortium, (/e).
  2. Google Sheets. Google , (/e).
  3. Google Colaboratory. Google, (/e).
  4. StatCrunch. Pearson, (/e).
Additional Materials:
  • Statistical software.
  • Graphing statistical calculator may be required.

Abbreviated Class Schedule Description:

This course is an introduction to statistical thinking and processes, including methods and concepts for discovery and decision-making using data. Topics include descriptive statistics; probability and sampling distributions; statistical inference; correlation and linear regression; analysis of variance, chi-squared, and t-tests; and application of technology for statistical analysis including the interpretation of the relevance of the statistical findings. Students apply methods and processes to applications using data from a broad range of disciplines. Formerly MTH 43.
Placement as determined by the college’s multiple measures assessment process or completion of a course taught at or above the level of intermediate algebra. Strongly Recommended: ENGL C1000 (Formerly ENGL 1), MTH 53 or MTH 55.

Discipline:
Mathematics*