College of Micronesia-FSM

MS 150 Statistics

With usage of general objectives and specific objectives

Course Description: A semester course designed as an introduction to the basic ideas of data presentation, descriptive statistics, and inferential statistics including confidence intervals and hypothesis testing. The course incorporates the use of a computer spreadsheet package, Microsoft Excel, for both data analysis and presentation. Basic concepts are studied using applications from business, social science, health science, and the natural sciences.

Hours per week: 3
Number of weeks: 16
Semester credits: 3
Prerequisite course: MS 100 College Algebra

The order of the sections is not intended to reflect the chronologic order of topics.

  1. General Objectives
    Students will be able to:
    1. Calculate basic statistics
    2. Represent data sets using histograms
    3. Solve problems using normal curve and t-statistic distributions including confidence intervals for means and hypothesis testing
    4. Determine and interpret p-values
    5. Perform a linear regression and make inferences based on the results
  2. Specific objectives
    Students will be able to:
    1. Calculate basic statistics
      1. Distinguish between a population and a sample
      2. Distinguish between a statistic and a parameter
      3. Identify different levels of measurement when presented with nominal, ordinal, interval, and ratio data.
      4. Determine a sample size
      5. Determine a sample minimum
      6. Determine a sample maximum
      7. Calculate a sample range
      8. Determine a sample mode
      9. Determine a sample median
      10. Calculate a sample mean
      11. Calculate a sample standard deviation
      12. Calculate a sample coefficient of variation
    2. Represent data sets using histograms
      1. Calculate a class width given a number of desired classes
      2. Determine class upper limits based on the sample minimum and class width
      3. Calculate the frequencies
      4. Calculate the relative frequencies (probabilities)
      5. Create a frequency histogram based on calculated class widths and frequencies
      6. Create a relative frequency histogram based on calculated class widths and frequencies
      7. Identify the shape of a distribution as being symmetrical, uniform, bimodal, skewed right, skewed left, or normally symmetric.
      8. Estimate a mean from class upper limits and relative frequencies using the formula Sx*P(x) here the probability P(x) is the relative frequency.
    3. Solve problems using normal curve and t-statistic distributions including confidence intervals for means and hypothesis testing
      1. Discover the normal curve through a course-wide effort involving tossing seven pennies and generating a histogram from the in-class experiment.
      2. Identify by characteristics normal curves from a set of normal and non-normal graphs of lines.
      3. Determine a point estimate for the population mean based on the sample mean
      4. Calculate a z-critical value from a confidence level
      5. Calculate a t-critical value from a confidence level and the sample size
      6. Calculate an error tolerance from a t-critical, a sample standard deviation, and a sample size.
      7. Solve for a confidence interval based on a confidence level, the associated z-critical, a sample standard deviation, and a sample size where the sample size is equal or greater than 30.
      8. Solve for a confidence interval based on a confidence level, the associated t-critical, a sample standard deviation, and a sample size where the sample size is less than 30.
      9. Use a confidence interval to determine if the mean of a new sample places the new data within the confidence interval or is statistically significantly different.
    4. Determine and interpret p-values
      1. Calculate the two-tailed p-value using a sample mean, sample standard deviation, sample size, and expected population mean to to generate a t-statistic.
      2. Infer from a p-value the largest confidence interval for which a change is not significant.
    5. Perform a linear regression and make inferences based on the results
      1. Identify the sign of a least squares line: positive, negative, or zero.
      2. Calculate the slope of the least squares line.
      3. Calculate the intercept of the least squares line.
      4. Solve for a y value given an x value and the slope and intercept of a least squares line.
      5. Solve for a x value given an y value and the slope and intercept of a least squares line.
      6. Calculate the correlation coefficient r.
      7. Use a correlation coefficient r to render a judgment as to whether a correlation is perfect, high, moderate, low, or none.
      8. Calculate the coefficient of determination r˛.

Course Intentions

Assessment

Assessment will be via quizzes, tests, midterm examinations and a final examination. All core outcomes will appear on the final examination.

  1. Textbook: Understanding Basic Statistics, Second Edition, Brase and Brase, Houghton Mifflin, 2001
  2. Required course materials: In-class access to a computer with Microsoft Excel.
  3. Reference materials
    1. Microsoft Excel spreadsheet mathematical software by Microsoft.
    2. "Data Analysis with Microsoft Excel", Berk and Carey, Duxbury Press, 1998
    3. "Statistics: A First Course", 6th ed. by Freund and Simon, Prentice Hall, Inc. 1995 (ISBN 0-13-083024-0),
    4. "Elementary Statistics." 6th ed. by Johnson, PVVS-KENT Pub., 1992 (ISBN 0-534-92980-X)
  4. Instructional costs: None anticipated at this time
  5. Methods of Instruction: The course will be taught by lecture, class discussion, and the use of Microsoft Excel for problem solving and computer simulations. This course will be taught in the Math/Science computer classroom. Also, students will be encouraged to utilize the computer labs outside of class for homework assignments.
  6. Evaluation: Homework, tests, quizzes, a midterm, and a final exam will be given. A standard 90%=A, 80%=B, 70%=C, 60%=D Below 60%=F grading scale is recommended pending the outcome of futher discussion on the overall meaning of grading in a student learning outcome centered environment.
  7. Credit by examination: None
  8. Attendance policy: As per the current college catalog

Appendix A

Further reading and background on the structure of this outline:
http://shark.comfsm.fm/~dleeling/department/slorevolution.html
http://shark.comfsm.fm/~dleeling/department/slorevolution_ii.html
http://shark.comfsm.fm/~dleeling/department/slorevolution_iii.html
http://shark.comfsm.fm/~dleeling/department/dnsm_program_outcomes.html
http://shark.comfsm.fm/~dleeling/department/slorevolution_v.html
http://shark.comfsm.fm/~dleeling/department/slorevolution_vi.html
http://shark.comfsm.fm/~dleeling/department/systemwidecompetencies.html