MS 150 Statistics Calendar and Syllabus • College of Micronesia-FSM • Instructor: Dana Lee Ling
Wk Day Name Date Topic Assess
0 Friday1/12/7 1.1 Overview 1.2 Types of Data 1.3 Critical Thinking • Notes
1 Monday1/15/7 1.4 Design of experiments 1.5 Introduction to [Calc]
Friday1/19/7   Quiz 1
2 Monday1/22/7 2.1 2.2 Frequency Distributions
Friday1/26/7   Quiz 2 2.3
3 Monday1/29/7 2.4 Measures of center
Wednesday1/31/7 2.5 Measures of variation • 2.6 Measures of relative standing: z only
4 Monday2/5/7 9.1 9.2 Correlation Ball drop?
Wednesday2/7/7 9.3 Regression
Friday2/9/7 Early warning Quiz 3
5 Monday2/12/7 3.1 3.2 Fundamentals of probability
Wednesday2/14/7 4.1 4.2 Random variablesMean from distribution
Friday2/16/7   Quiz 4 (3.2)(4.2)
6 Monday2/19/7 Pennies: The shape of randomness
Wednesday2/21/7 5.1 5.2 Standard Normal Distribution
Friday2/23/7 Quiz 5
7 Monday2/26/7 5.3 Applications of Normal Distributions
Wednesday2/28/7
8 Monday3/5/7 Review midterm
Wednesday3/7/7 5.4 Sampling Distributions and Estimators
Friday3/9/7 5.5 Central Limit Theorem • Middefs due
9 Monday3/12/7 6.1 6.2 Estimating a population proportion
Wednesday3/14/7 6.3 Estimating a Population mean: σ known
Friday3/16/7 Quiz 6
Wk Day Name Date Topic Assess
10 Monday3/19/7 6.4 Estimating a Population Mean: sx knownSample quiz
Wednesday3/21/7 Hypothesis testing using confidence intervals
Friday3/23/7 LDWWW Quiz 7
11 Monday3/26/7
Wednesday3/28/7 Test Twot2a
Friday3/30/7 Culture day (Observed)
12 Monday4/2/7Founding Day (Observed)
Wednesday4/4/7 Easter break
Friday4/6/7 Good Friday
13 Monday4/9/7 7.1 7.2 Hypothesis testingCourse selection
Wednesday4/11/7 7.3 Testing a claim about a proportion
Friday4/13/7Quiz 8
14 Monday4/16/7 7.4 Testing a Claim About a Mean: σ known
Wednesday4/18/7 7.5 Testing a Claim About a Mean: σ unknown, sx known
Friday4/20/7 Quiz 9
15 Monday4/23/7 8.1 8.2 Inferences from Two Proportions
Wednesday4/25/7 8.3 Inferences about Two Means: Independent Samples
Friday4/27/7 Quiz 10
16 Monday4/30/7 Barefoot day II
Wednesday5/2/7 8.4 Inferences from matched pairs.
Friday5/4/7 Quiz 11
17 Monday5/7/7 Last day of instruction. Question & Answer session
Wednesday5/9/7 M10 Final at 10:05 found dataodshtml
Friday5/11/7 M09 Final at 8:00 • faraway sakauodsxls

Do not alter the desktop settings, the screensaver, change color schemes, nor add nor delete panels to the computer desktop!

• Required Textbook:
Elementary Statistics Using Excel. Triola
• Statistics office hours: 1:00 P.M. to 3:30 P.M. Monday, Friday; 10:00 - 11:30 Tuesday, Thursday; or by appointment, walk-ins welcome!
Instructor: Dana Lee Ling.
Email: dleeling@comfsm.fm cc: dana@mail.fm
Web site: http://www.comfsm.fm/~dleeling/statistics/statistics.html
Work: 320-2480 extension 228 / Home phone: 320-2962.
• Attendance: Seven absences results in withdrawal from the course. A late is one third of an absence. Thus any combination of absences and lates that adds to seven will result in withdrawal. For example, twenty-one lates would result in withdrawal.
• No betelnut in class nor on campus except in the cultural huts.
No spitting over the balcony!
• Quizzes are given every Friday that there is not a test. Quizzes and tests can and do occur on a Wednesday wherein Friday is a holiday.
Homework is worth 1 to 3 points.
Quizzes are worth on the order of 5 to 10 points each. Tests are worth on the order of 20 points. The midterm is worth roughly 40 points. The final is worth up to roughly 60 points.
The term as a whole will generate some 200 plus points.
Grading is based on the standard College policy: Obtain 90% of the points or more to obtain an A, 80% to 89% for a B, and so forth.
Points map to student learning outcomes via questions on publicly published quizzes, tests, and examinations wherein each question can be linked back to a course or program learning outcome. For further information refer to the student learning outcomes nexus:
http://www.comfsm.fm/~dleeling/department/nexus.html
and the statistics outline:
http://www.comfsm.fm/~dleeling/department/ms150.html
• Academic Honesty Policy: Cheating on an assignment, quiz, test, midterm, or final will result in a score of zero for that assignment, quiz, or examination. Due to our cramped quarters, the course operates by necessity on a system of personal integrity and honor.
• Course student learning outcomes assessment: Based on item analysis of final examination aligned to the outline. During term student assessment occurs at the end of each week, see above.

Course Description: A semester course designed as an introduction to the basic ideas of data presentation, descriptive statistics, linear regression, and inferential statistics including confidence intervals and hypothesis testing. Basic concepts are studied using applications from education, business, social science, and the natural sciences. The course incorporates the use of a computer spreadsheet package for both data analysis and presentation. The course is intended to be taught in a computer laboratory environment.

1. General Objectives
Students will be able to:
1. Calculate basic statistics (define, calculate)
2. Represent data sets using histograms (define, calculate, estimate, represent)
3. Solve problems using normal curve and t-statistic distributions including confidence intervals for means and hypothesis testing (define, calculate, solve, interpret)
4. Determine and interpret p-values (calculate, interpret)
5. Perform a linear regression and make inferences based on the results (define, calculate, solve, interpret)
2. Specific Objectives
Students will be able to: Given one variable data and the use of a calculator or spreadsheet software on a computer
1. Calculate basic statistics
1. Distinguish between a population and a sample (define)
2. Distinguish between a statistic and a parameter (define)
3. Identify different levels of measurement when presented with nominal, ordinal, interval, and ratio data. (define)
4. Determine a sample size (calculate)
5. Determine a sample minimum (calculate)
6. Determine a sample maximum (calculate)
7. Calculate a sample range (calculate)
8. Determine a sample mode (calculate)
9. Determine a sample median (calculate)
10. Calculate a sample mean (calculate)
11. Calculate a sample standard deviation (calculate)
12. Calculate a sample coefficient of variation (calculate)
2. Represent data sets using histograms
1. Calculate a class width given a number of desired classes (calculate)
2. Determine class upper limits based on the sample minimum and class width (calculate)
3. Calculate the frequencies (calculate)
4. Calculate the relative frequencies (probabilities) (calculate)
5. Create a frequency histogram based on calculated class widths and frequencies (represent)
6. Create a relative frequency histogram based on calculated class widths and frequencies (represent)
7. Identify the shape of a distribution as being symmetrical, uniform, bimodal, skewed right, skewed left, or normally symmetric. (define)
8. Estimate a mean from class upper limits and relative frequencies using the formula ∑x*P(x) here the probability P(x) is the relative frequency. (estimate)
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. (develop)
2. Identify by characteristics normal curves from a set of normal and non-normal graphs of lines. (define)
3. Determine a point estimate for the population mean based on the sample mean (calculate)
4. Calculate a z-critical value from a confidence level (calculate)
5. Calculate a t-critical value from a confidence level and the sample size (calculate)
6. Calculate an error tolerance from a t-critical, a sample standard deviation, and a sample size. (calculate)
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. (solve)
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. (solve)
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. (interpret)
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. (calculate)
2. Infer from a p-value the largest confidence interval for which a change is not significant. (interpret)
5. Given two variable data and the use of spreadsheet software on a computer
6. Perform a linear regression and make inferences based on the results
1. Identify the sign of a least squares line: positive, negative, or zero. (Define)
2. Calculate the slope of the least squares line. (Calculate)
3. Calculate the intercept of the least squares line. (Calculate)
4. Solve for a y value given an x value and the slope and intercept of a least squares line. (Solve)
5. Solve for a x value given an y value and the slope and intercept of a least squares line. (Solve)
6. Calculate the correlation coefficient r. (Calculate)
7. Use a correlation coefficient r to render a judgment as to whether a correlation is perfect, high, moderate, low, or none. (Interpret)
8. Calculate the coefficient of determination rē. (Calculate)

Course Intentions

• Use of spreadsheets and spreadsheet functions throughout the course as opposed to using dedicated statistics software package. Spreadsheets will be the desktop software most widely available to MS 150 students both while taking the course and, more importantly, in the workplace post-graduation.
• Use of real-world data, examples, and problems to the extent appropriate and possible.