MS 150 Statistics Calendar and Syllabus • College of Micronesia-FSM • Instructor: Dana Lee Ling
Wk Day Name Date Topic Events
0 Friday1/13/6   Prequiz
1 Monday1/16/6 1.1 Variables and levels of measurement
1.2 Random samples
Friday1/20/6 1.3 Experimental design Quiz 1
Class lists due
2 Monday1/23/6 2.1 Bar and circle graphs using Excel
Wednesday1/25/6 2.2 Intro to freq distributions: bins
Friday1/27/6 2.2 Frequency distributions Quiz 2 2.2
3 Monday1/30/6 3.1 Mode, Median, Mean Grad apps due
Wednesday2/1/6 3.2 Range and standard deviation
4 Monday2/6/6 Review test. Handed out answer sheet. [Covered 4.2, skipping correlation material in 4.1]
Wednesday2/8/6 4.1 Intro to paired data [Covered 4.1 and 4.3]
Friday2/10/6 Quiz 3 4.2ans
5 Monday2/13/6 5.1 Probability: Intuition and equally likely outcomes: coins and dice Early warning
Wednesday2/15/6 5.1 Equally likely outcomes: Sample space, complement, probability versus statistics
Friday2/17/6 Quiz 4ans
6 Monday2/20/6 6.1 Discrete versus continuous variables
Wednesday2/22/6 6.1b Probability distributions: mean from distribution, x*p(x)
Friday2/24/6 Staff development day
7 Monday2/27/6 Supplemental material from Triola: relative standing, z-scores. Ordinary and unusual values.
Wednesday3/1/6 z-score homework, midterm questions answered.
8 Monday3/6/6 Review midterm
Wednesday3/8/6 7.1 Normal distributions
Introducing the shape we normally get: Pennies.
HW: Mean ” from penny distribution.
Friday3/10/6 Review penny distribution. • Middefs due Quiz 5
Wk Day Name Date Topic Events
9 Monday3/13/6 7.2 Standard units and areas under normal curve
Wednesday3/15/6 7.3 Area for any x under normal curve
Friday3/17/6 Quiz 6
10 Monday3/20/6
Wednesday3/22/6 Ten leaf measure HW
8.1 Sampling Distributions. Review terms.
Friday3/24/6 Quiz 7. LDWWW
11 Monday3/27/6 8.1 homework
Wednesday3/29/6 8.3
Friday3/31/6 Culture day
12 Monday4/3/6 9.1 Estimating ” with Large Samples Course select
Wednesday4/5/6 9.2 Estimating ” small sample
Friday4/7/6 Test Two
13 Monday4/10/6 Review Test Two. 9 - 10 Hypothesis testing via confidence intervals
Wednesday4/12/6 Easter recess
Friday4/14/6 Easter recess
14 Monday4/17/6 Confidence interval hypothesis testing
Wednesday4/19/6 10.1 Introduction to Hypothesis Testing
Friday4/21/6 Quiz 8: canceled due to Ashby funeral
15 Monday4/24/6 10.2, 4 Hypothesis tesing using t-distribution for ” provided n ≥ 5
Wednesday4/26/6 10.3 p values
Friday4/28/6 Quiz 8: Chap 9 & 10
16 Monday5/1/6 11.1 Test involving paired differences: barefoot day II. Actual: Quiz, Duke I
Wednesday5/3/6 11.2 Inferences about the difference of two means. Nixed. Did bodyfat, duke II
Friday5/5/6 Duke analysis plus body fat analysis on quiz Quiz 9
17 Monday5/8/6 11.3 Inferences about the difference of two means
Wednesday5/10/6 FSM Constitution Day
Friday5/12/6 Last day of instruction. Question & Answer
18 Tuesday5/16/6 M10 Final at 10:05 faraway rainbowanswers
Wednesday5/17/6 M09 Final at 8:00
• Required Textbook:
Understanding Basic Statistics 3rd, Brase and Brase.
• Statistics office hours: 1:00 P.M. to 3:30 P.M. Tuesday, Thursday, or by appointment, or walk-in any time.
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 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 rČ. (Calculate)

Course Intentions

• Use of Microsoft Excel and Microsoft Excel functions (or spreadsheet software with equivalent statistical functions) throughout the course as opposed to using dedicated statistics software package. Excel and other 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.