##### MS 150 Statistics Spring Calendar and Syllabus College of Micronesia-FSM Instructor: Dana Lee Ling
Day Name Date Topic Other
Monday 1/17/5 1.1 Variables and levels of measurement
Wednesday 1/19/5 Barefoot day: Determining your body fat
Friday 1/21/5 1.2 Random samples Quiz 1, End Add/Drop
Monday 1/24/5 1.3 Experimental design Class list due
Wednesday 1/26/5 2.1 Bar and circle graphs using Excel
Friday 1/28/5 2.2 Frequency distributions Quiz 2
Monday 1/31/5 3.1 Mode, Median, Mean
Wednesday 2/2/5 3.2 Range and standard deviation
Friday 2/4/5 Test One
Monday 2/7/5 Review test one. 4.1 Intro to paired data
Wednesday 2/9/5 4.2 Linear regression
Friday 2/11/5 4.3 Linear correlation coef Quiz 3
Monday 2/14/5   Early warning
Wednesday 2/16/5 5.1 Probability: Intuition and equally likely outcomes one die only Quiz 4
Friday 2/18/5 Staff development day
Monday 2/21/5 6.1: Using relative frequency to calculate a mean (mean from distribution)
Wednesday 2/23/5 Review mean from distribution HW. 6.1 Pennies in class: Making histogram is HW
Friday 2/25/5 6.1 Introducing the shape of tomorrow: what we normally get Quiz 5
Monday 2/28/5 6.1 Mean from distribution
Wednesday 3/2/5 6.1 StDev from distribution [Review for midterm?]
Friday 3/4/5 MidexTiresxlshtmlsxc
Monday 3/7/5 Review midterm
Wednesday 3/9/5 Penny probabilities(sxc)
Friday 3/11/5 7.1 Normal probability graphs Quiz 6 • Middefs due
Monday 3/14/5 7.1 Normal probability graphs [7.3 x to z to area]
Wednesday 3/16/5 7.2 Standardization
Friday 3/18/5 7.3 Areas under any normal curve Quiz 7
Monday 3/21/5 7.3 Areas under any normal curve
Wednesday 3/23/5 Easter Recess
Friday 3/25/5 Good Friday
Monday 3/28/5 8.1 Sampling Distributions
Wednesday 3/30/5 LDWWW
Thursday 3/31/5 Rahn en Tiahk
Friday 4/1/5 Founding Day
Monday 4/4/5 9.1 Estimating ” with Large Samples Course select
Wednesday 4/6/5 9.2 Estimating ” small sample
Friday 4/8/5 Quiz 8
Monday 4/11/5 Review Test Two. 9.4 Sample size
Wednesday 4/13/5 Worksheet: 9 - 10 Hypothesis testing via confidence intervals
Friday 4/15/5 Test Two Quiz 9
Monday 4/18/5 10.1 Hypothesis Testing
Wednesday 4/20/5 10.2 Large sample tests for ”
Friday 4/22/5 10.3 P values for large samples Quiz 10(sxc)
Monday 4/25/5 10.4 Small sample test for ”
Wednesday 4/27/5 P values for small samples using Excel
Friday 4/29/5 11.1 Inferences about paired diff Quiz 11(xls)
Monday 5/2/5 11.2 Inferences about two means (large samples)
Wednesday 5/4/5 11.3 Inferences about two means (small samples)
Friday 5/6/5 Prior review quizzes: s42q12q12axlss33r14s41r33s33r33s31r33
s23r33
Monday 5/9/5 Review quiz.
Tuesday 5/10/5 FSM Constitution Day
Thursday 5/12/5 Last day of instruction.
Monday 5/16/5 M10 Final at 10:05sxcoos
Tuesday 5/17/5 M09 Final at 8:00
• Required Textbook:
Understanding Basic Statistics 3rd, Brase and Brase.
• Statistics office hours: 1:00 P.M. to 3:00 P.M. Monday, Friday 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 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 Sx*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.