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2019-20

Web Schedule Fall 2019


Revision Date: 24-Jul-19

MAT-2021-VO01X - Statistics


Synonym: 189651
Location: Online
Credits: 3 (45 hours)
Accelerated Section: This course has special meeting dates and times. See comments below or consult VSC Web Services - Search for Sections in the VSC portal for specific dates and times. If you have any questions call the site office offering the course.
Day/Times: Meets online
Semester Dates: 09-03-2019 to 10-21-2019
Last day to drop without a grade: 09-12-2019 - Refund Policy
Last day to withdraw (W grade): 10-01-2019 - Refund Policy
Faculty: Daniel Lemay | View Faculty Credentials
This course has started, please contact the offering academic center about registration
This section meets the following General Education Requirement(s):
Mathematics
    Note
  1. Many degree programs have specific general education recommendations. In order to avoid taking unnecessary classes, please consult with additional resources like your program evaluation, your academic program page, and your academic advisor.
  2. Courses may only be used to meet one General Education Requirement.

Browse the Canvas Site for this class.

Course Description:

An introduction to the basic ideas and techniques of probability and statistics. Topics may include numerical and graphical descriptive measures, probability, random variables, the normal distribution, sampling theory, estimation, hypothesis testing, correlation and regression. The use of technology may be required. Students must take a math assessment for placement purposes prior to registration. Prerequisite: Foundations of Algebra or equivalent skills.

Essential Objectives:

1. Outline the general development of statistical science, and list a number of common applications of statistical methodology.
2. Distinguish between descriptive and inferential statistics.
3. Create and apply various techniques used to describe data, such as pie charts, bar graphs, frequency tables, and histograms.
4. Define three common measures of central tendency (mean, median, and mode), and demonstrate the ability to calculate each manually from a series of small data sets.
5. Describe common methods of measuring variability, including range, percentiles, variance, and standard deviation, and calculate each from a series of small data sets.
6 Explain the Normal Probability Distribution, techniques of sampling, the Central Limit Theorem, and the concept of standard error, and compute probabilities associated with normally distributed samples.
7. Test hypotheses about the value of the mean assuming the normal distribution and large sample results.
8. Select and perform common statistical tests including one- and two-tailed tests.
9. Define linear regression and correlation and discuss their applications
10. Interpret and evaluate the validity of statistical data and reports.
11. Demonstrate proficiency in understanding, interpreting, evaluating and applying quantitative data and information.

Methods:

For each of the seven weeks that this course is in session, I have identified at least one of the course Essential objects.

For each of the essential objectives I have identified specific skills and written them as learning targets.

For each of the learning targets, you will have a set of videos that you may watch along with some paper based exercises that you may complete. You don't have to submit these exercises. They are for your edification, to enhance your learning. I do have may work to these exercises as a PDF or as a video.

Required:

  • You do need to complete the MyOpenMath online exercises that I have identified as key

  • At the start of the course, you will have collected/created a set of data. For each learning target, or combination of learning targets, you will have to make a post in a discussion forum demonstrating how you are able to show proficiency with each of the learning targets using your collected data.

  • There will be a series of short, one exercise quizzes. For each of the quizzes, they are graded pass/fail. You can take them as many times as you need to.

  • Additionally there will be an exam at the 4 week mark and at the end of the course. These exams will be cumulative.

  • Being an online course that is open 24 hrs per day and being a condensed course, you expected to complete work/participate at least 5 of the 7 days each week. Spending 20 hours each week working on this material would not be considered an extreme amount.

I have Office Hours throughout the week where you can schedule one on one live online help time with me.

Evaluation Criteria:

All of the MOM Exercise sets count for 20% of your final grade. Try every exercise, earn at least an 90%, and it gets counted as full credit. Make sure to read this <link> about MyOpenMath.

Proficiency quizzes count for 30% of your final grade

Discussion forum posts, the ones where you post your work, count for 20% of your final grade.

The two summative exams count for 30% of your grade.

Grading Criteria:

A+ 98 - 100

B+ 88 - 89

C+ 78 - 79

D+ 68 - 69

S (70% or greater) Satisfactory

A 93 - 97

B 83 - 87

C 73 - 77

D 63 - 67

N (Less than 70%)Unsatisfactory

A- 90 - 92

B- 80 - 82

C- 70 - 72

D- 60 - 62

Textbooks:

Fall 2019 textbook data will be available on May 13. On that date a link will be available below that will take you to eCampus, CCV's bookstore. The information provided there will be for this course only. Please see this page for more information regarding the purchase of textbooks.

MAT-2021-VO01X Textbooks.

The last day to use a Financial Aid advance to purchase textbooks is the 3rd Tuesday of the semester. See your financial aid counselor at your academic center if you have any questions.

Contact Faculty:

Email: Daniel Lemay
Hiring Coordinator for this course: Laura Rubenis

Syllabus:

Course Outline

Week 1

  • I can distinguish between a quantitative variable and a categorical variable.

  • I can work with percentages.

  • I can identify and conduct convenience samples, random samples, systematic samples, cluster samples,stratified random samples.

  • I can identify retrospective/prospective observational studies.

  • I can distinguish between a controlled experiment and an observational study.

  • I can construct a dotplot by hand.

  • I can create stem and leaf plots along with back to back stem plots to compare distributions.

Assessments:

  1. Spreadsheet of data

  2. Find a study (part 1)

  3. MOM Exercises

  4. Your Post about the collected data

  5. Proficiency Quizzes in Canvas


Week 2

  • I can sort data into a frequency/relative frequency table and construct a histogram.

  • I can find a five number summary for a set of data.

  • I can calculate the mean and standard deviation for a set of data

  • I can describe a distribution by shape, center and spread.

  • I can identify the shape of the distribution.

  • I can use a spreadsheet to find summary statistics and make graphs of distributions.

  • I can use a spreadsheet to find summary statistics and make graphs of distributions.

  • I can find a weighted mean

Assessments

  1. MOM Exercises

  2. Proficiency Quizzes in Canvas

  3. Forum Posts about your spreadsheet calculations


Week 3

  • I can distinguish between empirical vs theoretical probabilities.

  • I can calculate probabilities applying the addition and multiplication rules

  • I can calculate marginal and conditional probabilities from a table.

  • I can find probabilities associated with Bernoulli Experiments and the Binomial distribution using technology.

  • I can use the 68-95-99.7 rule to describe Normal Distributions

  • I can compare different Normal Distributions using z scores

  • I can find and use probabilities associated with Normal Distributions.

  • I can use an applet to find probabilities associated with a Normal Distribution.

Assessments

  1. MOM Exercises

  2. Proficiency Quizzes in Canvas

  3. Forum Posts about your spreadsheet calculations


Week 4

  • I can describe a sampling distribution.

  • I can apply the Central Limit Theorem.

  • I can apply the Normal Approximation of a Binomial distribution

  • I can create and interpret a confidence interval for one and two sample situations.

Assessments

  1. Cumulative Exam on Weeks 1 - 3 skills/Ideas. Do this first

  2. MOM Exercises

  3. Proficiency Quizzes in Canvas

  4. Forum Posts about your data


Week 5

  • I can conduct tests of hypotheses for one and two sample situations.

  • I can test to see if three or more samples have the same mean (One-Way ANOVA)

Assessments

  1. MOM Exercises

  2. Proficiency Quizzes in Canvas

  3. Forum Posts about your data


Week 6

  • I can identify when a distribution of a categorical variable follows a known pattern (chi-square Goodness of Fit test)

  • I can identify when two categorical variables are strongly associated by conducting a chi-square test for independence.

Assessments

  1. MOM Exercises

  2. Proficiency Quizzes in Canvas

  3. Forum Posts about your data


Week 7

  • I can identify when two quantitative variables are strongly associated by examining the correlation coefficient.

  • I can estimate a linear equation to predict a data set.

  • I can use spreadsheets to analyze bivariate data.

  • I can identify lurking variables and explain the difference between causation and correlation.

Assessments

  1. MOM Exercises

  2. Proficiency Quizzes in Canvas

  3. Forum Posts about your data

  4. Final Exam Directly covers material from weeks 4 - 7.

Please note: In order to receive accommodations for disabilities in this course, students must make an appointment to see the Americans with Disabilities Coordinator in their site and bring documentation with them.

Academic Honesty: CCV has a commitment to honesty and excellence in academic work and expects the same from all students. Academic dishonesty, or cheating, can occur whenever you present -as your own work- something that you did not do. You can also be guilty of cheating if you help someone else cheat. Being unaware of what constitutes academic dishonesty (such as knowing what plagiarism is) does not absolve a student of the responsibility to be honest in his/her academic work. Academic dishonesty is taken very seriously and may lead to dismissal from the College.

Course description details subject to change. Please refer to this document frequently.

To check on space availability, choose Search for Classes.


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