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Welcome to MM207!

Welcome to MM207!. To resize your pods: Place your mouse here. Left mouse click and hold. Drag to the right to enlarge the pod. Unit 1 Seminar: Prof. Dan Thursday 8:00 pm ET. Seminar Outline. INSTRUCTOR AND SEMINAR INFORMATION                                  

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Welcome to MM207!

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  1. Welcome to MM207! To resize your pods: Place your mouse here. Left mouse click and hold. Drag to the right to enlarge the pod. Unit 1 Seminar: Prof. Dan Thursday 8:00 pm ET

  2. Seminar Outline INSTRUCTOR AND SEMINAR INFORMATION                                   • Instructor Name and Credentials: Dan Watson, M.A. Mathematics • Kaplan Email Address:    dwatson2@kaplan.edu • AIM Instant Messenger Name:   professordan79 • AIM Office Hours (EST):   By appointment.  Email me at the above Email address to set up an appointment. • Course Seminar Day and Time (EST): • Prof. Stefanie Reay– Wednesday 10:00 pm ET • Prof. Jamalee Stone – Thursday 9:00 pm ET • Prof. Tatiana Scott– Saturday 12:00 pm ET • Prof. Dan Watson – Monday 8:00 pm ET

  3. Grading Criteria/Course Evaluation All assignments submitted on time will be graded within five days of their due date (the Sunday of the following unit). Late work will be graded within five days of the submission date.

  4. Late Assignments • All unit assignments (projects, quizzes, discussion, seminar, etc.) are due Tuesday by 11:59 pm ET of the unit assigned.  • Late projects will be deducted ½ point per day the assignment is late • Late discussion posts to classmates may not receive credit as their purpose is to further the discussion and the discussion cannot be furthered after it has ended.

  5. Extenuating Circumstances • If you have extenuating circumstances that prevent you from completing projects, quizzes, seminars or participating in the class, please contact the professor to discuss alternative arrangements. • Prior notification does not automatically result in a waiver of the late penalties.  Examples of extenuating circumstances may include but are not limited to:  personal/family member hospitalization, death in the family, weather/environmental evacuation due to fire/hurricane, or active military assignment where internet connectivity is unavailable for a limited time period.  • General computer-related or internet connectivity issues are not considered extenuating circumstances. It is your responsibility to locate a reliable Internet connection and computer. They are available at most public libraries as well as locations such as FedEx Kinkos.

  6. Kaplan University Math Center • Tutoring and many other resources are available in the Kaplan University Math Center which you can access on the top left under My Studies on your KU Campus page.  You can chat with a live tutor during live tutoring hours. You can also submit a math problem and receive comments specific to that problem.  • Students may submit their projects to the Math Center for review. Tutors will not grade or correct the project, but they will provide guidance for improvement.  Students should submit assignments early enough to receive feedback and make corrections before the project due date (24 hour turn-around times Monday-Thursday and 48 hour turn-around times on weekends are typical).  • Email projects to: kumc@kaplan.edu.  Please put “project review” in the subject line of the message and please include your last name in the filename for the document. • Each project can only be reviewed 1 time.

  7. Data • Definition – information that is collected from making observations, counts, measurements, or surveys. Two types of data sets: • Sample – a subset of the population/the group of people you are surveying (ex. MM207-03) • Population – the collection of people you are drawing the sample from (ex. Kaplan Students)

  8. Statistics the science of collecting, organizing, analyzing, and interpreting data in order to make decisions • Descriptive – organizing, summarizing, and displaying data of a sample or population (data of sample or population is reported) • Inferential – uses a sample to draw conclusions about a population (an assumption about the population is made based on the sample; ‘based on a random sample’) • Ex: Of 350 randomly selected people in the town of Luserna, Italy, 280 people had the last name Nicolussi. • 80% of these people have the last name Nicolussi • 80% of all people living in Italy have the last name Nicolussi

  9. Types of Data • Quantitative dataQuantitative data has a value or a numerical measurement for which you can calculate sums, products and other numerical calculations. (ex: height, weight, etc) • Qualitative dataQualitative data is grouped into a category or group. Sums, products or other numerical calculations do not mean anything. (ex: hair color, social security number)

  10. Levels of Data Measurement • Nominal: Data is put in categories (ex: majors) • Ordinal: Data is put in ordered categories (ex: poor, fair, good, best) • Interval: Data can be ordered and calculations made (ex: temperature – degrees can be negative) • Ratio: Data can be ordered, calculations made, and it has an absolute zero point (ex: height – a person can not have a negative height)

  11. Sampling • Simple Random Sample – every sample of the same size has the same chance of being selected (usually performed by computers) • Systematic – every nth member of the population is selected • Cluster – everyone from each randomly selected groups (ex: five states are randomly selected and every person in that state is surveyed) • Stratified – a random selection from each (ex: five people are randomly selected from every state) • Convenience – available members of the population

  12. Organizing Data: Graphs and Tables • Common GraphsHistogram Circle graphs (Pie chart)Bar graphs Time series graphPareto chart Stem-and-leaf plot • Excel templates for creating Histograms and Pie Char https://docs.google.com/leaf?id=0B0ucIB81tO7DYjg0YjQzNDAtMDA2Yy00YzMzLWE1NzAtMTdlMjQ3OTJkY2Nm&sort=name&layout=list&num=50 • Additional resources available in the classroom by clicking on: Unit 11 -> Web Resources Analysis Toolpak Histogram Add-in

  13. Creating a Histogram in Excel

  14. Creating a Histogram in Excel Enter in Classes and Frequency in Columns A and B (if Excel auto-formats the class into a date, click on ‘Format’ -> ‘Cells’ -> ‘Text’)

  15. Creating a Histogram in Excel Click on ‘Insert’ -> ‘Chart’ -> ‘Column’ Highlight the Classes and Frequency and select appropriate histogram

  16. Creating a Histogram in Excel To make the bars touch each other, right click on one of the bars and select ‘Format Data Series’

  17. Creating a Histogram in Excel Click on ‘Option’ -> set Gap Width to 0 and click on OK

  18. Creating a Histogram in Excel Finished Product! -> Copy and Paste Histogram into your Word Document

  19. Creating a Pie Chart in Excel Creating a Pie Chart is the same procedure Click on ‘Insert’ -> ‘Chart’ -> ‘Pie’

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