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JOHN CABOT UNIVERSITY
COURSE CODE: "MA 208"
COURSE NAME: "Statistics I"
SEMESTER & YEAR:
Summer Session II 2018
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SYLLABUS
INSTRUCTOR:
Paul Johnson
EMAIL: [email protected]
HOURS:
MTWTH 9:00-10:50 AM
TOTAL NO. OF CONTACT HOURS:
45
CREDITS:
PREREQUISITES:
Prerequisite: Placement into MA 197 or completion of MA 100 or MA 101 with a grade of C- or above
OFFICE HOURS:
MTWTH 11:00 AM to 12:30 PM
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COURSE DESCRIPTION:
An introduction to descriptive statistics, elementary probability theory and inferential statistics. Included are: mean, median, mode and standard deviation; probability distributions, binomial probabilities and the normal distribution; problems of estimation; hypothesis testing, and an introduction to simple linear regression.
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SUMMARY OF COURSE CONTENT:
This course is a self-contained introduction to probability and statistics. It is designed to equip students with a level of statistical proficiency that is an essential part of their critical thinking toolkit. The course presents graphical and numerical techniques for representing data before studying the probability theory needed to conduct statistical analysis. Some of the more commonly used discrete and continuous probability distributions are discussed. The probability theory is used to develop the sampling distributions of point estimators so that interval estimation and statistical inference can be studied. Hypothesis tests for means, proportions, and variances as well as the differences between these parameters, and goodness of fit tests, are developed. The course concludes with an introduction to simple linear regression and some of its pitfalls and generalizations. Implementation of the statistical techniques studied using computer software is emphasized throughout the course.
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LEARNING OUTCOMES:
Upon successful completion of this course students will be able to:
- Use statistical core terminology accurately.
- Organise data using both numerical and graphical methods.
- Use measures of central tendency and variability to summarise a data set.
- Calculate probabilities of events using common probability distributions, including the normal.
- Compute point and confidence interval estimates of population parameters.
- Conduct tests of hypotheses about population parameters.
- Perform simple linear regression and understand its limitations
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TEXTBOOK:
| Book Title | Author | Publisher | ISBN number | Library Call Number | Comments | Format | Local Bookstore | Online Purchase |
| OpenIntro Statistics, 3rd Edition | Diez, Barr, and Çetinkaya-Rundel | OpenIntro | 978-1943450046, 978-1943450053 | | The textbook is available free of charge as a PDF file at https://www.openintro.org/stat/textbook.php?stat_book=os. Printed copies are also available at low cost on Amazon. Students will also need access to a computer with MS Excel and its Analysis Toolpak installed. Such computers are available in the computer labs at JCU. | | | |
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REQUIRED RESERVED READING:
RECOMMENDED RESERVED READING:
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GRADING POLICY
-ASSESSMENT METHODS:
| Assignment | Guidelines | Weight |
| Homework | Homework assignments will be graded | 10 |
| Attendance | Full credit for attendance will be given to students with two or fewer unexcused absences. | 10 |
| First intermediate exam | 1hr exam held during class period | 20 |
| Second intermediate exam | 1hr exam held during class period | 20 |
| Final exam | Comprehensive exam covering the entire semester's work | 40 |
-ASSESSMENT CRITERIA:
AWork of this quality directly addresses the question or problem raised and provides a coherent argument displaying an extensive knowledge of relevant information or content. This type of work demonstrates the ability to critically evaluate concepts and theory and has an element of novelty and originality. There is clear evidence of a significant amount of reading beyond that required for the cou
BThis is highly competent level of performance and directly addresses the question or problem raised.There is a demonstration of some ability to critically evaluatetheory and concepts and relate them to practice. Discussions reflect the student’s own arguments and are not simply a repetition of standard lecture andreference material. The work does not suffer from any major errors or omissions and provides evidence of reading beyond the required assignments.
CThis is an acceptable level of performance and provides answers that are clear but limited, reflecting the information offered in the lectures and reference readings.
DThis level of performances demonstrates that the student lacks a coherent grasp of the material.Important information is omitted and irrelevant points included.In effect, the student has barely done enough to persuade the instructor that s/he should not fail.
FThis work fails to show any knowledge or understanding of the issues raised in the question. Most of the material in the answer is irrelevant.
-ATTENDANCE REQUIREMENTS:
ATTENDANCE REQUIREMENTS AND EXAMINATION POLICY
You cannot make-up a major exam (midterm or final) without the permission of the Dean’s Office. The Dean’s Office will grant such permission only when the absence was caused by a serious impediment, such as a documented illness, hospitalization or death in the immediate family (in which you must attend the funeral) or other situations of similar gravity. Absences due to other meaningful conflicts, such as job interviews, family celebrations, travel difficulties, student misunderstandings or personal convenience, will not be excused. Students who will be absent from a major exam must notify the Dean’s Office prior to that exam. Absences from class due to the observance of a religious holiday will normally be excused. Individual students who will have to miss class to observe a religious holiday should notify the instructor by the end of the Add/Drop period to make prior arrangements for making up any work that will be missed. The final exam period runs until August 4.
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ACADEMIC HONESTY
As stated in the university catalog, any student who commits an act of academic
dishonesty will receive a failing grade on the work in which the dishonesty occurred.
In addition, acts of academic dishonesty, irrespective of the weight of the assignment,
may result in the student receiving a failing grade in the course. Instances of
academic dishonesty will be reported to the Dean of Academic Affairs. A student
who is reported twice for academic dishonesty is subject to summary dismissal from
the University. In such a case, the Academic Council will then make a recommendation
to the President, who will make the final decision.
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STUDENTS WITH LEARNING OR OTHER DISABILITIES
John Cabot University does not discriminate on the basis of disability or handicap.
Students with approved accommodations must inform their professors at the beginning
of the term. Please see the website for the complete policy.
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SCHEDULE
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Session
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Session Focus
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Reading Assignment
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Notes
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July 2
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Introduction
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Chapter 1.1-1.5
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July 3
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Basic Data Analysis
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Chapter 1.6-1.8
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Bring your laptop to class
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July 5
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Introduction to probability
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Chapter 2.1
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July 6
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Introduction to probability
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Chapter 2.4
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Make-up day for class missed on July 4
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July 9
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Introduction to probability
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Chapter 2.2
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July 10
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Discrete probability distributions
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Chapter 3.2, 3.5.2
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July 11
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Continuous probability distributions
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Chapter 2.5, 3.1
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July 12
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Sampling and Sampling Distributions
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Chapter 4.1
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July 16
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1st Intermediate Exam
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Material through July 11
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July 17
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Sampling and Sampling Distributions
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Chapter 4.4
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July 18
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Interval estimation
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Chapter 4.2, 4.5.1
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July 19
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Statistical inference
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Chapter 4.3, 5.1,6.1
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July 23
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Statistical inference
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July 24
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Tests of hypotheses about differences in parameters
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Chapter 5.2, 5.3, 6.2
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July 25
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Tests of hypotheses about variances
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July 26
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2nd Intermediate Exam
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Material from July 12 to July 23
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July 30
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Goodness of fit tests
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Chapter 6.3, 6.4
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July 31
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Simple linear regression
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Chapter 7.1-7.3
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August 1
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Simple linear regression
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Chapter 7.4,8.1,8.3
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August 2
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Course Review
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August 3
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Comprehensive final exam
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Note that there will be no class meeting on July 4 in observance of the Independence Day holiday. The missed class meeting will be made up on July 6.
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