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JOHN CABOT UNIVERSITY

COURSE CODE: "MA 208"
COURSE NAME: "Statistics I"
SEMESTER & YEAR: Summer Session II 2024
SYLLABUS

INSTRUCTOR: Charles Richard Johnson
EMAIL: [email protected]
 HOURS: MTWTH 11:10 AM - 1:00 PM
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: By appointment.
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.
SUMMARY OF COURSE CONTENT:

After a brief introduction to the subject, both graphical and numerical techniques for representing data sets will be analyzed; probability theory will be then discussed using both discrete and continuous probability distributions. We will then move to analyzing sampling distributions, point estimators and confidence intervals.
We will also discuss hypothesis tests covering tests of the mean, proportion, and variance as well as differences between these parameters, Chi-squared goodness of fit tests, and an introduction to simple linear regression.

The topics covered are:

Data and Statistics

Descriptive statistics: tabular and graphical displays

Descriptive statistics: numerical measures

Introduction to Probability

Discrete probability distributions

Continuous probability distributions

Sampling and Sampling Distributions

Interval Estimation

Hypothesis Tests

Statistical inference about means and proportions with two populations

Inferences about Population Variances

Tests of goodness of fit and independence

Simple Linear Regression 
LEARNING OUTCOMES:
Upon successful completion of this course the students will be able to:

- Use statistical core terminology accurately.

- Organize data using both numerical and graphical methods.

- Use measures of central tendency and variability to summarize a data set.

- Calculate probabilities of events explained by the normal and the standard normal distribution using the appropriate tables.

- Estimate population parameters using confidence intervals.

- Carry out tests of hypothesis about population parameters. 
TEXTBOOK:
Book TitleAuthorPublisherISBN numberLibrary Call NumberCommentsFormatLocal BookstoreOnline Purchase
Statistics for Business & Economics, 14 ed. D. Anderson, D. Sweeney, T. Williams, J. Camm, J. Cochran, M. Fry, J. Ohlmann Cengage Learning9781337901062     
REQUIRED RESERVED READING:
NONE

RECOMMENDED RESERVED READING:
NONE
GRADING POLICY
-ASSESSMENT METHODS:
AssignmentGuidelinesWeight
Homework 20%
Exam 1 20%
Exam 2 20%
FINAL EXAMThe final exam may replace your exam grades if it help you in the calculation of your final course grade.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 course.
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:
Students are required to attend classes following the University’s policies.
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.
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.
SCHEDULE
Session Session Focus Reading Assignment Other Assignment Meeting Place/Exam Dates
Week 1 Chapter 1: Data and Statistics. Chapter 2: Descriptive statistics: tabular and graphical displays. Chapter 3: Descriptive statistics: numerical measures.
Week 2 Chapter 4: Introduction to Probability (sections 4.1 to 4.5). Chapter 5: Discrete probability distributions (sections 5.1 to 5.5).
Week 3 Chapter 6: Continuous probability distributions (sections 6.1 to 6.3). Chapter 7: Sampling and Sampling Distributions (sections 7.1 to 7.7). Exam 1
Week 4 Chapter 8: Interval Estimation. Chapter 9: Hypothesis Tests (sections 9.1 to 9.5).
Week 5 Chapter 10: Statistical inference about means and proportions with two populations. Chapter 11: Inferences about Population Variances. Chapter 12: Tests of goodness of fit and independence (sections 12.1 and 12.2). Chapter 14: Simple Linear Regression (sections 14.1 to 14.4). Exam 2; Final Exam (Comprehensive): see University schedule for date and time