Miami-Dade College
Hialeah Campus
Dept Liberal Arts and Sciences
Course: STA2023 3 credits. Summer B 2007-03
Textbook: Elementary Statistics, 10th Edition, Mario Triola, Addison Wesley,
ISBN: 0-321-28839-4
Meeting Days: T-R 8:00 -11:20 AM Room 1319
Instructor: Dr. Jaime Bestard email jbestard@mdc.edu Ph (305)237-8766
OFFICE HRS: 11:05-1:00 PM, Room 1403-04
GENERAL EDUCATION LEARNING OUTCOMES
Purpose: Through the academic disciplines and co-curricular activities, General
Education provides multiple, varied, and intentional learning experiences to facilitate the
acquisition of fundamental knowledge and skills and the development of attitudes that
foster effective citizenship and life-long learning.
As graduates of Miami Dade College, students will be able to:
1.
2.
3.
4.
5.
Communicate effectively using listening, speaking, reading, and writing skills.
Use quantitative analytical skills to evaluate and process numerical data.
Solve problems using critical and creative thinking and scientific reasoning.
Formulate strategies to locate, evaluate, and apply information.
Demonstrate knowledge of diverse cultures, including global and historical
perspectives.
6. Create strategies that can be used to fulfill personal, civic, and social
responsibilities.
7. Demonstrate knowledge of ethical thinking and its application to issues in society.
8. Use computer and emerging technologies effectively.
9. Demonstrate an appreciation for aesthetics and creative activities.
10. Describe how natural systems function and recognize the impact of humans on the
environment.
Course description and objectives:
Collecting, grouping and presenting data; measures of central tendency and
dispersion; probability; testing hypotheses; confidence intervals, and correlation.
Prerequisite: MAT1033; co-requisite MAC 1105 or equivalent with a grade of “C” or
better.
EVALUATION POLICY:
Four 1 hour tests, a 1 h midterm exam and a mandatory comprehensive 1.5 hour final
exam will be given during the term. Students are supposed to show and write all their
work and conclusions in all evaluations. Students must pass the Final Exam with
score of 60 or more. The Final Grade will be composed as followed: 5 % total
homework or instructor criteria, Midterm Exam 20 %, Test 10 % each, Final Exam 35
%.Two missing evaluations will result in a failing grade. Absolutely no make-up
examinations will be given. HOMEWORK, SHOWING YOUR WORK, IS DUE
EVERY TEST DAY in class. Late returns in homework are not accepted. It is strongly
advised the use of the Academic Support Laboratory with its free tutoring service, as
well as any similar free service offered by certified tutors campus wide. The use of the
Campus Library is strongly advised to meet the required Information Literacy as well
GRADING SCALE:
90 – 100
=
A
80 – 89
=
B
70 – 79
=
C
60 – 69
=
D
0 – 59 =
F
ATTENDANCE:
Attendance and punctuality to class is mandatory, late arrivals and early leaves are
supposed to be only on breaks of the session to eliminate disruptions. Students are
expected to attend, to be punctual and to participate in class, two late arrival or early
leave are equivalent to an absence; three absences in a row or a total of five absences
across the course is cause of course withdrawal. Students are responsible to prepare all
topics and material covered in syllabus. Students who attend classes, and do not appear
on the class roll will be asked to report to the Registrar’s Office to obtain a
paid/validation schedule.
Under no circumstances you will be allowed to remain in class if your schedule is not
stamped paid/validated. Mobile phones are required to be turned off during lectures.
DROPS/WITHDRAWALS:
It is the student’s responsibility to withdraw from the class if he/she should decide to.
Cheating and Plagiarism: Academic honesty is the expected mode of behaviour.
Students are responsible for knowing the policies regarding cheating and plagiarism and
the penalties for such behaviour. Failure of an individual faculty member to remind the
students as to what constitutes cheating and plagiarism does not relieve the student of his
responsibility. Students must take care not to provide opportunities for others to cheat.
Students must inform the faculty member if cheating or plagiarism is taking place.
Diversity Statement: The MDC community shares the belief that individual and
collective educational excellence can only be achieved in an environment where human
diversity is valued.
Students with Disabilities: It is my intention to work with students with disabilities and
I recommend them to contact the Access Services, (305) 237-1272, Room 6112, North
Campus, to arrange for any special accommodations.
Learning outcomes:
1. To collect representative and un-biased data using the statistical methods.
2. To organize data in frequency distributions charts.
3. To construct data displays according to the statistical methods.
4. To understand and explain data using quantitative reasoning and statistical
inferences.
Specific objectives:
1. Collect, classify and organize data.
2. Construct a frequency distribution, which also shows cumulative and relative
frequencies.
3. Construct a histogram.
4. Construct and interpret stem-and-leaf plots.
5. Compute measures of central tendency.
6. Compute measures of dispersion.
7. Find the percentile of a score or find the score corresponding to a percentile.
8. Construct a box-and-whisker diagram.
9. Use various counting rules, including the Multiplication Rule, Permutations and
Combinations
10. Know the meanings of sample space, outcome, event, classical vs. empirical
probability
11. Compute probabilities of simple events, complement probabilities, and odds
12. Apply the addition rules of probability.
13. Apply the multiplication rules of probability.
14. Compute conditional probabilities.
15. Understand the meaning of a probability distribution and be able to construct
discrete.
16. Compute the mean & variance of a probability distribution.
17. Find the expected value of a discrete probability distribution.
18. Compute probabilities using the binomial probability distribution.
19. Apply the empirical rule for normally distributed data.
20. Calculate z-scores and find probabilities for normally distributed data.
21. Compute the mean of the sampling distribution of the means or proportions.
22. Apply the Central Limit Theorem.
23. Apply the normal approximation to the binomial distribution.
24. Construct confidence intervals for proportions.
25. Construct confidence intervals for a mean (  Known or  Not known).
26. Perform hypothesis tests for proportions.
27. Perform hypothesis tests for means (  Known or  Not known).
28. Perform hypothesis tests for variances or standard deviations (OPTIONAL).
29. Compute the p-value associated with a hypothesis test.
30. Understand the relationship between a confidence interval and a two-tail
hypothesis test.
31. Perform hypothesis tests for the difference between two means (independent
samples)
32. Perform hypothesis tests for the difference between two means (small, dependent
samples: Matched Pairs).
33. Perform hypothesis tests for the difference between two proportions.
34. Construct a scatter-plot for paired data.
35. Compute and understand the meaning of the linear correlation coefficient.
36. Determine the linear regression equation for paired data.
37. Be able to graph a linear regression equation and use it to make predictions.
TENTATIVE SCHEDULE
SECTIONS COVERED
Meeting
1 Introduction; 1.2: Types of Data; 1.3: Critical Thinking: Uses and Abuses of Statistics; 1.4:
Design of Experiments
2.2: Frequency Distributions; 2.3: Visualizing Data, histograms, pie charts and bar graphs
Chapter 3: Measures of Center
2 Measures of Variation
Measures of Relative Standing; Exploratory Data Analysis
Exercises
TEST #1
3 Chapter 4: Fundamentals of Probability; Events relationships, The Addition Rule
The Multiplication Rule; Complements and Conditional Probability
Counting Rules
4 Chapter 5: Random Variables
Binomial Probability Distribution; Mean, Variance, and Standard Deviation of Binomial
Distribution
Exercises
TEST #2
5 Chapter 6: The Standard Normal Distribution; Applications of Normal Distributions
(Conclusion of applications of normal distributions); Sampling Distributions and Estimators
The Central Limit Theorem
Normal Approximation to the Binomial
6 Chapter 7: Estimating a Population Proportion
Estimating a Population Mean:  Known; Estimating a Population Mean:  Not Known;
Estimating a Population Variance
Exercises
MIDTERM
7 Chapter 8: Basics of Hypothesis Testing; Testing a Claim about a Proportion
Testing a Claim about a Mean:  Known
Testing a Claim about a Mean  Not Known
Chapter 9: Inferences about Two Proportions
Inferences about Two Means: Independent Samples; Inferences from Matched Pairs
8 Correlation; Regression (Overview)
Exercises
Regression analysis, exercises using technology
Inferences about two populations, exercises using technology TEST 3
9 ANOVA, exercises, exercises using technology
Exercises. Review
10 Exercises. Review / TEST 4
11 Exercises Review
12 Review / FINAL EXAM
I, __________________________________________ , Student ID____________,
have understood and discussed the terms and conditions exposed in the course syllabus
for STA2023 already given to me by Dr. Jaime Bestard, professor on the first meeting of
this course at The MDC-Hialeah Campus. By signing this release, I enter in contract with
my course instructor and agree with my responsibility over the completion of the terms
and conditions to meet the appropriate development of the educational objectives and
learning outcomes of this subject.
_______________________________________, ___________________________
Student signature
Date