Course Descriptions & Syllabi

Course Descriptions & Syllabi

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Note: some or all of the courses in the subjects marked as "Transfer" can be used towards a transfer degree: Associate of Science and Arts or Associate of Engineering Science at DACC. Transferability for specific institutions and majors varies. Consult a counselor for this information.

Areas of Study | | MATH115 syllabus




COURSE NUMBER: MATH115
COURSE TITLE:Survey of Statistics (Statistics for non-math majors)
DIVISION:Sciences
IAI CODE(S): M1 902
SEMESTER CREDIT HOURS:3
CONTACT HOURS:60
STUDENT ENGAGEMENT HOURS:135
DELIVERY MODE:Online, In-Person

COURSE DESCRIPTION:

Focuses on statistical reasoning and the solving of problems using real-world data rather than on computational skills. Strong mphasis is on interpretation and evaluation of statistical results that arise from simulation and technology-based computations using technology such as the required TI83/84 Graphing Calculator with a built-in statistical package, and Microsoft Excel spreadsheets.. Topics include data collection processes (observational studies, experimental design, sampling techniques, bias), descriptive methods using quantitative and qualitative data, bivariate data, correlation, and least­-squares regression, basic probability theory, probability distributions (normal distributions and normal curve, binomial distribution), confidence intervals and hypothesis tests using p-values.

This course is designed as a general survey of basic statistical methods. Emphasis is placed on methodology, and applications to biological, social, and management sciences are stressed to underscore the practicality of the material.


PREREQUISITES:

A student in this course should be college-ready in mathematics by having completed: Intermediate Algebra (MATH108) with a C or better, placement, co-requisite course, multiple measures, transitional mathematics competencies, Applied Mathematical concepts (MATH107) with a C or better. Recommendations


NOTES:

STUDENT LEARNING OUTCOMES:
Upon completion of this course, students will be able to:
  • Use, understand and write all statistical symbols and abbreviations.
  • Clearly relate interpretation of solutions to statistical application problems.
  • Clearly show work or provide explanation as how to setup and generate a statistical solution for application problems.
  • Correctly make use of graphing calculators and /or spreadsheet software to complete statistical analysis.
  • Achieve strong critical thinking skills in terms of problem solving. Students are expected to be able to determine from any initial question of any of the following that apply: (1) the meaning and importance of all given statistics.
  • Learn definitions of, and be able to properly use in discussions (oral or written), basic statistical terminology.
  • Learn to use, understand and write all statistical symbols and abbreviations, as well as translating/explaining statements written in symbolic form into common non-mathematical language. Students will learn to maintain precision of language.
  • Learn to create statistical graphs from data by using technology.
  • Learn to interpret and explain trends from statistical graphs.
  • Learn to generate basic statistics from data, both manually and by using technology.
  • Learn to interpret basic statistical information, with regard to patterns and outliers.
  • Learn to use and apply basic probability concepts, and to develop statistical distributions from these concepts.
  • Learn and apply the Empirical Rule.
  • Learn the concept and application of a normal distribution.
  • Learn the concept and application of a binomial distribution.
  • Learn to create, test validity of, and apply linear models from data using regression and correlation techniques.
  • Learn the concept and application of the Empirical Rule.
  • Learn to estimate the population mean through the use of confidence intervals.
  • Learn to provide inferences and test hypotheses on population means using hypothesis tests with the Z statistic.
  • Learn to provide inferences and test hypotheses on population means using hypothesis tests with the t statistic.
  • Learn to clearly show work or provide explanation as how to setup, and then generate, a statistical solution for application problems. Showing work includes the ability to write down, with detail and precision, as well as the formats needed for when using technology.
  • Learn to strengthen critical thinking skills in terms of problem solving. Students will learn to be able to determine, from any initial question, the techniques needed to deconstruct the information provided in a problem as it relates to solution.
  • Learn to clearly relate interpretation of solution to give real-world meaning to statistical answers.
  • Learn to properly make use of the TI line of graphing calculators and /or spreadsheet software, such as Microsoft Excel, as needed to complete statistical analysis, create graphs, check answers. Students are expected to be able to utilize the internet to conduct research, as needed.

TOPICAL OUTLINE:
  • Introduction to Statistics [6%]
    • Background
    • Uses and abuses of statistics
    • The nature of data
    • Methods of sampling
  • Descriptive Statistics [14%]
    • Summarizing data
    • Data graphs
    • Measure of centrality
    • Measures of dispersion
    • Measures of position
  • Basic Sampling [6%]
    • Basic definitions
    • Bias
    • Sampling techniques
  • Probability [12%]
    • Fundamental definitions
    • Addition rule
    • Multiplication rule
  • Probability Distributions [24%]
    • Random variables
    • Binomial Distribution
    • Standard normal distribution
    • Non-standard distribution
    • The central limit theorem and the sampling distribution of the sample mean
  • Confidence Intervals [12%]
    • Estimation of a population mean
    • Estimation of a population proportion
    • Sample size considerations
  • Hypothesis Testing [12%]
    • Fundamental definitions
    • Testing a claim about a population mean: large samples
    • Testing a claim about a population mean: small samples (t-test)
    • Testing a claim about a proportion
  • Linear Regression [14%]
    • Fundamental definitions
    • Correlation and Pearson-R correlation coefficient
    • Regression (linear)

TEXTBOOK / SPECIAL MATERIALS:

The Basic Practice of Statistics, 7th Edition, Moore, Notz and Fligner, Freeman, 2015.

See bookstore website for current book(s) at https://www.dacc.edu/bookstore

EVALUATION:

The student shall be evaluated on the basis of quizzes & homework (15%), Excel lab projects* (7%), major examinations (60 %), final examination (18%), and progress during the course.

90-100%
80-89%
70-79%
60-69%
Below 60%
A
B
C
D
F

*A minimum of 6 Excel projects will be chosen from the following:
WEEKLY LAB OUTLINE: Labs are designed to develop students’ critical thinking skills and give them hands-on practice at applying the concepts discussed in class, organizing and analyzing data, and drawing and writing conclusions. Excel software will be used.
  • Lab 1 – Intro & Bar Graphs/Pie Charts:
    • Students are given a brief introduction to lab policies and procedures and create a bar graph/pie chart from given data.
  • Lab 2 – Histograms:
    • Students create a histogram from a given distribution and use the graph to describe the shape, center and spread of the distribution.
  • Lab 3 – Time Plots:
    • Students make a time plot from data and use it to describe cycles and trends.
  • Lab 4 – Measures of Center and Spread:
    • Students calculate several statistics and use multiple graphs to do individual and comparison analysis on two or more distributions.
  • Lab 5 – Standard Normal Curve Calculations:
    • Students create a template for performing various calculations involving the standard normal curve.
  • Lab 6 – Scatterplots, Regression and Correlation:
    • Students use given data to create a scatterplot and perform multivariate analysis using regression and the correlation coefficient.
  • Lab 7 – Residual Plots:
    • Students perform further multivariate analysis with the use of a residual plot they create.
  • Lab 8 – Probability Distributions:
    • Students make discrete probability distributions and calculate probabilities for simple random experiments, such as the outcome of the roll of two dice.
  • Lab 9 – Law of Large Numbers:
    • Students make an inference and test it by performing an experiment a “large” number of times, making calculations and graphing the results.
  • Lab 10 – Sampling Distributions of the Sample Mean:
    • Students consider continuous distributions by forming multiple random samples, calculating their sample means and creating a distribution of those sample means.
  • Lab 11 – Central Limit Theorem:
    • Students make further discoveries of the result of a sampling distribution, which leads to the Central Limit Theorem.
  • Lab 12 – One-Sample z Confidence Intervals
  • Lab 13 – One-Sample z Significance Tests
  • Lab 14 – One-Sample t Confidence Intervals
  • Lab 15 – One-Sample t Significance Tests

BIBLIOGRAPHY:

Current internet resources.

STUDENT CONDUCT CODE:
Membership in the DACC community brings both rights and responsibility. As a student at DACC, you are expected to exhibit conduct compatible with the educational mission of the College. Academic dishonesty, including but not limited to, cheating and plagiarism, is not tolerated. A DACC student is also required to abide by the acceptable use policies of copyright and peer-to-peer file sharing. It is the student’s responsibility to become familiar with and adhere to the Student Code of Conduct as contained in the DACC Student Handbook. The Student Handbook is available in the Information Office in Vermilion Hall and online at: https://www.dacc.edu/student-handbook

DISABILITY SERVICES:
Any student who feels s/he may need an accommodation based on the impact of a disability should contact the Testing & Academic Services Center at 217-443-8708 (TTY 217-443-8701) or stop by Cannon Hall Room 103. Please speak with your instructor privately to discuss your specific accommodation needs in this course.

REVISION:
Spring 2019

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