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 | | MATH161 syllabus

IAI CODE(S): M1 902 BUS 901
DELIVERY MODE:Online, In-Person

Introductory statistics course at the non-claculus level. Focuses on statistical reasoning and the solving of problems using real-world data rather than on computational skills. Emphasis is on interpretation and evaluation of statistical results that arise from simulation and technology-based computations using technology more advanced than a basic scientific calculator, such as graphing calculators with a statistical package (TI83/84 recommended), spreadsheets (Microsoft Excel will be used), or statistical computing software. 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.

A student in this course should be college-ready in mathematics as assessed by local institutions (for example: College Algebra with a C or better, placement, co-requisite course, multiple measures, transitional mathematics competencies, PMGE, or professional organization recommendations, etc.


Upon completion of this course, students will be able to:
  • 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 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.
  • 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.
Topic Specific Outcomes
  • Learn definitions of, and be able to properly use in discussions (oral or written), basic statistical terminology
  • Learn to create statistical graphs from data, both manually and by using technology
  • 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 and the Central Limit Theorem
  • Learn the concept and application of a normal and a binomial distribution
  • Learn the concept and application of a discrete distribution
  • Learn to estimate the population mean through the use of confidence intervals
  • Learn to provide inferences and test hypothesis on population means using hypotheses tests with the Z statistic
  • Learn to provide inferences and test hypothesis on population means using hypotheses tests with the t statistic
  • Learn to provide inferences and test population vs sample distributions using Chi-Square techniques
  • Learn to provide inferences on bivariate data using correlation
  • Learn the concept of linear regression, apply it to bivariate data, provide inferences on the data

The course is a one semester course in non-calculus statistics. Primary importance is given to the actual application of statistics.
  • A First Look at Statistics. 4%
    • Why study statistics?
    • What is statistics?
  • Graphical Methods of Data Description. 5%
    • Frequency Distributions and Histograms
    • Dotplots and Stem-Leaf Displays
    • Bar and Pie Charts
  • Numerical Methods of Inference. 18%
    • Central tendency
      • Mean
      • Median
      • Mode
    • Variability
    • Variance
    • Standard deviation
    • Empirical Rule
    • Chebyshev ’s Theorem
    • Z-Scores
    • Percentiles
    • Box Plots
  • Probability. 15%
    • Sample Space and Events
    • Random variables, continuous and discrete
    • Probability Rules for Addition and Multiplication
    • Conditional Probability
    • Random sampling
  • Discrete Distributions. 7%
    • Binomial
    • Poisson
    • Hypergeometric
  • Normal Distributions. 18%
    • Standard Normal curve
    • Central limit theorem for means and sums
    • Binomial Approximation
    • Application to Sampling processes
  • Testing of Hypothesis. 10%
    • Elements of a statistical test
    • Type I and type II errors
    • Statistical significance
    • Large Sample Tests
    • Small Sample Tests
    • Two Sample Tests
    • Chi-Square Distribution
    • ANOVA
    • Applications and Interpretations of Statistical Tests
  • Estimation. 10%
    • Point and interval estimation
    • Biased and unbiased estimators
    • Confidence limits, coefficients, and intervals
  • Non-Parametric Tests. 5%
    • Sign tests
    • Wilcoxen Tests
  • Linear Regression and Correlation. 8%
    • Linear Models
    • Method of least squares
    • Slope and intercepts
    • Correlation coefficient
    • Rank correlation coefficient
    • Coefficient of Determination
    • Interpretation
Weekly Topical Outline
  • Week 1
    • Introduction
    • Basic terminology
    • Sampling Techniques
  • Week 2
    • Data types
    • levels of measurement
    • graphs
    • distributions
  • Week 3
    • Measures of Center, variation, and position
    • 5-number summary
    • boxplots
    • outlier test
  • Week 4
    • Standard deviation
    • descriptive measures for a population
    • use of samples
  • Week 5
    • Basic probability definitions and application
    • Law of large numbers
    • Discrete distributions
  • Week 6
    • Combinatorics
    • Hypergeometric distribution
    • Binomial distribution
  • Week 7
    • The normal probability distribution
    • density functions
    • areas under normal curves
  • Week 8
    • Inverse normal processes
    • normal probability plots
  • Week 9
    • Distribution of the sample mean
    • Central limit theorem
    • probability and inverse calculations
  • Week 10
    • Distribution of the sample mean
    • applications
  • Week 11
    • Estimating a population mean
    • confidence intervals with known sigma
    • margin of error
    • sample size
  • Week 12
    • Estimation of population using Student t-Distribution
  • Week 13
    • Hypothesis testing
    • critical value and p-value approach
    • testing with known sigma
    • conclusion writing
  • Week 14
    • Hypothesis testing using Student t-distribution
    • non-parametric tests conclusion writing
  • Week 15
    • Chi-Square Distribution Regression and correlation
    • algebra graph review
    • regression equation
    • interpretation
    • graph analysis
  • Week 16
    • Correlation
    • Determination
    • application
    • conclusion writing
  • Week 17
    • Final Exam


Introductory Statistics, 10th Edition, Addison Wesley, 2016, Weiss.

See bookstore website for current book(s) at


The student should obtain a 70% competency in the above as measured by examinations, assignments, and a comprehensive Final.

"Assignments" will be 15 items composed of the following: 6 homework sets assigned from the textbook, 5 homework sets created by the instructor, 2 data collection/analysis surveys, 2 quizzes, 6 Excel projects.

For the online course, "Assignments" will be 20 online quizzes which incorporate textbook problem sets, instructor generated problems, and concepts in data collection/analysis. Additionally 6 Excel projects will be assigned.

The course grade will be determined by:
Final Exam:

Grading Scale:
A= 90-100%
B= 80-89%
C= 70-79%
D= 60-69%
F= less than 60%


Current internet resources.

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:

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.

Spring 2019

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