CCCOnline LogoCourse Snapshot for MAT 135 - Introduction to Statistics

The information listed below is subject to change. Please review the course syllabus within your online course at the start of class.

Course Competencies

The competencies you will demonstrate in this course are as follows:

  1. Recognize and give examples of statistics terms and concepts including: descriptive and inferential statistics, qualitative and quantitative data, discrete and continuous random variables, different levels of measurement, populations and samples, parameters and sample statistics.  
  2. Present and interpret various methods of depicting data including histograms, stem and leaf diagrams, box and whisker plots, line graphs, bar graphs, pie charts.
  3. Recognize and identify various shapes of data distributions.
  4. Present various statistical measures using proper notation including measures of central tendency (mean, median, mode, and midrange), measures of dispersion (range, variance, and standard deviation), and measures of position (z-score, percentile, quartile, and decile).
  5. Organize data into a grouped frequency table.
  6. Utilize the basic definitions to calculate simple probabilities.
  7. Utilize the addition rule to calculate probabilities for the occurrence of one event or another event.
  8. Demonstrate an understanding of how events are complementary and calculate the probability that an event does not occur.
  9. Use counting principles to determine the number of ways various events can occur.
  10. Develop the concepts of probability distributions including the binomial.
  11. Use formulas to calculate the mean, variance, standard deviation, and expected value of a probability distribution including the binomial.
  12. Recognize and identify various shapes of probability distributions.
  13. Describe the normal distribution and the associated statistics and probabilities including area under a probability curve.
  14. Determine probabilities using the standard normal distribution.
  15. Determine scores that correspond to given probabilities.
  16. Use the normal distribution to approximate probabilities associated with a binomial experiment and know the conditions for which these approximations are appropriate.
  17. Explain the meaning of a sampling distribution and apply the central limit theorem.
  18. Develop point estimates and interval estimates (confidence intervals) for population means, proportions, and variance (including chi squared).  Determine sample size for a population mean and population proportion.
  19. Perform a hypothesis test on one mean and other parameters.
  20. Calculate and interpret the correlation coefficient.  Find a line of best fit applying the concept of residuals.
Learning concepts for this course are enchanced using illustrative models, video, and online discussions.

Module Outcomes Mapped to Competencies

Module 1 Getting Started and Chapter 1: Data Collection Learning Outcomes

 Mapped to Course Competencies (above)
  1. Define statistics and statistical thinking.
  2. Explain the process of statistics.
  3. Distinguish between qualitative and quantitative variables, discrete and continuous variables, and an observational study and an experiment.
  4. Determine the level of measurement of a variable.
  5. Explain the various types of observational studies.
  6. Identify simple random sampling, stratified sampling,  systematic sampling, and cluster sampling and explain the sources of bias in sampling.
  7. Describe the characteristics of an experiment, explain the steps in designing an experiment, and explain completely randomized design, matched-pairs design, and randomized block design.
 1

Module 2 Organizing and Summarizing Data Learning Outcomes

Mapped to Course Competencies (above)

  1. Organize qualitative data and quantitative data  in tables showing both cumulative frequency and relative frequency.
  2. Construct bar graphs, pie charts, histograms of discrete and continuous data, stem-and-leaf plots, dot plots, frequency polygons, frequency and relative frequency gives, and time-series graphs.
  3. Identify the shape of a distribution.
  4. Describe what can make a graph misleading or deceptive.
  5. Determine the arithmetic mean, median, mode, range, standard deviation, and variance  of a variable from raw data.
  6. Explain what it means for a statistic to be resistant.
  7. Use the Empirical Rule to describe data that are bell shaped.
  8. Use Chebyshev’s Inequality to describe any data set.
  9. Approximate the mean and standard deviation of a variable from grouped data.
  10. Compute the weighted mean.
  11. Determine and interpret z-scores and percentiles.
  12. Determine and interpret quartiles and the interquartile range, check a set of data for outliers, compute the five-number summary, and draw and interpret boxplots.
  13. Draw and interpret scatter diagrams.
  14. Compute and interpret the linear correlation coefficient.
  15. Determine whether a linear relation exists between two variables.
  16. Explain the difference between correlation and causation.
  17. Find the least-squares regression line and use the line to make predictions and interpret the slope and the y-intercept of the least-squares regression line.
  18. Compute and Interpret the coefficient of determination.
  19. Perform residual analysis on a regression model.
  20. Identify influential observations.
 1, 2, 3, 4, 5, 20

Module 3 Probability Learning Outcomes

Mapped to Course Competencies (above)

  1. Apply the rules of probabilities.
  2. Compute and interpret probabilities using empirical and classical method.
  3. Use the Addition Rule for Disjoint Events, the General Addition Rule, the Complement Rule, the Multiplication Rule for Independent Events, and the General Multiplication Rule to compute probabilities and apply these to find at-least probabilities and conditional probabilities.
  4. Solve counting problems using the Multiplication rule, using permutations, and using combinations.
  5. Compute probabilities involving permutations and combinations.
  6. Distinguish between discrete and continuous random variables.
  7. Identify discrete probability distributions.
  8. Construct probability histograms.
  9. Compute and interpret the mean and standard deviation of a discrete random variable.
  10. Determine whether a probability experiment is a binomial experiment.
  11. Compute probabilities, the mean and standard deviation, and  binomial probability histograms of  binomial experiments.
  12. State the properties of the normal curve.
  13. Explain the role of area in the normal density function.
  14.  Compute probabilities using the standard normal distribution.
  15. Find and interpret the area under a normal curve.
  16. Find the value of a normal random variable for a given area under the density curve.
 1, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16

Module 4 Inferential Statistics Learning Outcomes

Mapped to Course Competencies (above)

  1. Describe the distribution of the sample mean:  normal and nonnormal populations.
  2. Describe the sampling distribution of a sample proportion and compute probabilities of a sample proportion.
  3. Obtain a point estimate for the population proportion.
  4. Construct and interpret a confidence interval for the population proportion.
  5. Determine the sample size necessary for estimating the population proportion within a specified margin of error.
  6. Obtain a point estimate for the population mean.
  7. State properties of Student’s t-distribution and determine t-values.
  8. Construct and interpret a confidence interval for a population mean.
  9. Find the sample size needed to estimate the population mean within a given margin of error.
  10. Find critical values for the chi-square distribution to construct and interpret confidence intervals for the population variance and standard deviation.
  11. Determine the appropriate confidence interval to construct.
  12. Determine the null and alternative hypotheses.
  13. Explain Type I and Type II errors.
  14. State conclusions to hypothesis tests.
  15. Explain the logic of hypothesis testing.
  16. Test the hypotheses about a population proportion.
  17. Test hypotheses about a population proportion using the binomial probability distribution.
  18. Test hypotheses about a mean.
  19. Understand the difference between statistical significance and practical significance.
  20. Test hypotheses about a population standard deviation.
  21. Determine the appropriate hypothesis test to perform.
1, 4, 14, 17, 18, 19

Course Time Commitment and Expectations

For every credit hour, students should plan to spend an average of 2-3 hours per week for course-related activities in a 15-week course. For example, a 3 credit hour course would average an average 6-9 hours per week to read/listen to the online content, participate in discussion forums, complete assignments, and study the course material. For 10  and 6-week courses, the amount of time per week will be higher so all course competencies, module outcomes, and assignments will be covered.

Aside from typical reading assignments, this course has the following (Please Note: This list is subject to change based on the discretion of the instructor facilitating this course.):

 

AssignmentPoints
Discussions 70 Points
Introduction Discussion 10
Equation Editor Discussion 5
Screen Shot Discussion 5
Module 1 Discussion 10
Module 2 Discussion 10
Module 3 Discussion 10
Module 4 Discussion 1 10
Module 4 Discussion 2 10
Dropbox 130 Points
Module 2 Dropbox 100
Module 3 Dropbox 30
MyLabsPlus Chapter Homework (Best 10 of 11 each 20 points) 200 Points
Module Assessments 500 Points
Module 1 Quiz (Chapter 1) 50
Module 2 Test (Chapters 2-4) 150
Module 3 Test (Chapters 5-7) 150
Module 4 Test (Chapters 8-11) 150
Final Exam 200 Points
TOTAL 1100 Points

Extra Credit

 26 Points
Module 5 Discussion

10
Goodbye Discussion 5
Module 1 Practice Quiz 1
Module 2 Practice Test (one for each chapter: 2, 3, and 4) 3
Module 3 Practice Test (one for each chapter: 5, 6, and 7) 3
Module 4 Practice Test (one for each chapter: 8, 9, 10,
and 11)		 4

CCCOnline Course Quality Commitment

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