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National College Credit Recommendation Service

Board of Regents  |  University of the State of New York

Eastwick College | Evaluated Learning Experience

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Introduction to Applied Statistics (MATH301)

Course Category: 

54 hours (12 Weeks)

10 South Franklin Turnpike, Ramsey, NJ 07446

August 2019 – Present

Instructional delivery format: 
Traditional classroom model
Learner Outcomes: 

Upon successful completion of the course, students will be able to: identify variables in a statistical study; distinguish between quantitative and qualitative variables; identify populations and samples; explain the importance of random samples; formulate a random sample; construct a simple random sample using random numbers; discuss what it means to take a census; discuss potential pitfalls that might make the data unreliable; determine types of graphs appropriate for specific data; organize raw data using a frequency table; recognize basic distribution shapes: uniform symmetric, skewed, and bimodal; interpret graphs in the context of the data setting; interpret information displayed in graphs; construct a stem-and-leaf display from raw data; compare a steam-and-leaf display to a histogram; formulate mean, median and mode from raw data; interpret what mean, median and mode will tell you; compute a weighted average; compute the range, variance, and standard deviation; apply Chebyshev’s theorem to raw data; interpret the meaning of percentile scores; calculate the median, quartiles, and five number summaries from raw data; demonstrate how to assign probabilities to events; apply basic rules of probability in everyday life; explain the relationship between statistics and probability; calculate probabilities of general compound events; use survey results to compute conditional probabilities; organize outcomes in a sample space using tree diagrams; explain how counting techniques relate to probability in everyday life; distinguish between discrete and continuous random variables; graph discrete probability distributions; list the defining features of a binomial experiment; use binomial probability distribution to solve real world applications; make histograms for binomial distributions; use the Poisson distribution to compute the probability of the occurrence of events spread out over time or space; illustrate how to graph a normal curve and summarize its important properties; apply the empirical rule to solve real world problems; graph the standard normal distribution, and find areas under the standard normal curve; calculate the probability of “standard events”; use the inverse normal to solve guarantee problems; review commonly used terms as random sample, relative frequency, parameter, statistic, and sampling distribution; recall the statement and underlying meaning of the central limit theorem well enough to explain it to a friend who is intelligent but doesn’t know much about statistics; state the assumptions needed to use the normal approximation to the binomial distribution; explain the meanings of confidence level, error of estimate, and critical value; solve for the critical value corresponding to a given confidence level; recall the degrees of freedom and student’s t distributions; calculate the critical values using degrees of freedom and confidence levels; calculate the maximal margin of error for proportions using a level of confidence; distinguish between independent and dependent samples; interpret the meaning and implications of an all positive, all negative, or mixed confidence interval; discuss the rationale for statistical test; identify right tailed, left tailed and two tailed tests; recognize types of errors, level of significance, and power of a test; review the general procedure for testing using the P-values; identify the components needed for testing a proportion; calculate the sample test statistic; identify paired data and dependent samples; explain the advantages of paired data tests; identify independent samples and sampling distributions; construct a scatter diagram; visually estimate the location of the “best fitting” line for a scatter diagram; state the least squares criterion; explain the difference between interpolation and extrapolation; test the correlation coefficient P; review the advantages of multiple regression; test coefficients in a model for statistical significance; design a test to investigate independence of random variables; conduct a test of homogeneity of populations; create a test to investigate how well a sample distribution fits a given distribution, calculate the sample X2 statistic; employ sample variances to compute the sample F statistic; discuss the notation and set up for a one way ANOVA test; discuss the notation and set up for a two way ANOVA test; state the criteria for setting up a rank sum test; complete a matched pair sign test; recall the criteria for setting up a rank sum test; use the distribution of ranks to complete the test; recognize the monotone relations and the Spearman rank correlation coefficient; analyze a sequence of numbers for randomness about the median; develop the analytical skills of the student in order to better comprehend various issues presented by problems involving STATISTICS.


Textbook readings; lecture, Powerpoints; handouts; chapter guided exercises; and review of problems; exams; assignments. Prerequisite: MATH 101, MATH 102

Credit recommendation: 

In the lower division baccalaureate / associate degree category, 3 semester hours in Applied Statistics, Statistics, or Mathematics (5/22). NOTE: This course was previously evaluated by the American Council on Education (ACE). To view credit recommendations previously established, visit the ACE National Guide.