STAT412
Learning Goals
Upon completing Stat 412 students should be able to demonstrate competency of the learning goals by:
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Analyzing a variety of situations, problems or questions involving statistical concepts and data interpretation;
Using statistical and probabilistic concepts, rules, and assumptions applicable to specific situations, problems, or questions;
Using consistent (logical) thinking to solve problems;
Producing and interpreting numerical and graphical representations of quantities and relationships;
Synthesizing information into a consistent solution in multiple (including unfamiliar) contexts.
Specific STAT 412 content competency requirements for meeting the learning goals include:
Knowledge
Empirical (experimental)
Theoretical Probability
Concepts of Probability
Interpretation of Probability
Probability Distributions
Knowledge
Understand Procedures for
Analyzing and Solving
Problems
Understand the Nature of
Statistical Inference
Probability
Skills
Be able to estimate the probability of an event based on the number of successes in
an experiment. Assessed through homework, laboratory assignments and exams.
Understand how the probability of an event with equally likely outcomes can be
determined as the ratio of the number of outcomes in which the event occurs to the
total number of outcomes. Assessed through homework, laboratory assignments and
exams.
Be able to compute probabilities using such basic concepts as mutually exclusive
events, independent events, complementary events and conditional probability.
Assessed through homework, laboratory assignments and exams.
Understand how probability relates to randomness in daily life. Assessed through
homework and laboratory assignments.
Be able to identify appropriate use of probability distributions such as the normal,
along with the use of sampling distributions in statistical inference. Assessed through
homework, laboratory assignments and exams.
Problem Solving and Logical Reasoning
Skills
Use problem solving strategies to identify appropriate statistical methods to solve a
variety of problems such as t-tests, regression and analysis of variance. Assessed
through
homework, laboratory assignments and exams.
Be able to follow the steps of a statistical argument and the role and limitations of
statistical inference including distinguishing between statistical significance or
non-significance and practical importance. Assessed through homework, laboratory
assignments and exams.
Applications of Statistics to Science, Societal Issues and Personal Situations
Knowledge
Skills
Statistics in Science
Have an awareness of how statistics is used in the biological, physical and social
sciences. Assessed through homework.
Statistics in Society
Have an awareness of how statistics is used in society such as measuring social
attitudes and opinions, testing the effectiveness of new drugs or used in biological
testing. Assessed through homework.
Statistics for Living
To understand and be able to distinguish between populations and samples, quantify
uncertainty and variability in everyday life and use statistical concepts for making
logical decisions. Assessed through homework and laboratory assignments.
Knowledge
Statistics as a Language to
Convey Quantitative
Information
Communication
Skills
Be able to organize and present quantitative information, incorporating appropriate
verbal, symbolic, and graphical elements of expression.
Summary of the Learning Outcomes
This course will strengthen each student’s creative reasoning and critical thinking by developing an understanding between the
research designs used to collect data, the statistical models used to analyze the data and the interpretation of the resulting analysis.
This course will specifically cover the appropriate collection and analysis of data so that valid inferences can be made concerning the
population(s) of interest. The topics covered in this course will include basic probability and probability models, the central limit
theorem, estimation and inference for one and two means and proportions from independent and dependent sources, chi-square tests
for qualitative data, regression modeling and correlation and analysis of variance.