Course Goals:
A student will be able to:
- Interpret mathematical information using symbolic, visual, numerical and verbal conventions.
- Solve problems using numeric, algebraic, geometric and statistical method.
- Use quantitative information in context to determine reasonableness of results.
- Use appropriate mathematical tools (e.g. calculators, computers, measurement instruments
and manipulatives) in problem solving.
Description
A quantitative literacy course with a statistical theme. Includes descriptive statistics,
sampling, and inferential methods. Emphasizes problem solving and critical thinking.
Design of Experiments
A student is able to:
- Distinguish between observational studies and randomized comparative experiments.
- Explain the logic of experimental design including the concepts of blinding and the
placebo effect.
- Identify confounding and discuss “control” as it relates to both experiments and observational
studies
Descriptive Statistics
A student is able to:
- Accurately read and interpret density histograms
- Compute percent of observations above or below a given value using area under the
curve.
- Estimate the average and standard deviation of the distribution.
- Describe shape of the distribution.
- Compute the average and standard deviation for numerical data sets
- Compute the five number summary, construct and accurately interpret boxplots.
- Compute percentiles and probabilities for the normal distribution using both the z-transformation
and the “68-95-99.7” rule
- Separate measurement error into chance error and bias.
Correlation and Regression
A student is able to:
- Determine strength and direction of the relationship between two variables using a
scatter plot.
- Estimate the correlation from a scatter plot.
- Compute the correlation coefficient..
- Distinguish between association and causation and discuss the limitations of correlation.
- Distinguish between studies using cross-sectional data versus longitudinal data and
discuss their ramifications.
- Compute and make predictions using the standard deviation line.
- Compute and make predictions using the regression line.
- Compare the standard deviation line to the regression line and discuss the regression
to the mean phenomena.
- Compute and interpret the root mean square error for regression.
- Compute and interpret the coefficient of determination for regression.
- Compute probabilities using the normal curve inside a vertical strip in regression.
Probability
A student is able to:
- Compute probabilities using the multiplication rule.
- Distinguish between conditional probabilities and independence.
- Compute probabilities using the addition rule.
- Identify mutually exclusive events.
Chance Variability
A student is able to:
- Discuss the Law of Averages.
- Compute absolute error and relative error for a chance process.
- Compute the expected value and standard error for the sum / avg. of draws in a chance
process.
- Numerical data
- Count / classify data
- Use the normal curve to compute probabilities for the sum / avg. of draws in a chance
process.
- Numerical data
- Count / classify data
Sampling
A student is able to:
- Compare and contrast probability sampling methods with quota sampling methods.
- Distinguish between populations and samples.
- Identify target population and sample population.
- Distinguish between parameters and statistics.
- Identify types of bias in sampling: selection bias, non-response bias, response bias.
- Compute and interpret confidence intervals for the population average.
- Compute and interpret confidence intervals for the population proportion / percent.
Tests of Significance
A student is able to:
- Conduct a large sample test of significance for the population average
- Conduct a small sample test of significance for the population average.
- Conduct a large sample test of significance for the population proportion / percent.
- Conduct a test of significance to compare two population averages using independent
samples
- Conduct a test of significance to compare two population proportions / percents using
independent samples.
- Conduct the chi-square goodness-of-fit test.
- Conduct the chi-square test for independence.
- Interpret p-values and statistical significance.
- Discuss the limitations of tests of significance.
Practice Questions
Chapter 1/2, Chapter 3, Chapter 4, Chapter 5/6, Chapter 8, Chapter 9, Chapter 10, Chapter 11, Chapter 12, Chapter 13, Chapter 14, Chapter 16, Chapter 17, Chapter 18, Chapter 19, Chapter 20, Chapter 21, Chapter 23, Chapter 26, Chapter 27, Chapter 28, Chapter 29.
Additional Information
Sample 1040 Syllabus/Homework/Schedule Spring 2018 (word format)
Course Coordinator for Faculty Resources
Contact Dr. Erik Heiny, Erik.Heiny@uvu.edu
