STAT 216 Learning Outcomes by Topic

By the end of this course, students should be able to...

Data Literacy

Describe the statistical investigation process
Identify observational units, variables, and variable types in a statistical study
Develop skills needed to evaluate, analyze, prioritize and synthesize information in research articles
Understand and explain the effects of multiple testing
Understand and explain the role variability plays in statistics
Demonstrate an awareness of ethical issues associated with sound statistical practice

Explain the purpose of random sampling and its effect on scope of inference
Explain the purpose of random assignment and its effect on scope of inference
Identify whether a study is observational or an experiment
Identify potential types of sampling bias in a study (selection, response, non-response)
Identify confounding variables in observational studies and explain why they are confounding

Understand and explain the role of randomness in designing studies and drawing conclusions
Recognize and simulate probabilities as long-run frequencies
Construct two-way tables to evaluate conditional and unconditional probabilities

Identify and create appropriate summary statistics and plots given a data set or research question
Interpret the following summary statistics in context: median, lower quartile, upper quartile, standard deviation, inter-quartile range, coefficient of determination, regression line slope
Given a plot or set of plots, describe and compare the distribution(s) of a single quantitative variable (center, variability, shape, outliers)
Given a plot or set of plots, describe the association between two quantitative variables (form, direction, strength, outliers)

Create and interpret the following summary statistics and plots:

Summary statistics for a single categorical variable: frequencies, relative frequencies
Summary statistics for a single quantitative variable: mean, median, percentiles, standard deviation, inter-quartile range, range, 5-number summary
Summary statistics for association between two quantitative variables: correlation, coefficient of determination (R-squared), regression line
Plots for a single categorical variable: bar plot
Plots for association between two categorical variables: segmented bar plot, mosaic plot
Plots for a single quantitative variable: dotplot, boxplot, histogram, density plot
Plots for association between two quantitative variables: scatterplot
Plots for association between one quantitative and one categorical variable: side-by-side boxplots; stacked histograms, density plots or dotplots
Multivariable plots (e.g., scatterplot with factor)

General Inference Concepts

Identify the sample and population of interest
Recognize the difference between statistics and parameters and their symbols
Describe the scope of inference for a study
Understand and quantify sampling variability
Understand how different factors of a study (e.g., sample size) affect power, p-values and confidence intervals
Explain the difference between practical importance and statistical significance
Identify the two possible explanations (one assuming the null hypothesis, and one assuming the alternative hypothesis) for a relationship seen in sample data
Identify and describe in context the consequences of a Type I and Type II error

The Five Scenarios

For each of the five scenarios:

Given a research question, construct the null and alternative hypotheses in words and using appropriate statistical symbols
Describe and perform simulation-based hypothesis tests
Calculate and carry-out theory-based hypothesis tests
Interpret and evaluate a p-value
Calculate and interpret a standardized statistic
Construct and interpret a theory-based confidence interval
Use a confidence interval to determine the conclusion of a hypothesis test

Conduct exploratory data analyses and inferential statistical analyses in R in a reproducible manner through R Markdown