Stat 202 Discussion

Broad Objectives

Understand the traditional way of structuring data (datasets, tables, cases, variables, values).
Be able to recognize the types of variables in a data set (quantitative, identifier, categorical (ordinal, nominal, binary)).
Understand that different analyses and displays are appropriate for different types of variables.
Understand the concept of a distribution of a variable (what values the variable takes and how often it takes those values).
Be able to describe the distribution of a single quantitative variable (histogram, box plot, QQ plot, shape, outliers, center, spread, modes, symmetry, skewness, normal/bell shaped, mean, median, standard deviation, Q1, Q3, IQR, percentiles).
Be able to describe the distribution of a categorical variable (bar plot, pie chart, frequency table).
Understand the concept of and apply transformations of a variable (e.g. z-score, change of units, log) and know the special properties of a linear transformation.
Understand what it means for data (a quantitative variable) to fit a Normal model with parameters (histogram, QQ-Plot, including typical noise) and know how to make predictions based on that assumption.

Describe the relationship between a categorical variable and a quantitative variable (series of histograms, side-by-side box plots).
Know what it means for a pair of quantitative variables to fit a linear models with scatter.
Know that correlation and regression are only appropriate for a pair of quantitative variables that fit a linear model with scatter.
Describe the linear relationship between quantitative variables with correlation and regression.

[Coming soon...]

Understand and use set notation (element of, subset of, union, intersection, complement of, null set, disjoint sets).
Understand and apply the terminology of probability (random phenomenon, sample space, outcomes, events, independent sets) and the rules/"axioms" of probability.
Understand the mathematical concept of a function to be able to apply it to the definition of a random variable.
Know and understand the definition of a random variable as a function mapping a sample space of a random phenomenon to real numbers, and be able to give important examples of random variables (coin toss, die toss, sampling, binomial).
Know how to compute the mean and standard deviation of a discrete random variable from its probability table.
Know how to apply formulas to compute means, variances, and standard deviations of sums, differences, and linear transformations of independent and correlated random variables.
Know the assumptions behind the use of a Binomial model, and recognize situations when this model is applicable.
Use the Binomial calculator to make predictions in StatCrunch; know how to use other StatCrunch calculators (geometric, Normal, Uniform) and when they apply.

[Coming soon ...]

[Coming soon...]