Difference between revisions of "Stat 202 Discussion"

Revision as of 19:42, 31 October 2018

Broad Objectives

Single Variable Descriptive Statistics

• 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.

Pair of Variables Descriptive Statistics

• Know what it means for a pair of quantitative variables to fit a linear models with scatter.

Design of Experiments

[Coming soon...]

Probability

• 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.

Sampling Distributions

[Coming soon ...]

Inference

[Coming soon...]