Difference between revisions of "Stat 202 Discussion"
From Sean_Carver
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=== Pair of Variables Descriptive Statistics === | === Pair of Variables Descriptive Statistics === | ||
| + | * 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 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. | ||
=== Design of Experiments === | === Design of Experiments === | ||
Revision as of 19:50, 31 October 2018
Contents
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
- 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.
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...]
