Difference between revisions of "Stat 202 Discussion"

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(Part I)
(Part I)
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== Broad Objectives ==
 
== Broad Objectives ==
  
=== Part I ===
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=== Single Variable Descriptive Statistics ===
  
 
* Understand the traditional way of structuring data (datasets, tables, cases, variables, values).
 
* Understand the traditional way of structuring data (datasets, tables, cases, variables, values).
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* Be able to describe the distribution of a categorical variable (bar plot, pie chart, frequency table).
 
* 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 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 (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.
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* 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.
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=== Pair of Variables Descriptive Statistics ===
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* Know what it means for a pair of quantitative variables to fit a linear models with scatter.
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=== Design of Experiments ===
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=== Probability ===
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* Understand and use set notation.
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* Understand and apply the rules of probability.
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=== Sampling Distributions ===
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=== Inference ===

Revision as of 19:19, 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

Probability

  • Understand and use set notation.
  • Understand and apply the rules of probability.

Sampling Distributions

Inference