Difference between revisions of "Stat 202 Discussion"
From Sean_Carver
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* 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 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). | * Be able to describe the distribution of a categorical variable (bar plot, pie chart, frequency table). | ||
| − | * Understand the concept of | + | * 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. | * 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. | ||
Revision as of 19:09, 31 October 2018
Broad Objectives
Part I
- 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 (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.
