Difference between revisions of "Stat 202 Discussion"
From Sean_Carver
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== Broad Objectives == | == Broad Objectives == | ||
| − | === | + | === 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. | + | * 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 === | ||
Revision as of 19:19, 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
- 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.
