Sure! Here's a structured overview of Unit 5 vocabulary focused on Scatter Plots and Data Analysis, including definitions and examples for clarity.
Unit 5 Vocabulary: Scatter Plots and Data Analysis
| Key Terms | Meaning | Description | Picture/Example |
|----------------|-------------|------------------|----------------------|
| U5 L1: Data Modeling | Creating a representation of data | Data modeling involves using mathematical equations or visual graphs to represent relationships between variables. | 
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| U5 L2: Bivariate Data | Data involving two variables | Bivariate data consists of pairs of numerical values that are analyzed to understand the relationship between the two variables. | 
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| U5 L2: Scatter Plot | A graph showing relationships between variables | A scatter plot uses Cartesian coordinates to display values for typically two variables, which helps in identifying correlation. | 
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| U5 L2: Outlier | A data point that differs significantly | An outlier is a value that lies outside the overall pattern of a distribution, potentially affecting the analysis outcome. | 
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| U5 L2: Clustering | Grouping of data points | Clustering refers to a trend where data points gather close together in a scatter plot, indicating a potential correlation between values. | 
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| U5 L5: Linear/Nonlinear Association | The type of correlation between variables | A linear association involves a straight-line relationship, while a nonlinear association suggests a curve or other form of relationship. | 
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| U5 L5: Association/Correlation | Relationship strength and direction | - Positive Correlation: Both variables increase together.<br>- Negative Correlation: One variable increases as the other decreases.<br>- No Correlation: No apparent relationship between the variables. | 
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| U5 L5: Qualitative Variable | Non-numerical variable | A qualitative variable represents categories or qualities (e.g., colors, names) rather than quantities. | 
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| U5 L5: Quantitative Variable | Numerical variable | A quantitative variable represents measurable data or amounts (e.g., height, weight). | 
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| U5 L5: Line of Best Fit | A line that best represents data | A line of best fit minimizes the distance of the points from the line and is used for prediction in linear data sets. | 
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| U5 L6: Key points of trend lines | Important features of lines | - The y-intercept is the point where the line crosses the y-axis, indicating the value of y when x = 0.<br>- The x-intercept is the point where the line crosses the x-axis, indicating the value of x when y = 0. | 
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| U5 L6: Inference | Drawing conclusions from data | Inference involves making predictions or generalizations about a population based on sample data analysis. | 
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| U5 L7: Slope Interpretation | Understanding trend steepness | The slope of a trend line indicates the rate of change between the variables; a steeper slope represents a larger change in y for a given change in x. | 
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| U5 L7: Extrapolate | Estimating values beyond the data range | Extrapolation involves predicting unknown values based on extending the trend line beyond the given data points. | 
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| U5 L10: Interpreting a graph | Analyzing graph information | To interpret a graph, one looks at trends, shapes, and relationships among plotted points to derive meaningful insights. | 
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| U5 L10: Probabilistic vs Deterministic | Types of outcomes | - Probabilistic: Outcomes that are uncertain and based on probability (e.g., rolling a die).<br>- Deterministic: Outcomes that are predictable and not subject to chance (e.g., mathematical equations). | 
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Note: Replace the URLs with actual images or illustrations relevant to the definitions as needed for enhanced understanding.
