Here is a comprehensive vocabulary chart for Unit 5: Scatter Plots and Data Analysis, incorporating meanings, example descriptions, and structured layout for clarity.
Unit 5: Scatter Plots and Data Analysis Vocabulary
| Key Terms | Meaning - Description | Picture | Example - All work shown |
|----------------------------------------------------|----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|------------------------------------------|------------------------------------|
| U5 L1 Data Modeling | Data modeling is the process of creating a representation of data and its relationships to understand and analyze it better. This often includes the use of mathematical models to predict future values or trends based on existing data. | 
| Example: Creating a regression model |
| U5 L2 Bivariate Data | Bivariate data contains two different variables. This type of data can show how one variable affects or relates to another, often represented in a scatter plot. | | Example: Height vs. Weight data |
| U5 L2 Scatter Plot | A scatter plot is a graphical representation of bivariate data using dots to represent the values of two different variables. Each point on the plot corresponds to one observation. | | Example: Plotting test scores vs study hours |
| U5 L2 Outlier | An outlier is a data point that significantly differs from other observations in a dataset. Outliers can indicate variability in measurements, experimental errors, or a novelty. | | Example: One student's exceptionally high score |
| U5 L2 Clustering | Clustering refers to the grouping of data points that are closer to one another based on their characteristics. In scatter plots, clustering can indicate correlations or patterns within the data. | | Example: Group of students scoring similarly |
| U5 L5 Linear/Nonlinear Association | A linear association occurs when the data points in a scatter plot form a pattern that can be approximated by a straight line, whereas nonlinear association occurs when the relationship between the data points cannot be approximated with a straight line. | | Example: Linear: Height vs Age; Nonlinear: Age vs Reaction Time |
| U5 L5 Association/Correlation | Correlation measures the relationship between two variables. <br> Positive: As one variable increases, the other tends to increase. <br> Negative: As one variable increases, the other tends to decrease. <br> None: No apparent relationship between the two variables. | | Example: Positive: Hours studied vs test score |
| U5 L5 Qualitative Variable | A qualitative variable, also known as a categorical variable, represents categories or distinct groups. It cannot be measured numerically but instead described by characteristics. | | Example: Colors of cars |
| U5 L5 Quantitative Variable | A quantitative variable can be measured and expressed numerically. It can be continuous (any value within a range) or discrete (specific countable values). | | Example: Age or height |
| U5 L5 Line of Best Fit | A line of best fit, or trend line, is a straight line that best represents the data on a scatter plot. It can help identify the relationship between the two variables and make predictions. | | Example: Average trend in study hours vs grades |
| U5 L6 Interpreting key points of trend lines | The y-intercept represents the value of the dependent variable when the independent variable (x) equals zero. The x-intercept represents the value of the independent variable when the dependent variable (y) equals zero. These points provide context for the data trends. | | Example: y-intercept of 5 in a line shows y when x = 0 |
| U5 L6 Inference | Inference refers to the process of drawing conclusions or making predictions based on data analysis, often supported by statistical methods. The reliability of inferences depends on the data used and its representation. | | Example: Predicting future sales based on historical data |
| U5 L7 Interpreting the Slope of a trend line | The slope of a trend line indicates how much the dependent variable (y) is expected to increase or decrease with each unit increase in the independent variable (x). A positive slope means y increases with x, whereas a negative slope signifies that y decreases with x. | | Example: Slope of 2 means for every hour studied, test scores increase by 2 points |
| U5 L7 Extrapolate from a line of best fit | Extrapolation involves applying the trend observed from a dataset beyond the range of the data. It can lead to estimates of values that may not be reliable if the pattern changes beyond the observed data range. | | Example: Predicting income for an age outside the data range |
| U5 L10 How do you Interpret a graph? | Interpreting a graph involves analyzing the axes, labels, data points, trend lines, and context of the graph to understand the relationships and significance of the data visually presented. | | Example: Understanding trends in sales data over time |
| U5 L10 Probabilistic vs Deterministic | Probabilistic: Outcomes are uncertain; predictions include a range of likely results. <br> Deterministic: Outcomes are precisely determined by initial conditions; predictions yield a specific result without variation. | | Example: Weather forecasts (probabilistic) vs mechanical clock behavior (deterministic) |
This table allows for organized learning while also accommodating visual aids and examples. You can fill in the image URLs and examples with specific data from your course materials or lessons as appropriate.
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