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Unit 5 Scatter Plots and Data Analysis Vocabulary Key Terms Meaning - Description - Picture Example - All work shown U5 L1 Data...
Unit 5 Scatter Plots and Data Analysis
Vocabulary
Key Terms
Meaning - Description - Picture
Example - All work shown
U5 L1
Data Modeling
U5 L2
Bivariate Data
U5 L2
Scatter Plot
U5 L2
Outlier
U5 L2
Clustering
U5 L5
Linear/NonLinear
Association
U5 L5
Association/
Correlation
Positive
Negative
None
U5 L5
Qualitative Variable
U5 L5
Quantitative Variable
U5 L5
Line of Best Fit
U5 L6
Interpreting key points of trend lines
The y-intercept represents the amount when x = 0.
The x-intercept represents the amount when y = 0.
U5 L6
Inference
U5 L7
Interpreting the Slope of a trend line or line of best fit
U5 L7
Extrapolate from a line of best fit
U5 L10
How do you Interpret a graph?
U5 L10
Probabilistic vs
Deterministic
Probabilistic
Deterministic
do the work
Vocabulary
Key Terms
Meaning - Description - Picture
Example - All work shown
U5 L1
Data Modeling
U5 L2
Bivariate Data
U5 L2
Scatter Plot
U5 L2
Outlier
U5 L2
Clustering
U5 L5
Linear/NonLinear
Association
U5 L5
Association/
Correlation
Positive
Negative
None
U5 L5
Qualitative Variable
U5 L5
Quantitative Variable
U5 L5
Line of Best Fit
U5 L6
Interpreting key points of trend lines
The y-intercept represents the amount when x = 0.
The x-intercept represents the amount when y = 0.
U5 L6
Inference
U5 L7
Interpreting the Slope of a trend line or line of best fit
U5 L7
Extrapolate from a line of best fit
U5 L10
How do you Interpret a graph?
U5 L10
Probabilistic vs
Deterministic
Probabilistic
Deterministic
do the work
Answers
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GPT-4o mini
### Unit 5: Scatter Plots and Data Analysis Vocabulary
---
#### U5 L1: Data Modeling
- **Meaning**: The process of creating a model that represents the relationship between variables using data.
- **Description**: Data modeling involves creating mathematical equations or visual representations to understand patterns and relationships in data.
- **Picture**: [Insert diagram showing a model with variables and a graph]
---
#### U5 L2: Bivariate Data
- **Meaning**: Data that involves two different variables.
- **Description**: Bivariate data is analyzed to see how two variables relate to one another, often visualized with scatter plots.
- **Picture**: [Insert example of a scatter plot showing two variables]
---
#### U5 L2: Scatter Plot
- **Meaning**: A graph that displays two quantitative variables on a Cartesian plane.
- **Description**: Each point represents an observation with an x (independent variable) and a y (dependent variable) value.
- **Picture**: [Insert a scatter plot example]
---
#### U5 L2: Outlier
- **Meaning**: A data point that differs significantly from other observations.
- **Description**: Outliers can skew the results of data analysis and may indicate variability in measurement or experimental errors.
- **Picture**: [Show a scatter plot with an outlier marked]
---
#### U5 L2: Clustering
- **Meaning**: When data points are grouped closely together in a scatter plot.
- **Description**: Clustering indicates a potential association between the variables and helps identify patterns.
- **Picture**: [Insert scatter plot with highlighted clusters]
---
#### U5 L5: Linear/Nonlinear Association
- **Linear Association**: When the data points in a scatter plot can be approximated by a straight line. There is a consistent increase or decrease between variables.
- **Nonlinear Association**: When the data points form a curve or another non-linear form and cannot be well represented by a straight line.
---
#### U5 L5: Association/Correlation
1. **Positive Correlation**: As one variable increases, the other also increases.
- **Picture**: [Insert scatter plot illustrating positive correlation]
2. **Negative Correlation**: As one variable increases, the other decreases.
- **Picture**: [Insert scatter plot illustrating negative correlation]
3. **No Correlation**: There is no discernible pattern in the relationship between the two variables.
- **Picture**: [Insert scatter plot illustrating no correlation]
---
#### U5 L5: Qualitative Variable
- **Meaning**: A variable that categorizes or describes an attribute or characteristic.
- **Description**: Qualitative variables are non-numerical and often represent categories, such as color or type.
- **Picture**: [Insert examples of qualitative variables]
---
#### U5 L5: Quantitative Variable
- **Meaning**: A variable that can be measured and expressed numerically.
- **Description**: Quantitative variables can be discrete (countable values) or continuous (measurable values).
- **Picture**: [Insert examples of quantitative variables]
---
#### U5 L5: Line of Best Fit
- **Meaning**: A straight line that best represents the data on a scatter plot.
- **Description**: The line minimizes the distance between itself and all the data points, providing a way to predict values.
- **Picture**: [Insert graph showing a line of best fit through a scatter plot]
---
#### U5 L6: Interpreting Key Points of Trend Lines
- **Y-Intercept**: The value of y when x = 0; represents the starting point of the trend line.
- **X-Intercept**: The value of x when y = 0; can indicate when the outcome variable is expected to reach zero.
---
#### U5 L6: Inference
- **Meaning**: Drawing conclusions about a population based on sample data.
- **Description**: Inference involves making predictions or generalizations based on the observed data.
---
#### U5 L7: Interpreting the Slope of a Trend Line or Line of Best Fit
- **Meaning**: The slope indicates the rate of change in y for a unit change in x.
- **Description**: A positive slope indicates a positive relationship, while a negative slope indicates a negative relationship.
---
#### U5 L7: Extrapolate from a Line of Best Fit
- **Meaning**: Estimating values outside the range of the data points based on the line of best fit.
- **Description**: Extrapolation should be done cautiously as it may lead to inaccuracies beyond observed data.
---
#### U5 L10: How do you Interpret a Graph?
- **Description**: Read the axes, identify the variables, look for trends, correlations, and anomalies, and assess the context of the data.
---
#### U5 L10: Probabilistic vs Deterministic
- **Probabilistic**: Outcomes are based on probability; there is uncertainty and variability in predictions.
- **Deterministic**: Outcomes are fixed and certain; the same input will always produce the same output.
---
### Example of All Work Shown
1. **Data Modeling**: Create a model for the following situation where x is the temperature in degrees and y is the number of ice creams sold.
2. **Bivariate Data**: Collect data on temperature (x) and ice cream sales (y).
3. **Scatter Plot**: Plot the points on a scatter plot with temperature on the x-axis and ice creams sold on the y-axis.
4. **Outlier**: Identify any points that are significantly different from the rest.
5. **Clustering**: Note if data points are clustered in certain temperature ranges.
6. **Association/Correlation**: Analyze and determine if the relationship is positive, negative, or none.
7. **Line of Best Fit**: Draw a line that best fits the data points. Calculate the slope to find the relationship.
Make use of graphs and tables to visualize and support your explanation.
---
#### U5 L1: Data Modeling
- **Meaning**: The process of creating a model that represents the relationship between variables using data.
- **Description**: Data modeling involves creating mathematical equations or visual representations to understand patterns and relationships in data.
- **Picture**: [Insert diagram showing a model with variables and a graph]
---
#### U5 L2: Bivariate Data
- **Meaning**: Data that involves two different variables.
- **Description**: Bivariate data is analyzed to see how two variables relate to one another, often visualized with scatter plots.
- **Picture**: [Insert example of a scatter plot showing two variables]
---
#### U5 L2: Scatter Plot
- **Meaning**: A graph that displays two quantitative variables on a Cartesian plane.
- **Description**: Each point represents an observation with an x (independent variable) and a y (dependent variable) value.
- **Picture**: [Insert a scatter plot example]
---
#### U5 L2: Outlier
- **Meaning**: A data point that differs significantly from other observations.
- **Description**: Outliers can skew the results of data analysis and may indicate variability in measurement or experimental errors.
- **Picture**: [Show a scatter plot with an outlier marked]
---
#### U5 L2: Clustering
- **Meaning**: When data points are grouped closely together in a scatter plot.
- **Description**: Clustering indicates a potential association between the variables and helps identify patterns.
- **Picture**: [Insert scatter plot with highlighted clusters]
---
#### U5 L5: Linear/Nonlinear Association
- **Linear Association**: When the data points in a scatter plot can be approximated by a straight line. There is a consistent increase or decrease between variables.
- **Nonlinear Association**: When the data points form a curve or another non-linear form and cannot be well represented by a straight line.
---
#### U5 L5: Association/Correlation
1. **Positive Correlation**: As one variable increases, the other also increases.
- **Picture**: [Insert scatter plot illustrating positive correlation]
2. **Negative Correlation**: As one variable increases, the other decreases.
- **Picture**: [Insert scatter plot illustrating negative correlation]
3. **No Correlation**: There is no discernible pattern in the relationship between the two variables.
- **Picture**: [Insert scatter plot illustrating no correlation]
---
#### U5 L5: Qualitative Variable
- **Meaning**: A variable that categorizes or describes an attribute or characteristic.
- **Description**: Qualitative variables are non-numerical and often represent categories, such as color or type.
- **Picture**: [Insert examples of qualitative variables]
---
#### U5 L5: Quantitative Variable
- **Meaning**: A variable that can be measured and expressed numerically.
- **Description**: Quantitative variables can be discrete (countable values) or continuous (measurable values).
- **Picture**: [Insert examples of quantitative variables]
---
#### U5 L5: Line of Best Fit
- **Meaning**: A straight line that best represents the data on a scatter plot.
- **Description**: The line minimizes the distance between itself and all the data points, providing a way to predict values.
- **Picture**: [Insert graph showing a line of best fit through a scatter plot]
---
#### U5 L6: Interpreting Key Points of Trend Lines
- **Y-Intercept**: The value of y when x = 0; represents the starting point of the trend line.
- **X-Intercept**: The value of x when y = 0; can indicate when the outcome variable is expected to reach zero.
---
#### U5 L6: Inference
- **Meaning**: Drawing conclusions about a population based on sample data.
- **Description**: Inference involves making predictions or generalizations based on the observed data.
---
#### U5 L7: Interpreting the Slope of a Trend Line or Line of Best Fit
- **Meaning**: The slope indicates the rate of change in y for a unit change in x.
- **Description**: A positive slope indicates a positive relationship, while a negative slope indicates a negative relationship.
---
#### U5 L7: Extrapolate from a Line of Best Fit
- **Meaning**: Estimating values outside the range of the data points based on the line of best fit.
- **Description**: Extrapolation should be done cautiously as it may lead to inaccuracies beyond observed data.
---
#### U5 L10: How do you Interpret a Graph?
- **Description**: Read the axes, identify the variables, look for trends, correlations, and anomalies, and assess the context of the data.
---
#### U5 L10: Probabilistic vs Deterministic
- **Probabilistic**: Outcomes are based on probability; there is uncertainty and variability in predictions.
- **Deterministic**: Outcomes are fixed and certain; the same input will always produce the same output.
---
### Example of All Work Shown
1. **Data Modeling**: Create a model for the following situation where x is the temperature in degrees and y is the number of ice creams sold.
2. **Bivariate Data**: Collect data on temperature (x) and ice cream sales (y).
3. **Scatter Plot**: Plot the points on a scatter plot with temperature on the x-axis and ice creams sold on the y-axis.
4. **Outlier**: Identify any points that are significantly different from the rest.
5. **Clustering**: Note if data points are clustered in certain temperature ranges.
6. **Association/Correlation**: Analyze and determine if the relationship is positive, negative, or none.
7. **Line of Best Fit**: Draw a line that best fits the data points. Calculate the slope to find the relationship.
Make use of graphs and tables to visualize and support your explanation.
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