Multi-variable decision-making
Clearly describe how to leverage additional variables or additional outside data to make a logical argument, and identify potential risks of overdoing it.
K–2 Competencies
Classroom resources
Data Science Starter Kit Module 4: Drawing Conclusions - Interpreting Problems and Results
Welcome to one of the most critical skills in data science—learning how to draw valid conclusions from data and understanding what those conclusions can and cannot tell us. This module focuses on the thinking skills that separate good data science from misleading claims.🔗
Interpreting Problems and Results isn’t about complex statistical tests or advanced mathematics. It’s about developing the intellectual honesty and critical thinking skills to say, “Based on this data, here’s what we can reasonably conclude, here’s what we’re not sure about, and here’s what we still need to investigate.”
3–5 Competencies
Describe patterns in two-variable data, such as data that show trends that increase or decrease, or relationships shown in different types of graphs. e.g., side-by-side bar charts and line graphs
Classroom resources
Data Science Starter Kit Module 4: Drawing Conclusions - Interpreting Problems and Results
Welcome to one of the most critical skills in data science—learning how to draw valid conclusions from data and understanding what those conclusions can and cannot tell us. This module focuses on the thinking skills that separate good data science from misleading claims.🔗
Interpreting Problems and Results isn’t about complex statistical tests or advanced mathematics. It’s about developing the intellectual honesty and critical thinking skills to say, “Based on this data, here’s what we can reasonably conclude, here’s what we’re not sure about, and here’s what we still need to investigate.”
6–8 Competencies
Distinguish direct vs. inverse relationships in multivariate data, such as associations between two categorical groups within the same visualization.
Use color to differentiate categories in a scatterplot and identify patterns in their relationships.
Calculate and compare the slopes and intercepts of multiple trend lines within the same graph to analyze differences between categories and their relationships.
Classroom resources
Data Science Starter Kit Module 4: Drawing Conclusions - Interpreting Problems and Results
Welcome to one of the most critical skills in data science—learning how to draw valid conclusions from data and understanding what those conclusions can and cannot tell us. This module focuses on the thinking skills that separate good data science from misleading claims.🔗
Interpreting Problems and Results isn’t about complex statistical tests or advanced mathematics. It’s about developing the intellectual honesty and critical thinking skills to say, “Based on this data, here’s what we can reasonably conclude, here’s what we’re not sure about, and here’s what we still need to investigate.”
9–10 Competencies
Use computer software to explore how adding additional numerical variables to a linear model changes the interpretation of the results.
Use computer software to analyze the relationship between two or more numerical variables by interpreting the strength and direction (e.g., positive, negative, none) of the association using computed values.
Classroom resources
Data Science Starter Kit Module 4: Drawing Conclusions - Interpreting Problems and Results
Welcome to one of the most critical skills in data science—learning how to draw valid conclusions from data and understanding what those conclusions can and cannot tell us. This module focuses on the thinking skills that separate good data science from misleading claims.🔗
Interpreting Problems and Results isn’t about complex statistical tests or advanced mathematics. It’s about developing the intellectual honesty and critical thinking skills to say, “Based on this data, here’s what we can reasonably conclude, here’s what we’re not sure about, and here’s what we still need to investigate.”
11–12 Competencies
Use computer software to analyze the relationship between an independent and dependent variable in a linear model by changing the number and combination of dependent variables.
Evaluate how changes to the number and combination of dependent variables affect the model by interpreting R-squared and regression coefficients.
Explore how polynomials of different degrees fit scatterplots.
Analyze how increasing or decreasing the degree of a polynomial can lead to potential overfitting or underfitting the data.
Classroom resources
Data Science Starter Kit Module 4: Drawing Conclusions - Interpreting Problems and Results
Welcome to one of the most critical skills in data science—learning how to draw valid conclusions from data and understanding what those conclusions can and cannot tell us. This module focuses on the thinking skills that separate good data science from misleading claims.🔗
Interpreting Problems and Results isn’t about complex statistical tests or advanced mathematics. It’s about developing the intellectual honesty and critical thinking skills to say, “Based on this data, here’s what we can reasonably conclude, here’s what we’re not sure about, and here’s what we still need to investigate.”
Advanced Competencies
Analyze and interpret the regression coefficients to understand the effect of the categories on the model.
Create an “ideal” multi-variable model for real-world data in a computer-based software that explains as much variance as possible, without overfitting a model. Justify how you have found the “ideal” model by comparing R^2, covariance, and the number of variables chosen in relation to their real-world context.
Use computer software to incorporate categorical variables into a linear regression model.
Classroom resources
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