It looks like you've listed a number of terms and concepts related to statistics and data analysis. Here’s a brief explanation of each term:
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Overestimates: An estimation that is greater than the actual value; skewed results that can lead to incorrect conclusions.
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Relative Frequency Tables: Tables that display the relative frequency of each category or value, expressed as a fraction or percentage of the total.
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Underestimates/Underestimate: An estimation that is less than the actual value; could imply a failure to capture the complete data.
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Conditional Relative Frequency: The relative frequency of a certain event given that another event has occurred, often used to analyze relationships between two variables.
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Extrapolate/Extrapolation: The process of estimating an unknown value by extending a known sequence of values or trends beyond the observed range.
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Univariate Data: Data that consists of observations on a single variable or characteristic.
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Scatterplot or Scatter Plot: A graphical representation that uses dots to represent the values obtained for two different variables (bivariate data).
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Residual: The difference between an observed value and the value predicted by a model; used to assess the accuracy of the model.
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Interpolate: The process of estimating unknown values within the range of known data points.
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Qualitative/Categorical Data: Data that can be categorized based on traits or characteristics, without a numerical value (e.g., colors, names).
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Negative Correlation: An inverse relationship between two variables, where an increase in one variable corresponds with a decrease in the other.
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Best of Fit Line or Best Fit Line: A straight line that best represents the data points in a scatterplot, used in regression analysis.
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Negative Linear Relationship: A relationship between two variables where one variable decreases as the other increases, represented by a downward-sloping line.
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Association: A relationship or correlation between two or more variables.
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Nonlinear Relationship: A relationship between two variables that does not form a straight line when graphed.
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Least Squares Regression: A statistical method used to determine the line of best fit that minimizes the sum of the squares of the residuals.
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Linear Regression: A method for modeling the relationship between a dependent variable and one or more independent variables, assuming linearity.
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Positive Correlation: A direct relationship where an increase in one variable corresponds with an increase in another variable.
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Marginal Relative Frequency: The sum of the relative frequencies of a single category within a two-way frequency table.
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Causation: A relationship between events where one event causes or has a direct effect on another.
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Linear Relationship: A relationship that can be described by a straight line, with a constant rate of change.
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Quantitative/Numerical Data: Data that consists of numerical values, often used for mathematical calculations (e.g., age, height).
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Two-way Frequency Table: A table that displays the frequency counts for two categorical variables.
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Bivariate Data: Data that involves two different variables or quantities that can be analyzed for relationships.
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Joint Relative Frequency: The relative frequency of a specific combination of outcomes in a two-way frequency table.
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ŷ (y-hat): A symbol used to represent predicted values in regression analysis.
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Correlation Coefficient (r-value): A statistical measure that describes the strength and direction of a relationship between two variables.
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Residual Plot: A graph that plots residuals on the vertical axis and independent variables on the horizontal axis, used to visualize variations in a regression model.
If you need more detailed explanations of any specific term or concept, feel free to ask!