Property.
Section 1
Analyzing Lines of Fit
A line of fit (or regression line) is a straight line that best represents the overall trend in a scatterplot. When analyzing a line of fit, we examine how well it models the relationship between two variables by looking at how closely the data points cluster around the line. The line can be used to identify patterns, make predictions, and understand the strength of the relationship between variables.
Section 2
Residuals and Residual Analysis
A residual is the difference between an actual data point and the predicted value from a line of fit:
Section 3
Linear Regression Using Technology
Linear regression on a graphing calculator finds the line of best fit and correlation coefficient by entering data into lists and using the LinReg function to calculate optimal parameters.
Section 4
Interpolation
Using a regression line to estimate values between known data points is called interpolation.
Cocoa sales data is collected for temperatures between C and C. Using the regression line to predict sales at C is interpolation. We get cups.
A baby whale's length is recorded at birth (0 months) and 7 months. Using a linear model to estimate its length at 4 months is interpolation, because 4 is between 0 and 7.
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Section 1
Analyzing Lines of Fit
A line of fit (or regression line) is a straight line that best represents the overall trend in a scatterplot. When analyzing a line of fit, we examine how well it models the relationship between two variables by looking at how closely the data points cluster around the line. The line can be used to identify patterns, make predictions, and understand the strength of the relationship between variables.
Section 2
Residuals and Residual Analysis
A residual is the difference between an actual data point and the predicted value from a line of fit:
Section 3
Linear Regression Using Technology
Linear regression on a graphing calculator finds the line of best fit and correlation coefficient by entering data into lists and using the LinReg function to calculate optimal parameters.
Section 4
Interpolation
Using a regression line to estimate values between known data points is called interpolation.
Cocoa sales data is collected for temperatures between C and C. Using the regression line to predict sales at C is interpolation. We get cups.
A baby whale's length is recorded at birth (0 months) and 7 months. Using a linear model to estimate its length at 4 months is interpolation, because 4 is between 0 and 7.
Book overview
Jump across lessons in the current chapter without opening the full course modal.
Continue this chapter