### Topics

- Linear Regression
- Korrelasjon
- Residuals
- Outlier
- Data

- Linear Regression
- Korrelasjon
- Residuals
- Outlier
- Data

- Interpret r (the correlation coefficient) as data points are added, moved, or removed.
- Interpret the sum of the squared residuals while manually fitting a line.
- Interpret the sum of the squared residuals of a best-fit line as a data point is added, moved, or removed.
- Compare the sum of the squared residuals between a manually fitted line and the best-fit line.
- Determine if a linear fit is appropriate.

HSS-ID.C.8

Compute (using technology) and interpret the correlation coefficient of a linear fit.HSS-ID.B.6c

Fit a linear function for a scatter plot that suggests a linear association.HSS-ID.B.6b

Informally assess the fit of a function by plotting and analyzing residuals.HSS-ID.B.6a

Fit a function to the data; use functions fitted to data to solve problems in the context of the data. Use given functions or choose a function suggested by the context. Emphasize linear, quadratic, and exponential models.HSS-ID.B.6

Represent data on two quantitative variables on a scatter plot, and describe how the variables are related.8.SP.A.3

Use the equation of a linear model to solve problems in the context of bivariate measurement data, interpreting the slope and intercept.*For example, in a linear model for a biology experiment, interpret a slope of 1.5 cm/hr as meaning that an additional hour of sunlight each day is associated with an additional 1.5 cm in mature plant height*.8.SP.A.2

Know that straight lines are widely used to model relationships between two quantitative variables. For scatter plots that suggest a linear association, informally fit a straight line, and informally assess the model fit by judging the closeness of the data points to the line.8.SP.A.1

Construct and interpret scatter plots for bivariate measurement data to investigate patterns of association between two quantities. Describe patterns such as clustering, outliers, positive or negative association, linear association, and nonlinear association.

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