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1-4 Curve Fitting with Linear Models Warm Up Write the equation of the line passing through each pair of passing points in slope-intercept form. 1. (5, –1), (0, –3) 2. (8, 5), (–8, 7) Use the equation y = –0.2x + 4. Find x for each given value of y. 3. y = 7 Holt McDougal Algebra 2 4. y = 3.5 1-4 Curve Fitting with Linear Models Objectives Fit scatter plot data using linear models with and without technology. Use linear models to make predictions. Vocabulary regression correlation line of best fit correlation coefficient Holt McDougal Algebra 2 1-4 Curve Fitting with Linear Models Researchers, such as anthropologists, are often interested in how two measurements are related. The statistical study of the relationship between variables is called regression. A scatter plot is helpful in understanding the form, direction, and strength of the relationship between two variables. Correlation is the strength and direction of the linear relationship between the two variables. Holt McDougal Algebra 2 1-4 Curve Fitting with Linear Models If there is a strong linear relationship between two variables, a line of best fit, or a line that best fits the data, can be used to make predictions. Helpful Hint Try to have about the same number of points above and below the line of best fit. Holt McDougal Algebra 2 1-4 Curve Fitting with Linear Models Example 1: Meteorology Application Albany and Sydney are about the same distance from the equator. Make a scatter plot with Albany’s temperature as the independent variable. Name the type of correlation. Then sketch a line of best fit and find its equation. Holt McDougal Algebra 2 1-4 Curve Fitting with Linear Models Example 1 Continued o Holt McDougal Algebra 2 1-4 Curve Fitting with Linear Models The correlation coefficient r is a measure of how well the data set is fit by a model. Holt McDougal Algebra 2 1-4 Curve Fitting with Linear Models Example 2: Anthropology Application Anthropologists can use the femur, or thighbone, to estimate the height of a human being. The table shows the results of a randomly selected sample. Holt McDougal Algebra 2 1-4 Curve Fitting with Linear Models Example 2 Continued Find the correlation coefficient r and the line of best fit. Interpret the slope of the line of best fit in the context of the problem. Enter the data into lists L1 and L2 on a graphing calculator. Use the linear regression feature by pressing STAT, choosing CALC, and selecting 4:LinReg. The equation of the line of best fit is h ≈ 2.91l + 54.04. Holt McDougal Algebra 2 1-4 Curve Fitting with Linear Models Example 2 Continued c. A man’s femur is 41 cm long. Predict the man’s height. The equation of the line of best fit is h ≈ 2.91l + 54.04. Use the equation to predict the man’s height. For a 41-cm-long femur, h ≈ 2.91(41) + 54.04 Substitute 41 for l. h ≈ 173.35 The height of a man with a 41-cm-long femur would be about 173 cm. Holt McDougal Algebra 2 1-4 Curve Fitting with Linear Models Example 3: Meteorology Application Find the following for this data on average temp and rainfall for eight months in Boston, MA. a. Make a scatter plot of the data with temperature as the independent variable. b. Find the correlation coefficient and the equation of the line of best fit. Draw the line of best fit on your scatter plot. c. Predict the temperature when the rainfall is 86 mm. How accurate do you think your prediction is? Holt McDougal Algebra 2 1-4 Curve Fitting with Linear Models HW: pg 5-15, 19-21 [6,7,13-15 GC] points used to find LOBF Holt McDougal Algebra 2 For 5,8,&12, identify your