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  • https://math.libretexts.org/Courses/Mission_College/Math_10%3A_Elementary_Statistics_(Kravets)/10%3A_Linear_Regression_and_Correlation/10.06%3A_Prediction
    After determining the presence of a strong correlation coefficient and calculating the line of best fit, you can use the least squares regression line to make predictions about your data.  The process...After determining the presence of a strong correlation coefficient and calculating the line of best fit, you can use the least squares regression line to make predictions about your data.  The process of predicting inside of the observed x values observed in the data is called interpolation. The process of predicting outside of the observed x-values observed in the data is called extrapolation.
  • https://math.libretexts.org/Courses/Mission_College/Math_10%3A_Elementary_Statistics_(Sklar)/12%3A_Linear_Regression_and_Correlation/12.05%3A_Prediction
    After determining the presence of a strong correlation coefficient and calculating the line of best fit, you can use the least squares regression line to make predictions about your data.  The process...After determining the presence of a strong correlation coefficient and calculating the line of best fit, you can use the least squares regression line to make predictions about your data.  The process of predicting inside of the observed x values observed in the data is called interpolation. The process of predicting outside of the observed x-values observed in the data is called extrapolation.
  • https://math.libretexts.org/Courses/Coastline_College/Math_C160%3A_Introduction_to_Statistics_(Lee)/03%3A_Linear_Regression_and_Correlation/3.06%3A_Prediction
    After determining the presence of a strong correlation coefficient and calculating the line of best fit, you can use the least squares regression line to make predictions about your data.  The process...After determining the presence of a strong correlation coefficient and calculating the line of best fit, you can use the least squares regression line to make predictions about your data.  The process of predicting inside of the observed x values observed in the data is called interpolation. The process of predicting outside of the observed x-values observed in the data is called extrapolation.
  • https://math.libretexts.org/Courses/Austin_Peay_State_University/Supplementary_Material_for_Math_Models/02%3A_Emperical_Modelsing/2.03%3A_Linear_Regression_and_Correlation/2.3.06%3A_Prediction
    After determining the presence of a strong correlation coefficient and calculating the line of best fit, you can use the least squares regression line to make predictions about your data.  The process...After determining the presence of a strong correlation coefficient and calculating the line of best fit, you can use the least squares regression line to make predictions about your data.  The process of predicting inside of the observed x values observed in the data is called interpolation. The process of predicting outside of the observed x-values observed in the data is called extrapolation.
  • https://math.libretexts.org/Under_Construction/Purgatory/Remixer_University/Username%3A_Matthew.Lathrop@heartland.edu/Introduction_to_Statistics_(Lathrop)_OFFICIAL/11%3A_Linear_Models/11.5%3A_Prediction
    After determining the presence of a strong correlation coefficient and calculating the line of best fit, you can use the least squares regression line to make predictions about your data.  The process...After determining the presence of a strong correlation coefficient and calculating the line of best fit, you can use the least squares regression line to make predictions about your data.  The process of predicting inside of the observed x values observed in the data is called interpolation. The process of predicting outside of the observed x-values observed in the data is called extrapolation.
  • https://math.libretexts.org/Courses/Heartland_Community_College/HCC%3A_Introduction_to_Statistics_(Lathrop)/11%3A_Linear_Models/11.5%3A_Prediction
    After determining the presence of a strong correlation coefficient and calculating the line of best fit, you can use the least squares regression line to make predictions about your data.  The process...After determining the presence of a strong correlation coefficient and calculating the line of best fit, you can use the least squares regression line to make predictions about your data.  The process of predicting inside of the observed x values observed in the data is called interpolation. The process of predicting outside of the observed x-values observed in the data is called extrapolation.
  • https://math.libretexts.org/Courses/Coastline_College/Math_C160%3A_Introduction_to_Statistics_(Tran)/03%3A_Linear_Regression_and_Correlation/3.06%3A_Prediction
    After determining the presence of a strong correlation coefficient and calculating the line of best fit, you can use the least squares regression line to make predictions about your data.  The process...After determining the presence of a strong correlation coefficient and calculating the line of best fit, you can use the least squares regression line to make predictions about your data.  The process of predicting inside of the observed x values observed in the data is called interpolation. The process of predicting outside of the observed x-values observed in the data is called extrapolation.
  • https://math.libretexts.org/Courses/Florida_SouthWestern_State_College/MGF_1131%3A_Mathematics_in_Context__(FSW)/02%3A_Modeling_in_Mathematics/2.02%3A_Exponential_Growth_Models
    This section explains exponential growth through various models, including fish populations, tuition rates, bank balances, and CO2 emissions. It highlights the differences between exponential and line...This section explains exponential growth through various models, including fish populations, tuition rates, bank balances, and CO2 emissions. It highlights the differences between exponential and linear growth, using formulas like P(t)=P0(1+r)t for predictions. The importance of accurate growth rate calculations and rounding effects is emphasized, alongside specific examples in different contexts, such as population predictions for cities.
  • https://math.libretexts.org/Courses/Mission_College/Math_10%3A_Elementary_Statistics_(Hwang)/03%3A_Linear_Regression_and_Correlation/3.05%3A_Prediction
    After determining the presence of a strong correlation coefficient and calculating the line of best fit, you can use the least squares regression line to make predictions about your data.  The process...After determining the presence of a strong correlation coefficient and calculating the line of best fit, you can use the least squares regression line to make predictions about your data.  The process of predicting inside of the observed x values observed in the data is called interpolation. The process of predicting outside of the observed x-values observed in the data is called extrapolation.

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