Forty-six mountains in the Adirondacks of upstate New York are known as the High Peaks with...

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Forty-six mountains in the Adirondacks of upstate New York are known as the High Peaks with elevations near or above 4000 feet. Below is some R output from a linear regression model of Y= Time (expected trip time to hike the peak, in hours) on X= Ascent (in feet). > summary (time.1m) Call: 1m (formula = Time Ascent, data = HighPeaks) Coefficients: Estimate Std. Error t value Pr (>It]) (Intercept) 4.2100541 1.8661683 2.256 0.02909 * Ascent 0.0020805 0.0005909 3.521 0.00101 ** Residual standard error: 2.496 on 44 degrees of freedom Multiple R-squared: 0.2198, Adjusted R-squared: b.2021 F-statistic: 12.4 on 1 and 44 DF, p-value: 0.001014 1. Interpret the slope with appropriate units. 2. The 95% confidence interval for ẞ1, the coefficient of Ascent, is (0.00089, 0.00327). Interpret this interval using an increment of 1000 feet of Ascent. 3. Interpret the residual standard error. 4. Interpret the coefficient of determination. 5. Using this model, predict the hiking time for a mountain with an ascent of 4000 feet. Forty-six mountains in the Adirondacks of upstate New York are known as the High Peaks with elevations near or above 4000 feet. Below is some R output from a linear regression model of Y= Time (expected trip time to hike the peak, in hours) on X= Ascent (in feet). > summary (time.1m) Call: 1m (formula = Time Ascent, data = HighPeaks) Coefficients: Estimate Std. Error t value Pr (>It]) (Intercept) 4.2100541 1.8661683 2.256 0.02909 * Ascent 0.0020805 0.0005909 3.521 0.00101 ** Residual standard error: 2.496 on 44 degrees of freedom Multiple R-squared: 0.2198, Adjusted R-squared: b.2021 F-statistic: 12.4 on 1 and 44 DF, p-value: 0.001014 1. Interpret the slope with appropriate units. 2. The 95% confidence interval for ẞ1, the coefficient of Ascent, is (0.00089, 0.00327). Interpret this interval using an increment of 1000 feet of Ascent. 3. Interpret the residual standard error. 4. Interpret the coefficient of determination. 5. Using this model, predict the hiking time for a mountain with an ascent of 4000 feet.

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The slope of the regression line is 00020805 with units of hours per foot of ascent This means that ... View the full answer