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Managers of an outdoor coffee stand in Coast City are examing the relationship between (hot) coffee sales and daily temperature, hoping to be able to predict a day's total coffee sales from the maximum temperature that day. The bivariate data values for the coffee sales (denoted by y, in dollars) and the maximum temperature (denoted by x, in degrees Fahrenheit) for each of fifteen randomly selected days during the past year are given below. These data are plotted in the scatter plot in Figure 1. Temperature, x (in degrees Fahrenheit) Coffee sales, y (in dollars) 81.6 1534.6 39.5 2264.1 2400+ 51.5 2179.5 2200- 75.6 1531.0 72.5 1941.4 2000- 73.7 1670.5 1800. 69.6 1699.0 47.9 1983.3 1600- 47.9 2167.9 1400- 73.8 2020.3 54.9 1617.4 1200 58.7 1922.9 44.9 1803.9 38.6 1986.2 Figure 1 61.4 1812.6 Send data to calculator Send data to Excel The value of the sample correlation coefficient r for these data is approximately -0.643. Answer the following. Carry your intermediate computations to at least four decimal places, and round your answers as specified below. (If necessary, consult a list of formulas.) What is the value of the slope of the least-squares regression line for these data? Round your answer to at least two decimal II places. What is the value of the y-intercept of the least-squares regression line for these data? Round your answer to at least two decimal places.

Managers of an outdoor coffee stand in Coast City are examing the relationship between (hot) coffee sales and daily temperature, hoping to be able to predict a day's total coffee sales from the maximum temperature that day. The bivariate data values for the coffee sales (denoted by y, in dollars) and the maximum temperature (denoted by x, in degrees Fahrenheit) for each of fifteen randomly selected days during the past year are given below. These data are plotted in the scatter plot in Figure 1. Temperature, x (in degrees Fahrenheit) Coffee sales, y (in dollars) 81.6 1534.6 39.5 2264.1 2400+ 51.5 2179.5 2200- 75.6 1531.0 72.5 1941.4 2000- 73.7 1670.5 1800. 69.6 1699.0 47.9 1983.3 1600- 47.9 2167.9 1400- 73.8 2020.3 54.9 1617.4 1200 58.7 1922.9 44.9 1803.9 38.6 1986.2 Figure 1 61.4 1812.6 Send data to calculator Send data to Excel The value of the sample correlation coefficient r for these data is approximately -0.643. Answer the following. Carry your intermediate computations to at least four decimal places, and round your answers as specified below. (If necessary, consult a list of formulas.) What is the value of the slope of the least-squares regression line for these data? Round your answer to at least two decimal II places. What is the value of the y-intercept of the least-squares regression line for these data? Round your answer to at least two decimal places.

MATLAB: An Introduction with Applications
MATLAB: An Introduction with Applications
6th Edition
ISBN:9781119256830
Author:Amos Gilat
Publisher:Amos Gilat
Chapter1: Starting With Matlab
Section: Chapter Questions
Problem 1P
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Managers of an outdoor coffee stand in Coast City are examining the relationship between (hot) coffee sales and daily temperature, hoping to be able to predict a day's total coffee sales from the maximum temperature that day. The bivariate data values for the coffee sales (denoted by \( y \), in dollars) and the maximum temperature (denoted by \( x \), in degrees Fahrenheit) for each of fifteen randomly selected days during the past year are given below. These data are plotted in the scatter plot in Figure 1. ### Data Table | Temperature, \( x \) (in degrees Fahrenheit) | Coffee Sales, \( y \) (in dollars) | |----------------------------------------------|------------------------------------| | 81.6 | 1534.6 | | 39.5 | 2264.1 | | 51.5 | 2179.5 | | 75.6 | 1531.0 | | 72.5 | 1941.4 | | 73.7 | 1670.5 | | 69.6 | 1699.0 | | 47.9 | 1983.3 | | 47.9 | 2167.9 | | 73.8 | 2020.3 | | 54.9 | 1617.4 | | 58.7 | 1922.9 | | 44.9 | 1803.9 | | 38.6 | 1986.2 | | 61.4 | 1812.6 | ### Scatter Plot Description (Figure 1) The scatter plot graphically represents the relationship between temperature \( x \) and coffee sales \( y \). The horizontal axis represents temperature in degrees Fahrenheit, while the vertical axis represents coffee sales in dollars. Each point in the plot corresponds to one of the data pairs from the table, showing how coffee sales tend to change with temperature. ### Analysis The value of the sample correlation coefficient \( r \) for these data is approximately \( -0.643 \). ### Additional Calculations - **Slope of the least-squares regression line:** The task is to find the slope and round it to at least two decimal places. - **Y-intercept of the least-squares regression line:** Calculate
Transcribed Image Text:Managers of an outdoor coffee stand in Coast City are examining the relationship between (hot) coffee sales and daily temperature, hoping to be able to predict a day's total coffee sales from the maximum temperature that day. The bivariate data values for the coffee sales (denoted by \( y \), in dollars) and the maximum temperature (denoted by \( x \), in degrees Fahrenheit) for each of fifteen randomly selected days during the past year are given below. These data are plotted in the scatter plot in Figure 1. ### Data Table | Temperature, \( x \) (in degrees Fahrenheit) | Coffee Sales, \( y \) (in dollars) | |----------------------------------------------|------------------------------------| | 81.6 | 1534.6 | | 39.5 | 2264.1 | | 51.5 | 2179.5 | | 75.6 | 1531.0 | | 72.5 | 1941.4 | | 73.7 | 1670.5 | | 69.6 | 1699.0 | | 47.9 | 1983.3 | | 47.9 | 2167.9 | | 73.8 | 2020.3 | | 54.9 | 1617.4 | | 58.7 | 1922.9 | | 44.9 | 1803.9 | | 38.6 | 1986.2 | | 61.4 | 1812.6 | ### Scatter Plot Description (Figure 1) The scatter plot graphically represents the relationship between temperature \( x \) and coffee sales \( y \). The horizontal axis represents temperature in degrees Fahrenheit, while the vertical axis represents coffee sales in dollars. Each point in the plot corresponds to one of the data pairs from the table, showing how coffee sales tend to change with temperature. ### Analysis The value of the sample correlation coefficient \( r \) for these data is approximately \( -0.643 \). ### Additional Calculations - **Slope of the least-squares regression line:** The task is to find the slope and round it to at least two decimal places. - **Y-intercept of the least-squares regression line:** Calculate
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