To investigate the relationship between temperature and the number of visitors, the park ranger can create a scatter plot with temperature (°F) on the x-axis and number of visitors on the y-axis. By plotting the data points from the table on the scatter plot, the ranger can visually examine if there is a pattern or correlation between temperature and number of visitors.
Additionally, the ranger can calculate the correlation coefficient to quantify the strength and direction of the relationship between temperature and number of visitors. The correlation coefficient ranges from -1 to 1, where 1 indicates a perfect positive correlation, -1 indicates a perfect negative correlation, and 0 indicates no correlation. By analyzing the correlation coefficient, the ranger can determine if there is a significant relationship between temperature and number of visitors.
Furthermore, the ranger can conduct a regression analysis to create a regression line that best fits the data points on the scatter plot. The regression line can help predict the number of visitors based on the temperature, allowing the ranger to make informed decisions and forecasts for park management based on the weather conditions.
Overall, by utilizing statistical analysis techniques such as scatter plots, correlation coefficients, and regression analysis, the park ranger can gain insights into the relationship between temperature and number of visitors, enabling effective park management strategies.
Temperature (°F)
50
55
60
65
70
75
Number of visitors
420
637
824
1,185
1,210
1,23:
A park ranger is investigating the relationship between the outside temperature and the number of visitors to the park. The table gives recorded daily high temperatures, in degrees Fahrenheit (°F), and the corresponding number of visitors to the park for selected days.
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