Time and Motion In a physics experiment at Doane College, a soccer ball was thrown upward from the bed of a moving truck. The table below lists the time (sec) that has lapsed from the throw and the corresponding height (m) of the soccer ball.
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b. Based on the result from part (a), what do you conclude about a linear correlation between time and height?

Linear correlation refers to the relationship between two variables where a change in one variable is associated with a proportional change in another. This relationship can be quantified using the correlation coefficient, which ranges from -1 to 1. A value close to 1 indicates a strong positive correlation, while a value close to -1 indicates a strong negative correlation. A value around 0 suggests no linear correlation.

A scatter plot is a graphical representation of two variables, where each point represents an observation in the dataset. It helps visualize the relationship between the variables, making it easier to identify patterns, trends, or correlations. In the context of the soccer ball experiment, plotting time against height can reveal whether a linear relationship exists between these two variables.

Regression analysis is a statistical method used to determine the relationship between a dependent variable and one or more independent variables. In this case, it can help quantify how height (dependent variable) changes with time (independent variable). By fitting a regression line to the data, one can assess the strength and nature of the correlation, providing insights into the dynamics of the soccer ball's motion.