ast reactions: In a study of reaction times, the time to respond to a visual stimulus (x) and the time to respond to an auditory timulus (y) were recorded for each of 6 subjects. Times were measured in thousandths of a second. The results are presented in he following table. Visual Auditory 125 134 115 124 125 126 105 105 95 138 100 152 Send data to Excel Use the P-value method to test H:B,-0 versus H,: B,<0. Can you conclude that visual response is useful in predicting auditory response? Use the a=0.01 level of significance and TI-84 Plus calculator.

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Fast reactions: In a study of reaction times, the time to respond to a visual stimulus (x) and the time to respond to an auditory
stimulus (y) were recorded for each of 6 subjects. Times were measured in thousandths of a second. The results are presented in
the following table.
Visual
Auditory
125
134
115
124
125
126
105
105
95
138
100
152
Send data to Excel
Use the P-value method to test H,: B,-0 versus H: B,<0. Can you conclude that visual response is useful in predicting
auditory response? Use the a=0.01 level of significance and TI-84 Plus calculator.
Compute the least-squares regression line for predicting auditory (y) from visual (x). Round the slope and y-intercept
values to at least four decimal places.
Regression line equation: y
Transcribed Image Text:Fast reactions: In a study of reaction times, the time to respond to a visual stimulus (x) and the time to respond to an auditory stimulus (y) were recorded for each of 6 subjects. Times were measured in thousandths of a second. The results are presented in the following table. Visual Auditory 125 134 115 124 125 126 105 105 95 138 100 152 Send data to Excel Use the P-value method to test H,: B,-0 versus H: B,<0. Can you conclude that visual response is useful in predicting auditory response? Use the a=0.01 level of significance and TI-84 Plus calculator. Compute the least-squares regression line for predicting auditory (y) from visual (x). Round the slope and y-intercept values to at least four decimal places. Regression line equation: y
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