traffic safety company publishes reports about motorcycle fatalities and helmet use. In the first accompanying data table, the distribution shows the proportion of atalities by location of injury for motorcycle accidents. The second data table shows the location of injury and fatalities for 2064 riders not wearing a helmet. Complet arts (a) and (b) below. Click the icon to view the tables. Distribution of fatalities by location of injury = Click the icon to view the chi-square table of critical values. H,: The distribution of fatal injuries for riders not wearing a helmet Full data set E Compute the expected counts for each fatal injury. Proportion of fatalities by location of injury for motorcycle accidents Location of Multiple locations Abdomen/ Lumbar/ Spin Location of injury Observed Count Expected Co Head Neck Thorax injury Proportion Multiple Locations 1026 0.570 0.310 0.030 0.060 0.030 Head 867 Location of injury and fatalities for 2064 riders not wearing a helmet Neck 37 Location of Multiple locations Thorax 86 Head Neck Thorax Abdomen/ injury Lumbar/ Spin Abdomen/Lumbar/Spine "Round to two decimal places as needed.) 48 Number 1026 867 37 86 48
Continuous Probability Distributions
Probability distributions are of two types, which are continuous probability distributions and discrete probability distributions. A continuous probability distribution contains an infinite number of values. For example, if time is infinite: you could count from 0 to a trillion seconds, billion seconds, so on indefinitely. A discrete probability distribution consists of only a countable set of possible values.
Normal Distribution
Suppose we had to design a bathroom weighing scale, how would we decide what should be the range of the weighing machine? Would we take the highest recorded human weight in history and use that as the upper limit for our weighing scale? This may not be a great idea as the sensitivity of the scale would get reduced if the range is too large. At the same time, if we keep the upper limit too low, it may not be usable for a large percentage of the population!
What js the expected counts?
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A traffic safety company publishes reports about motorcycle fatalities and helmet use. In the first accompanying data table, the distribution shows the proportion of
fatalities by location of injury for motorcycle accidents. The second data table shows the location of injury and fatalities for 2064 riders not wearing a helmet. Complete
parts (a) and (b) below.
Click the icon to view the tables.
Distribution of fatalities by location of injury
Click the icon to view the chi-square table of critical values.
H,: The distribution of fatal injuries for riders not wearing a helmet
Full data set
Compute the expected counts for each fatal injury.
Proportion of fatalities by location of injury for motorcycle accidents
Location of
Multiple
locations
Location of injury
Head
Abdomen/
Observed Count
Expected Co
Neck
Thorax
injury
Proportion
Lumbar/ Spine
Multiple Locations
1026
0.570
0.310
0.030
0.060
0.030
Head
867
Location of injury and fatalities for 2064 riders not wearing a helmet
Neck
37
Location of
Multiple
locations
Abdomen/
Lumbar/ Spine
Thorax
86
Head
Neck
Thorax
injury
Number
Abdomen/Lumbar/Spine
48
1026
867
37
86
48
(Round to two decimal places as needed.)
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