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38. A researcher wishes to see if the total number of infections that occurred in 3 groups of randomly selected hospitals is the same. At ?=0.05 is there enough evidence to reject the claim that the number of infections in the 3 groups of hospitals is the same. (Use Kruskal-Wallis Test). Group A | Group B | Group C 546 | 450 | 100 310 | 200 | 110 950 | 90 | 150 175 | 210 | 160

          38. A researcher wishes to see if the total number of infections that occurred in 3 groups of randomly selected hospitals is the same. At ?=0.05 is there enough evidence to reject the claim that the number of infections in the 3 groups of hospitals is the same. (Use Kruskal-Wallis Test).
Group A | Group B | Group C
546 | 450 | 100
310 | 200 | 110
950 | 90 | 150
175 | 210 | 160
        
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38. A researcher wishes to see if the total number of infections that occurred in 3 groups of randomly selected hospitals is the same. At ?=0.05 is there enough evidence to reject the claim that the number of infections in the 3 groups of hospitals is the same. (Use Kruskal-Wallis Test).
Group A | Group B | Group C
546 | 450 | 100
310 | 200 | 110
950 | 90 | 150
175 | 210 | 160

Added by Silvia B.

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Elementary Statistics a Step by Step Approach
Elementary Statistics a Step by Step Approach
Allan G. Bluman 9th Edition
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38. A researcher wishes to see if the total number of infections that occurred in 3 groups of randomly selected hospitals is the same. At α=0.05 is there enough evidence to reject the claim that the number of infections in the 3 groups of hospitals is the same. (Use Kruskal-Wallis Test). Group A 546 310 950 175 Group B 450 200 90 210 Group C 100 110 150 160
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38. A researcher wishes to see if the total number of infections that occurred in 3 groups of randomly selected hospitals is the same. At α=0.05 is there enough evidence to reject the claim that the number of infections in the 3 groups of hospitals is the same. (Use Kruskal-Wallis Test). Group A 546 310 950 175 Group B 450 200 90 210 Group C 100 110 150 160

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A medical investigation claims that the average number of infections per week at a hospital in Pennsylvania is not 19.1. A random sample of 32 weeks had a mean number of 17.7 infections, and a sample standard deviation is 3.8. Is there enough evidence to support the investigator's claim? Use a 10 % level of significance to test this claim.

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Transcript

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00:01 Alright, so for step one here, what we want to do is combine the data into two columns.
00:15 Basically, one column we'll be using for keeping track of which sample the data originated from, and one column will be keeping track of the values.
00:22 Once we have that done, as we can see here, we have our different samples, and the corresponding values arranged into one set of two columns.
00:33 For the second step here, we want to sort the data by value, tracking sample as appropriate.
00:51 So just to be clear here by what i mean when i say tracking samples, when we look at our data, it would appear that this value of 90 is our smallest value.
01:00 So when we create our new table of samples and values, we have sample to value 90 is the smallest value.
01:09 Then, in order, the next would be sample 3, value 100.
01:13 Sample 3, value 110, sample three value 150, sample 3 value 160, sample 1, value 175, sample 2, value 200, sample 2, value 210, sample 1, value 310, sample 2, value 450, sample 1, value 4, or 546, sample 1, value 950.
01:36 Then for our third step, we want to rank the results, making sure that we adjust for ties, but we can see that there are no ties here.
01:51 So, simply, the rank would be 1, 2, 3, 4, 5, 6, 7, 9, 10, 11, 12.
02:00 Then, for our fourth step, we'd want to re -sort the data, not resort, pardon me, re -sort, or just we'd want to sort the data by sample again.
02:20 So now that we have that, we can see that we have all of the ranks for each one of the values, but we have all of our data sorted into the individual samples again, because what we'll want to do is, for the fifth step, find the sum of ranks for each sample.
02:48 So i'm just going to bring something on screen here for doing some calculations, so we'd have r1, as we'd expect, well, we sum up the ranks for sample 1.
03:00 So that's 6 plus 9 plus 11 plus 12.
03:02 So r1 is 38.
03:07 For r2, you can see that that would be 1 plus 7 plus 8 plus 10.
03:13 So that's 26, and then for r3, that's 2 plus 3 plus 4 plus 5.
03:18 So that's 14.
03:19 So we have r2 is equal to 26, and r3 is equal to 14.
03:27 Now, for the sake of our hypothesis test, i'll state explicitly that our null hypothesis is going to be that the samples come from populations with the same median...
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