of 10 > 0 A In a study of exhaust emissions from school buses, the pollution int
ID: 3369639 • Letter: O
Question
of 10 > 0 A In a study of exhaust emissions from school buses, the pollution intake by passengers was determined for a sample of nine school buses used in the Southern California Air Basin. The pollution intake is the amount of exhaust emissions, in grams per person, that would be inhaled while traveling on the bus during its usual 18-mile trip on congested freeways from South Central LA to a magnet school in West LA. (As a reference, the average intake of motor emissions of carbon monoxide in the LA area is estimated to be about 0.000046 grams per person.) The amounts for the nine buses when driven with the windows open are given. 1.15 0.33 0.40 0.33 135 0.38 0.25 0.40 0.35 To aceces c Excel Minitab JMP SPSS TI R Mac-TXT PC-TXT CSV Crunchlt! (a) Make a stemplot. Are there outliers or strong skewness that would forbid use of the t procedures? O The stemplot does not indicate the presence of outliers. The sample is small and the stemplot is skewed, so the O The stemplot suggests the presence of outliers. The sample is small but the stemplot is not skewed, so the use of O The stemplot does not indicate the presence of outliers. The sample is small but the stemplot is not skeyrol O The stemplot suggests the presence of outliers. The sample is small and the stemplot is skewed, so the use of t. use of t procedures is not appropriate. procedures is appropriate. use of t procedures is appropriate. Question Source: Moore, The Basic Practice Of Statistics, Be /PublisiExplanation / Answer
Here R - code is
x=c(1.15,0.33,0.4,0.33,1.35,0.38,0.25,0.4,0.35)
stem(x) # Stem plot of all data
t.test(x,conf.level = 0.9) # For help in compute 90% C.I.
y=x[-c(1,5)]
stem(y) # Stem plot after removing outlires
t.test(y,conf.level = 0.9) # For help in compute 90% C.I.
And the output is:
> x=c(1.15,0.33,0.4,0.33,1.35,0.38,0.25,0.4,0.35)
> stem(x) # Stem plot of all data
The decimal point is at the |
0 | 3334444
0 |
1 | 24
>
> t.test(x,conf.level = 0.9) # For help in compute 90% C.I.
One Sample t-test
data: x
t = 4.0838, df = 8, p-value = 0.003516
alternative hypothesis: true mean is not equal to 0
90 percent confidence interval:
0.2989534 0.7988243
sample estimates:
mean of x
0.5488889
>
> y=x[-c(1,5)]
> stem(y) # Stem plot after removing outlires
The decimal point is 1 digit(s) to the left of the |
2 | 5
3 | 33
3 | 58
4 | 00
>
>
> t.test(y,conf.level = 0.9) # For help in compute 90% C.I.
One Sample t-test
data: y
t = 17.488, df = 6, p-value = 2.242e-06
alternative hypothesis: true mean is not equal to 0
90 percent confidence interval:
0.3098402 0.3873026
sample estimates:
mean of x
0.3485714
a) The stem plot indicate that some outliers are present in given data.
b) Now,
from output it is seen that;
lower bound (all data) =0.2989534
Upper bound (all data)=0.7988243
lower bound(outlier removes)=0.3098402
upper bound(outlier removed)=0.3873026
c) It is seen that removing outliers results in reducing confidence interval.
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