A: Assumptions • Since Select an answer information was collected from each object, what conditions do we need to check? Check all that apply. Oois known ON>20n OG is unknown On(p) ≥ 10 Onp > 10 On > 30 or normal population no outliers in the data On(1-p) ≥ 10 On(1-p) ≥ 10 outliers in the data Check those assumptions: 1. Is the value of a known?? 2. Given the following modified boxplot (If using a screenreader, use technology to generate the modified boxplot and answer the question below.): 56 80 108 131 40 60 80 100 120 140 pulse rate after running one minute in bpm 166 160 180 a How can we identify outliers on a modified boxplot? Select an answer <
A: Assumptions • Since Select an answer information was collected from each object, what conditions do we need to check? Check all that apply. Oois known ON>20n OG is unknown On(p) ≥ 10 Onp > 10 On > 30 or normal population no outliers in the data On(1-p) ≥ 10 On(1-p) ≥ 10 outliers in the data Check those assumptions: 1. Is the value of a known?? 2. Given the following modified boxplot (If using a screenreader, use technology to generate the modified boxplot and answer the question below.): 56 80 108 131 40 60 80 100 120 140 pulse rate after running one minute in bpm 166 160 180 a How can we identify outliers on a modified boxplot? Select an answer <
A: Assumptions • Since Select an answer information was collected from each object, what conditions do we need to check? Check all that apply. Oois known ON>20n OG is unknown On(p) ≥ 10 Onp > 10 On > 30 or normal population no outliers in the data On(1-p) ≥ 10 On(1-p) ≥ 10 outliers in the data Check those assumptions: 1. Is the value of a known?? 2. Given the following modified boxplot (If using a screenreader, use technology to generate the modified boxplot and answer the question below.): 56 80 108 131 40 60 80 100 120 140 pulse rate after running one minute in bpm 166 160 180 a How can we identify outliers on a modified boxplot? Select an answer <
The table below contains pulse rates after running for 1 minute, collected from a random sample of females who drink alcohol. Find a 95% confidence interval for the mean pulse rate after exercise of all women who do drink alcohol.
pulse rate after running one minute in bpm
133
134
100
111
61
90
80
130
138
104
79
166
127
72
108
87
56
131
66
67
129
149
135
130
91
108
Transcribed Image Text:How can we identify outliers on a modified boxplot? Select an answer
Are there any outliers?
Select an answer
3. n=
Is it reasonable to assume the population is normally distributed?
Select an answer
N: Name the procedure
The conditions are met to use a Select an answer
I: Interval and point estimate
The symbol and value of the point estimate are as follows:
(Round answer to 1)
?v=
which is ? ✓
The interval estimate for? is as follows:
C: Conclusion
(Round endpoints to 1 decimal places and use units exactly as stated in the problem)
• We are
bpm
Select an answer
% confident that
bpm and
bpm
vis between
Transcribed Image Text:A: Assumptions
• Since Select an answer information was collected from each object, what conditions do we need
to check? Check all that apply.
Oo is known
ON > 20n
Oo is unknown
In(p) > 10
Onp > 10
On 30 or normal population
no outliers in the data
On(1-p) ≥ 10
On(1-p) ≥ 10
outliers in the data
Check those assumptions:
1. Is the value of a known??
2. Given the following modified boxplot (If using a screenreader, use technology to generate the
modified boxplot and answer the question below.):
56
80
108
131
166
1
160
180
40
60
80
100
120 140
pulse rate after running one minute in bpm
How can we identify outliers on a modified boxplot? Select an answer
Definition Definition Measure of central tendency that is the average of a given data set. The mean value is evaluated as the quotient of the sum of all observations by the sample size. The mean, in contrast to a median, is affected by extreme values. Very large or very small values can distract the mean from the center of the data. Arithmetic mean: The most common type of mean is the arithmetic mean. It is evaluated using the formula: μ = 1 N ∑ i = 1 N x i Other types of means are the geometric mean, logarithmic mean, and harmonic mean. Geometric mean: The nth root of the product of n observations from a data set is defined as the geometric mean of the set: G = x 1 x 2 ... x n n Logarithmic mean: The difference of the natural logarithms of the two numbers, divided by the difference between the numbers is the logarithmic mean of the two numbers. The logarithmic mean is used particularly in heat transfer and mass transfer. ln x 2 − ln x 1 x 2 − x 1 Harmonic mean: The inverse of the arithmetic mean of the inverses of all the numbers in a data set is the harmonic mean of the data. 1 1 x 1 + 1 x 2 + ...
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