The following data set represents the number of new computer accounts registered during ten consecutivedays:43,37,50,51,58,52,4 5,45,58,130(a) Compute the mean, median, IQR, and standard deviation(b) Check for outliers using the 1.5(IQR) rule, and indicate which data points are outliers.(c) Remove the detected outliers and compute the new mean, median, IQR, and standard deviation.(d) Make a conclusion about the effect of outliers on the basic descriptive sta

Outliers have great effect on the mean and standard deviation of the data set

Step-by-step explanation:

Mean =(43+37+50+51+58+52+45+45+58+130)/10

Mean= 579/10

Mean = 57.9

Arranging in ascending order

= 37,43,45,45,50,51,52,58,58,130

Median= (50+51)/2

Median= 101/2

Median= 50.5

IQR= (130-37)/2

IQR= 93/2

IQR= 46.5

Standard deviation

=√(((37-57.9)²+(43-57.9)²+(45-57.9)²+(45-57.9)²+(50-57.9)²+(51-57.9)²+(52-57.9)²+(58-57.9)²+(58-57.9)²+(130-57.9)²)/10)

Standard deviation= 25.1

1.5*(46.5)= 69.75

The number more than 69.75 is 130 and it's the outlier

Without outlier

Mean= (43+37+50+51+58+52+45+45+58)/9

Mean = 449/9

Mean = 49.88

Arranging in ascending order

= 37,43,45,45,50,51,52,58,58

Median= 50

IQR= (58-37)/2

IQR=21/2

IQR=10.5

Standard deviation

answer

the correct answer is

explanation

the given function is

the excluded values are obtained by solving:

we factor to obtain:

the excluded values are

d. 24+-0.114

step-by-step explanation:

the question requires us to calculate the approximate 95% confidence interval for the mean number of ounces of ketchup per bottle in the sample given the following statistics;

sample mean = 24

standard deviation, σ = 0.4

sample size , n = 49

the confidence interval for a population mean is calculated using the formula;

sample mean ± z-score*(σ/sqrt(n))

the z-score associated with a 95% confidence interval, from the standard normal tables, is;

1.96

substituting these values into the formula;

24 ± 1.96*(0.4/7)

= 24 ± 0.112

from the alternatives given, choice d is closest to the above expression;