The following data represent petal lengths (in cm) for independent random samples of two species of iris.Petal length (in cm) of Iris virginica: x1; n1 = 365.25.86.16.15.15.55.35.56.95.04.96.04.86.15.65.15.64.85.45.15.15.95.25.75.44.56.45.35.56.75.74.94.85.75.15.1Petal length (in cm) of Iris setosa: x2; n2 = 381.41.81.41.51.51.61.41.11.21.41.71.01.71.91.61.41.51.41.21.31.51.31.61.91.41.61.51.41.61.21.91.51.61.41.31.71.51.5 (a)Use a calculator with mean and standard deviation keys to calculate x1, s1, x2, and s2. (Round your answers to four decimal places.)x1=s1=x2=s2=(b)Let μ1 be the population mean for x1 and let μ2 be the population mean for x2. Find a 99% confidence interval for μ1 − μ2. (Round your answers to two decimal places.)lower limitupper limit

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Description: The following data represent petal lengths (in cm) for independent random samples of two species of iris.Petal length (in cm) of Iris virginica: x1; n1 = 365.25.86.16.15.15.55.35.56.95.04.96.04.86.15.65.15.64.85.45.15.15.95.25.75.44.56.45.35.56.75.74

The Given samples: n1=36 x1= 5.2 5.8 6.1 6.1 5.1 5.5 5.3 5.5 6.9 5.0 4.9 6.0 4.8 6.1 5.6 5.1…

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MATLAB: An Introduction with Applications

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The following data represent petal lengths (in cm) for independent random samples of two species of iris.

Petal length (in cm) of Iris virginica: x₁; n₁ = 36

5.2	5.8	6.1	6.1	5.1	5.5	5.3	5.5	6.9	5.0	4.9	6.0	4.8	6.1	5.6	5.1
5.6	4.8	5.4	5.1	5.1	5.9	5.2	5.7	5.4	4.5	6.4	5.3	5.5	6.7	5.7	4.9
4.8	5.7	5.1	5.1

Petal length (in cm) of Iris setosa: x₂; n₂ = 38

1.4	1.8	1.4	1.5	1.5	1.6	1.4	1.1	1.2	1.4	1.7	1.0	1.7	1.9	1.6	1.4
1.5	1.4	1.2	1.3	1.5	1.3	1.6	1.9	1.4	1.6	1.5	1.4	1.6	1.2	1.9	1.5
1.6	1.4	1.3	1.7	1.5	1.5

(a)

Use a calculator with mean and standard deviation keys to calculate x₁, s₁, x₂, and s₂. (Round your answers to four decimal places.)

x₁=s₁=x₂=s₂=

(b)

Let μ₁ be the population mean for x₁ and let μ₂ be the population mean for x₂. Find a 99% confidence interval for μ₁ − μ₂. (Round your answers to two decimal places.)

lower limitupper limit

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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