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Here is a description from Minitab of the strength datagiven in Exercise 13.


Variable N Mean Median TrMean StDev SE Mean
strength 153 135.39 135.40 135.41 4.59 0.37
Variable Minimum Maximum Q1Q3
strength 122.20 147.70 132.95 138.25

  1. Comment on any interesting features (the quartiles and fourths are virtually identical here).
  2. Construct a boxplot of the data based on the quartiles, and comment on what you see.

Short Answer

Expert verified

a. Since the values of mean, median and trimmed mean are identical, the distribution of strength is symmetrical.

b. Three outliers exist in the data which indicate that the sample observations are distant from the rest of the data.

Step by step solution

01

Given information

The Minitab output is provided that include the following:

Sample size, \(N = 153\)

Sample mean, \(\bar x = 135.39\)

Sample median, \(\tilde x = 135.40\)

Trimmed mean, \({\bar x_{Tr}} = 135.41\)

Standard deviation, \(s = 4.59\)

SE mean = 0.37

Minimum, min = 122.20

Maximum, max = 147.70

Lower fourth, \({Q_1} = 132.95\)

Upper fourth, \({Q_3} = 138.25\)

02

Comment on the observations from the given data

From the Minitab output, the values of sample mean, sample median and trimmed mean are approximately identical to each other.The data's minimum and maximum values are found to be evenly spaced from the median of the data. This shows the good amount of symmetry present in the given data. The standard deviation is low which tells that the data values are close to the mean of the data and less variability.

This provides additional support to the fact that the data distribution is symmetrical.

03

Construct a comparative box plot for the given data

Following are the steps to make boxplot by hand:

  1. Draw a plot line of range 120 to 150.
  2. Draw three vertical lines that consists of first quartile, second quartile and third quartile and make two horizontal lines to make it in rectangular form like a box for strength.
  3. Draw whiskers on both sides of four boxplots and set the minimum and maximum value with respect to the obtained lower fence and upper fence.

04

Draw conclusion from the obtained box-plot of strength

The left whisker of the boxplot is slightly bigger than the right whisker indicate that, it is slightly right-skewed. Since, there are three outliers exist so the observations are distant from rest of the data. These outliers are minimum observation 122.20, and two maximum observations 147.7 and 147.7.

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Most popular questions from this chapter

The accompanying frequency distribution of fracture strength (MPa) observations for ceramic bars fired in aparticular kiln appeared in the article 鈥淓valuating TunnelKiln Performance鈥 (Amer. Ceramic Soc. Bull., Aug.1997: 59鈥63).

Class81-<83 83-<85 85-<87 87-<89 89-<91

Frequency6 7 17 30 43

Class91-<93 93-<95 95-<97 97-<99

Frequency28 22 13 3

  1. Construct a histogram based on relative frequencies, and comment on any interesting features.
  2. What proportion of the strength observations are at least 85? Less than 95?

c. Roughly what proportion of the observations are less than 90?

Two airplanes are flying in the same direction in adjacent parallel corridors. At time \({\rm{t = 10}}\), the first airplane is \({\rm{10}}\)km ahead of the second one. Suppose the speed of the first plane (km/hr.) is normally distributed with mean \({\rm{520\;}}\)and standard deviation \({\rm{10}}\) and the second plane鈥檚 speed is also normally distributed with mean and standard deviation \({\rm{500\; and\; 10}}\), respectively.

a. What is the probability that after \({\rm{2hr}}{\rm{. }}\)of flying, the second plane has not caught up to the first plane?

b. Determine the probability that the planes are separated by at most \({\rm{10km\; after\; 2hr}}{\rm{. }}\)

The three measures of center introduced in this chapter are the mean, median, and trimmed mean. Two additional measures of center that are occasionally used are the midrange,which is the average of the smallest and largest observations, and the midfourth,which is the average of the two fourths. Which of these five measures of center are resistant to the effects of outliers and which are not? Explain your reasoning.

A mutual fund is a professionally managed investment scheme that pools money from many investors and invests in a variety of securities. Growth funds focus primarily on increasing the value of investments, whereas blended funds seek a balance between current income and growth. Here is data on the expense ratio (expenses as a % of assets, from www .morningstar.com) for samples of 20 large-cap balanced

funds and 20 large-cap growth funds (鈥渓argecap鈥 refers to the sizes of companies in which the funds invest; the population sizes are 825 and 762,

respectively):

Bl 1.03 1.23 1.10 1.64 1.30

1.27 1.25 0.78 1.05 0.64

0.94 2.86 1.05 0.75 0.09

0.79 1.61 1.26 0.93 0.84

Gr 0.52 1.06 1.26 2.17 1.55

0.99 1.10 1.07 1.81 2.05

0.91 0.79 1.39 0.62 1.52

1.02 1.10 1.78 1.01 1.15

a. Calculate and compare the values of\(\bar x\),\(\tilde x\), and sfor the two types of funds.

b. Construct a comparative boxplot for the two types of funds, and comment on interesting features.

The sample data \({x_1},{x_2}, \ldots ,{x_n}\) sometimes represents a time series, where \({x_t} = {\rm{the observed value of a response variable x at time t}}\). Often the observed series shows a great deal of random variation, which makes it difficult to study longer-term behavior. In such situations, it is desirable to produce a smoothed version of the series. One technique for doing so involves exponential smoothing. The value of a smoothing constant \(\alpha \) is chosen \(\left( {0 < \alpha < 1} \right)\).Then with \({{\bf{x}}_t} = {\bf{smoothed}}{\rm{ }}{\bf{value}}{\rm{ }}{\bf{at}}{\rm{ }}{\bf{time}}{\rm{ }}{\bf{t}}\), we set \({\bar x_1} = {x_1}\), and for \(t = 2,{\rm{ }}3,...,n,\)\({x_t} = a{x_t} + \left( {1 - \alpha } \right){\bar x_{t - 1}}\).

a. Consider the following time series in which \({x_t} = {\rm{temperature}}\left( {8F} \right)\)of effluent at a sewage treatment plant on dayt:

47, 54, 53, 50, 46, 46, 47, 50, 51, 50, 46, 52, 50, 50

Plot each \({x_t}\)against t on a two-dimensional coordinate system (a time-series plot). Does there appear to be any pattern?

b. Calculate the \({\bar x_t}s\) using\(\;\alpha = 0.1\). Repeat using \(\;\alpha = 0.5\). Which value of a gives a smoother \({x_t}\)series?

c. Substitute \({x_{t - 1}} = a{x_{t - 1}} + \left( {1 - \alpha } \right){\bar x_{t - 2}}\)on the right-hand side of the expression for \({x_t}\), then substitute \({\bar x_{t - 2}}\)in terms of \({x_{t - 2}}\) and \({\bar x_{t - 3}}\), and so on. On how many of the values \({x_t},{x_{t - 1}}{\rm{,}} \ldots ,{x_1}\)does \({\bar x_t}\)depend? What happens to the coefficient on \({x_{t - k}}\) ask increases?

d. Refer to part (c). If t is large, how sensitive is x t to the initialization \({\bar x_1} = {x_1}\)? Explain.(Note: A relevant reference is the article 鈥淪imple Statistics for Interpreting Environmental Data,鈥漌ater Pollution Control Fed. J., 1981: 167鈥175.)

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