Table 6.4 shows a sample of the maximum capacity (maximum number of spectators) of sports stadiums. The table does not Include horse-racing or motor-racing stadiums. Table 6.4 a. Calculate the sample mean and the sample standard deviation for the maximum capacity of sports stadiums (the data). b. Construct a histogram. c. Draw a smooth curve through the midpoints of the tops of the bars of the histogram. d. In words, describe the shape of your histogram and smooch curve. e. Let the sample mean approximate p and the sample standard deviation approximate a. The distribution of X can then be approximated by X ∼ ____________). f. Use the distribution in part e to calculate the probability that the maximum capacity of sports stadiums Is less than 67,000 spectators. g. Determine the cumulative relative frequency that the maximum capacity of sports stadiums is less than 67,000 spectators. Hint: Order the data and count the sports stadiums that have a maximum capacity less than 67,000. Divide by the total number of sports stadiums in the sample. h. Why aren’t the answers to part f and part g exactly the same? 40.000 40.000 45.050 45.500 46.249 48.134 49.133 50.071 50.096 50.466 50.832 51.100 51.500 51.900 52.000 52.132 52.200 52.530 52.692 53,864 54.000 55.000 55.000 55.000 55.000 55,000 55.000 55.082 57.000 58.008 59.680 60.000 60.000 60.492 60.580 62.380 62.872 64.035 65.000 65.050 65.647 66.000 66.161 67.428 68.349 68.976 69.372 70.107 70.585 71.594 72.000 72.922 73.379 74.500 75.025 76.212 78.000 80.000 80.000 82.300
Table 6.4 shows a sample of the maximum capacity (maximum number of spectators) of sports stadiums. The table does not Include horse-racing or motor-racing stadiums. Table 6.4 a. Calculate the sample mean and the sample standard deviation for the maximum capacity of sports stadiums (the data). b. Construct a histogram. c. Draw a smooth curve through the midpoints of the tops of the bars of the histogram. d. In words, describe the shape of your histogram and smooch curve. e. Let the sample mean approximate p and the sample standard deviation approximate a. The distribution of X can then be approximated by X ∼ ____________). f. Use the distribution in part e to calculate the probability that the maximum capacity of sports stadiums Is less than 67,000 spectators. g. Determine the cumulative relative frequency that the maximum capacity of sports stadiums is less than 67,000 spectators. Hint: Order the data and count the sports stadiums that have a maximum capacity less than 67,000. Divide by the total number of sports stadiums in the sample. h. Why aren’t the answers to part f and part g exactly the same? 40.000 40.000 45.050 45.500 46.249 48.134 49.133 50.071 50.096 50.466 50.832 51.100 51.500 51.900 52.000 52.132 52.200 52.530 52.692 53,864 54.000 55.000 55.000 55.000 55.000 55,000 55.000 55.082 57.000 58.008 59.680 60.000 60.000 60.492 60.580 62.380 62.872 64.035 65.000 65.050 65.647 66.000 66.161 67.428 68.349 68.976 69.372 70.107 70.585 71.594 72.000 72.922 73.379 74.500 75.025 76.212 78.000 80.000 80.000 82.300
Table 6.4 shows a sample of the maximum capacity (maximum number of spectators) of sports stadiums. The table does not Include horse-racing or motor-racing stadiums.
Table 6.4
a. Calculate the sample mean and the sample standard deviation for the maximum capacity of sports stadiums (the data).
b. Construct a histogram.
c. Draw a smooth curve through the midpoints of the tops of the bars of the histogram.
d. In words, describe the shape of your histogram and smooch curve.
e. Let the sample mean approximate p and the sample standard deviation approximate a. The distribution of X can then be approximated by X
∼
____________).
f. Use the distribution in part e to calculate the probability that the maximum capacity of sports stadiums Is less than 67,000 spectators.
g. Determine the cumulative relative frequency that the maximum capacity of sports stadiums is less than 67,000 spectators. Hint: Order the data and count the sports stadiums that have a maximum capacity less than 67,000. Divide by the total number of sports stadiums in the sample.
h. Why aren’t the answers to part f and part g exactly the same?
40.000
40.000
45.050
45.500
46.249
48.134
49.133
50.071
50.096
50.466
50.832
51.100
51.500
51.900
52.000
52.132
52.200
52.530
52.692
53,864
54.000
55.000
55.000
55.000
55.000
55,000
55.000
55.082
57.000
58.008
59.680
60.000
60.000
60.492
60.580
62.380
62.872
64.035
65.000
65.050
65.647
66.000
66.161
67.428
68.349
68.976
69.372
70.107
70.585
71.594
72.000
72.922
73.379
74.500
75.025
76.212
78.000
80.000
80.000
82.300
Features Features Normal distribution is characterized by two parameters, mean (µ) and standard deviation (σ). When graphed, the mean represents the center of the bell curve and the graph is perfectly symmetric about the center. The mean, median, and mode are all equal for a normal distribution. The standard deviation measures the data's spread from the center. The higher the standard deviation, the more the data is spread out and the flatter the bell curve looks. Variance is another commonly used measure of the spread of the distribution and is equal to the square of the standard deviation.
The following data represent total ventilation measured in liters of air per minute per square meter of body area for two independent (and randomly chosen) samples.
Analyze these data using the appropriate non-parametric hypothesis test
each column represents before & after measurements on the same individual. Analyze with the appropriate non-parametric hypothesis test for a paired design.
Need a deep-dive on the concept behind this application? Look no further. Learn more about this topic, statistics and related others by exploring similar questions and additional content below.