There were over 3.5 million hospital discharges in the year 2000 in the U.S. state of California. Patient length of stay summary statistics available on all reported year 2000 hospital discharges in California include a median length of stay of 3.0 days, a mean length of stay of 4.6 days, and a standard deviation of 4.5 days. Below is a histogram that shows the distribution of the length of stay, measured in days, for all hospital discharges in the year2000 in California. (the California all discharge data set). You may consider this the population distribution of hospital discharges for the year 2000 in California. If a random sample of 1,000 discharges were taken from the California all-dischargedatabase, and a histogram were made of patient length of stay for the sample, which of thefollowing is most likely true:a) The histogram will look approximately like a normal distributionbecause the sample size is large , and the Central Limit Theorem applies.b) The histogram will look approximately like a normal distributionbecause the number of samples is large, and the Central Limit Theoremapplies.c) The histogram will appear to be right skewed.d) The histogram will appear to be left skewed.e) The histogram will look like a uniform distributionSuppose we compared 2 random samples taken from the California all-discharge databasedescribed above. Sample A is a random sample with 100 discharges. Sample B is arandom sample with 2,000 discharges. What can be said about the relationship between thesample standard error in Sample A (SEA) relative to the sample standard error of length-of-stay value in Sample B (SEB)?a) SEA< SEBb) SEA> SEBc) SEA is exactly equal to SEBd) Not enough information given to determine relationship between the twostandard errors. 0 151015202530Hospital Length of Stay (Days)Percentage of Patients.2.1.34.

From the given data Median = 3.0 Mean = 4.6 Standard Deviation =4.5 are the descriptive statistics…

Answered: There were over 3.5 million hospital…

MATLAB: An Introduction with Applications

6th Edition

ISBN:9781119256830

Author:Amos Gilat

Publisher:Amos Gilat

Chapter1: Starting With Matlab

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There were over 3.5 million hospital discharges in the year 2000 in the U.S. state of California. Patient length of stay summary statistics available on all reported year 2000 hospital discharges in California include a median length of stay of 3.0 days, a mean length of stay of 4.6 days, and a standard deviation of 4.5 days. Below is a histogram that shows the distribution of the length of stay, measured in days, for all hospital discharges in the year
2000 in California. (the California all discharge data set). You may consider this the population distribution of hospital discharges for the year 2000 in California.

If a random sample of 1,000 discharges were taken from the California all-discharge
database, and a histogram were made of patient length of stay for the sample, which of the
following is most likely true:
a) The histogram will look approximately like a normal distribution
because the sample size is large , and the Central Limit Theorem applies.
b) The histogram will look approximately like a normal distribution
because the number of samples is large, and the Central Limit Theorem
applies.
c) The histogram will appear to be right skewed.
d) The histogram will appear to be left skewed.
e) The histogram will look like a uniform distribution
Suppose we compared 2 random samples taken from the California all-discharge database
described above. Sample A is a random sample with 100 discharges. Sample B is a
random sample with 2,000 discharges. What can be said about the relationship between the
sample standard error in Sample A (SEA) relative to the sample standard error of length-of-
stay value in Sample B (SEB)?
a) SEA< SEB
b) SEA> SEB
c) SEA is exactly equal to SEB
d) Not enough information given to determine relationship between the two
standard errors.

0 1
5
10
15
20
25
30
Hospital Length of Stay (Days)
Percentage of Patients
.2
.1
.3
4.

Definition Definition Middle value of a data set. The median divides a data set into two halves, and it also called the 50th percentile. The median is much less affected by outliers and skewed data than the mean. If the number of elements in a dataset is odd, then the middlemost element of the data arranged in ascending or descending order is the median. If the number of elements in the dataset is even, the average of the two central elements of the arranged data is the median of the set. For example, if a dataset has five items—12, 13, 21, 27, 31—the median is the third item in ascending order, or 21. If a dataset has six items—12, 13, 21, 27, 31, 33—the median is the average of the third (21) and fourth (27) items. It is calculated as follows: (21 + 27) / 2 = 24.

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