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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 The histogram will look approximately like a normal distribution because the sample size is large, and the Central Limit Theorem applies. The histogram will look approximately like a normal distribution because the number of samples is large, and the Central Limit Theorem applies. The histogram will appear to be right skewed. The histogram will appear to be left skewed. 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 () relative to the sample standard error of length-of stay value in Sample B ()?       Not enough information given to determine relationship between the two standard errors. Suppose we took 5,000 random samples from the California all-discharge data set, each sample containing 100 discharges. For each of the 5,000 samples, the sample mean was computed.  A histogram was then created with the 5,000 sample mean values. Which of the following statements most likely describes this histogram? The histogram will look approximately like a normal distribution because the size of each sample is large, and the Central Limit Theorem applies. The histogram will look approximately like a normal distribution because the number of samples is large, and the Central Limit Theorem applies. The histogram will appear to be right skewed. The histogram will appear to be left skewed. The histogram will look like a uniform distribution

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 The histogram will look approximately like a normal distribution because the sample size is large, and the Central Limit Theorem applies. The histogram will look approximately like a normal distribution because the number of samples is large, and the Central Limit Theorem applies. The histogram will appear to be right skewed. The histogram will appear to be left skewed. 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 () relative to the sample standard error of length-of stay value in Sample B ()?       Not enough information given to determine relationship between the two standard errors. Suppose we took 5,000 random samples from the California all-discharge data set, each sample containing 100 discharges. For each of the 5,000 samples, the sample mean was computed.  A histogram was then created with the 5,000 sample mean values. Which of the following statements most likely describes this histogram? The histogram will look approximately like a normal distribution because the size of each sample is large, and the Central Limit Theorem applies. The histogram will look approximately like a normal distribution because the number of samples is large, and the Central Limit Theorem applies. The histogram will appear to be right skewed. The histogram will appear to be left skewed. The histogram will look like a uniform distribution

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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.

  1. 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
    1. The histogram will look approximately like a normal distribution because the sample size is large, and the Central Limit Theorem applies.
    2. The histogram will look approximately like a normal distribution because the number of samples is large, and the Central Limit Theorem applies.
    3. The histogram will appear to be right skewed.
    4. The histogram will appear to be left skewed.
    5. The histogram will look like a uniform distribution
  2. 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 () relative to the sample standard error of length-of stay value in Sample B ()?
    1.  
    2.  
    3.  
    4. Not enough information given to determine relationship between the two standard errors.
  • Suppose we took 5,000 random samples from the California all-discharge data set, each sample containing 100 discharges. For each of the 5,000 samples, the sample mean was computed.  A histogram was then created with the 5,000 sample mean values. Which of the following statements most likely describes this histogram?
    1. The histogram will look approximately like a normal distribution because the size of each sample is large, and the Central Limit Theorem applies.
    2. The histogram will look approximately like a normal distribution because the number of samples is large, and the Central Limit Theorem applies.
    3. The histogram will appear to be right skewed.
    4. The histogram will appear to be left skewed.
    5. The histogram will look like a uniform distribution
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