HLT 540 Broyles Textbook Practice Exercise

HLT 540 Broyles Textbook Practice Exercise

HLT540

HLT 540 Grand Canyon Week 5 Discussion 1

Discuss the concept of the normal distribution, why it is important, and what you think it means.

 

HLT 540 Grand Canyon Week 5 Discussion 2

When students talk about “grading on the curve,” how does that apply to the normal distribution?

 

HLT 540 Grand Canyon Week 5 Assignment 1

Broyles Textbook Practice Exercise

Details:

Complete “Practice Exercise 1” (page 157) and “Practice Exercise 11” (page 180) in the textbook. For the data set listed, use Excel to extract the mean and standard deviation for the sample of lengths of stay for cardiac patients. Use the following Excel steps:

1) Enter the data set into Excel.

2) Click on the Data tab at the top.

3) Highlight your data set with your mouse.

4) Click on the Data Analysis tab at the top right.

5) Click on Descriptive Statistics in the analysis tool list.

6) Find the mean and standard deviation of the data sets.

7) Send the results to instructor via e-mail, along with your analysis of the description of the data set.

APA format is not required, but solid academic writing is expected.

 

HLT 540 Grand Canyon Week 5 Assignment 2

Coyne and Messina Articles Part 3 Spearman Coefficient Review

Details:

1) Write a paper (750-1,000 words) regarding the use of the Spearman rank correlation coefficient by Messina, et al. in “The Relationship between Patient Satisfaction and Inpatient Admissions Across Teaching and Nonteaching Hospitals,” listed in the module readings.

2) Comment on what variables were used; whether it answered the research question; and whether the Spearman rank correlation coefficient is appropriately used, given the requirements of the Spearman rank correlation coefficient.

3) Prepare this assignment according to the APA guidelines found in the APA Style Guide, located in the Student Success Center. An abstract is not required.

 

 

ADDITIONAL INFO 

The concept of the normal distribution

Introduction

The normal distribution is a generalization of the Gaussian distribution, which describes many real-world continuous random variables. The normal distribution function describes how the probability changes over different values of one or more parameters.

A normal distribution is a statistical concept that refers to a specific type of data distribution.

A normal distribution is a statistical concept that refers to a specific type of data distribution. It is used to describe the probability of certain values occurring in certain ranges. A normal distribution can be used to model data that follows this type of pattern, such as the heights and weights of people who are healthy or ill, or how many times an object falls before it stops moving (in physics).

The normal distribution is often represented by what is called the bell curve

The normal distribution is often represented by what is called the bell curve, which has its origin in statistics and refers to data that follow this shape. The mean and median are equal, as well as the standard deviation (the distance between these two values).

The most common way to describe a normal distribution is by using a graph called a histogram or bar graph. In this case, we use five parameters: mean (μ), median (σ), standard deviation (σ2), variance σ3/N1), skewness skew(skew=exp(-mu)).

The normal distribution function has two parameters

The normal distribution function has two parameters:

• The mean, which is the center of the distribution. This value can be found by finding its average (the sum of all values divided by n) or mean-squared (the square root of this value):

• The standard deviation, which measures how spread out your data is from this mean.

The shape of the normal distribution depends on both these parameters

The mean and standard deviation of a normal distribution are important because they determine how spread out the data is. The mean is the average, while the standard deviation is a measure of how spread out or “variable” that data actually is. In practice, these two values can be calculated by subtracting them from each other (the difference between their squares).

To find these statistics:

• Calculate your data’s mean by summing up all observed values (x_1 + x_2 + …) then dividing by n-1 where n is how many observations there were; this gives you an estimate of how far away each value should be from its neighbors on average. You can use this to estimate if your sample size has been small enough for it to be considered “unbiased” or not—but remember that bias isn’t always bad! It just means one value was more likely than others so we shouldn’t throw away our results entirely because one person didn’t answer correctly 100% of time! We’ll talk more about this later in this section…

Conclusion

The normal distribution is a useful tool for describing data. It’s a common statistical concept that helps us understand how certain kinds of distributions may be more likely to occur than others by showing us where their average falls on the x-axis of their bell curve.