Working With Descriptive Statistics Assignment

Working With Descriptive Statistics Assignment

DNP 830 Topic 3 Working With Descriptive Statistics GCU

General Requirements:

This course helps you develop a basic understanding of statistics. This course addresses two distinct types, descriptive and inferential. In this assignment, you will have the opportunity to use a software program that makes it easy to analyze data using specific tests. This assignment will give you practice with mean, median, mode, frequency, range, and standard deviation. Be sure to review the Topic Material videos before undertaking this practice.

Use the following information to ensure successful completion of the assignment:

• For assistance with accessing SPSS, refer to the resources “How to Use SPSS From the GCU Server” and “How to Install SPSS on Your Computer.”
• Before beginning this assignment, be sure to view the tutorial videos provided as Topic Materials: (1) SPSS for Beginners 1 – Introduction; (2)SPSS for Beginners 2a: Frequency Counts; (3) SPSS for Beginners 2b: Descriptive Statistics and Z-scores; and (4) Graphing and Descriptive Stats in SPSS With Dr. Ami Gates.
• Doctoral learners are required to use APA style for their writing assignments. The APA Style Guide is located in the Student Success Center.
• You are not required to submit this assignment to LopesWrite.

Directions:

SPSS Output

Open SPSS and obtain an output (as in the tutorial videos) with the following results highlighted:

1. Determine the statistics for each gender as follows: Frequency Counts, Mean, Standard Deviation, Minimum, and Maximum.
2. Graphing and Descriptive Stats in SPSS: Create a bar graph with gender (axis X) and blood sugar (axis Y).

Data Set

Use the following data set for this assignment:

1. You have a group of patients observed with a diagnosis of Diabetes and their blood sugar levels are listed below based on gender. Men: 74, 71, 75, 248, 388, 505, 42, 21.
2. Female: 62, 68, 61, 71, 68, 80, 390, 148.

Summary

Write a 250-500 word summary of your results and how this statistical analysis may be applied to your prospectus. Provide a bar graph with gender on the x-axis and blood sugar levels on the y-axis. Add your SPSS output as an Appendix to this summary.

Portfolio Practice Hours:

Practice immersion assignments are based on your current course objectives and is application based learning using your real-world practice setting. These assignments earn practice immersion hours and are indicated in the syllabus by a Portfolio Practice Hours statement, which reminds you, the learner, to enter in a corresponding case log in Typhon. Actual clock hours are entered, but the average hours associated with each practice immersion assignment is 10.

You are required to complete your assignment using real-world application. Real-world application requires the use of evidence-based data, contemporary theories, and concepts presented in the course. The culmination of your assignment must present a viable application in a current practice setting. For more information on parameters for practice immersion hours, please refer to DNP resources in the DC Network.

To earn portfolio practice hours, enter the following after the references section of your paper:

Practice Hours Completion Statement DNP-830

I, (INSERT NAME), verify that I have completed (NUMBER OF) clock hours in association with the goals and objectives for this assignment. I have also tracked said practice hours in the Typhon Student Tracking System for verification purposes and will be sure that all approvals are in place from my faculty and practice mentor.

DNP 830 Topic 4 CITI Training: Basic Research Course

For this assignment, you will complete the Basic Research Course.The Collaborative Institutional Training Initiative (CITI) training in research ethics is a requirement for submission of your practice implementation project to the Institutional Review Board (IRB). CITI training provides you with information regarding IRB requirements. During this online training, you will be required to read and process information and to take quizzes to demonstrate your understanding of research ethics and IRB requirements. You will be able to save your work at the end of each module, exit, and return later. You will complete both the Basic Research Course and the Social and Behavioral Responsible Conduct of Research Course during this topic.

General Requirements:

Use the following information to ensure successful completion of the assignment:

• CITI registration directions are found in the DC Network. To access them, please use the following link: https://dc.gcu.edu/documents/irb_documents__iris/citi-ethics-training/citi_training_instructions_doctoral_learner_pdf
• Set up your CITI account and register for the required Basic Course by going to https://www.citiprogram.org. Click on the “Register Here” to register. You may register for the Social and Behavioral Responsible Conduct of Research Course at this time as well.

Directions:

1. Log on to the CITI training website and register for the CITI Training.
2. Complete all modules of the Basic Research Course. A completion report will be generated by the CITI website after you complete all modules.
3. Copy and paste your completion report from the CITI website into a Word document and save the file to your computer.
4. Submit the saved completion report to the instructor.

Portfolio Practice Hours:

Practice immersion assignments are based on your current course objectives and is application based learning using your real-world practice setting. These assignments earn practice immersion hours and are indicated in the syllabus by a Portfolio Practice Hours statement, which reminds you, the learner, to enter in a corresponding case log in Typhon. Actual clock hours are entered, but the average hours associated with each practice immersion assignment is 10.

You are required to complete your assignment using real-world application. Real-world application requires the use of evidence-based data, contemporary theories, and concepts presented in the course. The culmination of your assignment must present a viable application in a current practice setting. For more information on parameters for practice immersion hours, please refer to DNP resources in the DC Network.

To earn portfolio practice hours, enter the following at the end of the completion report:

Practice Hours Completion Statement DNP-830

I, (INSERT NAME), verify that I have completed (NUMBER OF) clock hours in association with the goals and objectives for this assignment. I have also tracked said practice hours in the Typhon Student Tracking System for verification purposes and will be sure that all approvals are in place from my faculty and practice mentor.

SAMPLE ANSWER

Working With Descriptive Statistics

Descriptive statistics are a branch of mathematics that deals with the collection, analysis, interpretation, presentation, and organization of data. In other words, it helps us to understand data that we have collected. There are many different ways to collect and analyze data, but descriptive statistics is one of the most commonly used methods. This is because it is relatively easy to understand and can be used to effectively describe a large amount of data. If you’re working with data, there’s a good chance you’ll need to use descriptive statistics at some point. In this blog post, we’ll give you an overview of what descriptive statistics are and how they can be used.

What are Descriptive Statistics?

Descriptive statistics are used to describe the main features of a data set in a simple way. They are often used to give an overview of a data set, or to highlight any unusual features.

There are four main types of descriptive statistics:

1. Measures of central tendency
2. Measures of dispersion
3. Measures of shape
4. Other measures

Measures of central tendency include the mean, median and mode. These measures give us an idea of where the center of a data set lies. The mean is the most common measure of central tendency and is simply the average of all the values in a data set. The median is the middle value in a data set, while the mode is the most common value in a data set.

Measures of dispersion tell us how spread out our data is. The range, variance and standard deviation are all measures of dispersion. The range tells us the difference between the highest and lowest values in a data set, while the variance and standard deviation tell us how much variation there is within a data set.

Measures of shape give us an idea of the distribution of our data. Common measures of shape include skewness and kurtosis. Skewness tells us if our data is skewed to one side or another, while kurtosis tells us how peaked or flat our data is.

Other measures include percentiles and quartiles. Percentiles tell us what percentage of

How to Calculate Descriptive Statistics

Descriptive statistics are a set of tools used to summarize and describe data. They can be used to calculate measures of central tendency (mean, median, and mode) and dispersion (range, variance, and standard deviation).

To calculate descriptive statistics, you will need to gather your data. This can be done by collecting data from a variety of sources, such as surveys, experiments, or observational studies. Once you have collected your data, you will need to organize it in a way that makes it easy to analyze. This can be done by creating a frequency table or using a software program like Microsoft Excel.

Once your data is organized, you can begin calculating measures of central tendency. The mean is the most commonly used measure of central tendency and is calculated by taking the sum of all the data points and dividing by the number of data points. The median is the middle value when the data is sorted from smallest to largest. The mode is the most frequently occurring value in the data set.

To calculate measures of dispersion, you will need to find the range, variance, and standard deviation. The range is the difference between the highest and lowest values in the data set. The variance is a measure of how spread out the data is and is calculated by taking the sum of the squares of the difference between each data point and the mean divided by the number of data points. The standard deviation is simply the square root of the variance.

The Different Types of Descriptive Statistics

Descriptive statistics are often used to describe the distribution of data. This can be done in a number of ways, including using measures of central tendency (mean, median, and mode) and measures of dispersion (range, variance, and standard deviation).

Another way to describe data is to use percentiles. Percentiles tell you what percentage of values fall below a certain point. For example, the 20th percentile would tell you what percentage of values fall below the 20th percentile.

There are also a number of other types of descriptive statistics, including:

-Measures of skewness: These measures tell you whether your data is skewed to the left or right.
-Measures of kurtosis: These measures tell you whether your data is peaked or flat.
-IQR: The IQR is a measure of dispersion that tells you how much the middle 50% of your data deviates from the mean.
-Range: The range is simply the difference between the highest and lowest values in your data set.

Pros and Cons of Descriptive Statistics

Descriptive statistics are a powerful tool for understanding data, but they have their limitations. Here are some pros and cons of using descriptive statistics:

Pros:
-Descriptive statistics can help you to understand the overall pattern of a dataset.
-They can also help you to spot anomalies and outliers in the data.
-Descriptive statistics are easy to calculate and interpret.

Cons:
-Descriptive statistics do not allow you to make inferences about a population from a sample.
-They also cannot be used to establish cause-and-effect relationships.
-Because descriptive statistics summarise data in a way that is easy to understand, they can sometimes mask the underlying complexity of the data.

When to Use Descriptive Statistics

Descriptive statistics are tools that help you understand data. They are typically used to summarize data, to find patterns in data, and to make predictions.

There are many different types of descriptive statistics, and the type of data you have will dictate which type of descriptive statistic is most appropriate. For example, if you have a lot of numerical data, then you might want to use measures of central tendency (mean, median, mode) to summarize your data. If you have categorical data, then you might want to use measures of association (chi-square) to find patterns in your data.

Here are some guidelines for when to use descriptive statistics:

-When you want to summarize a large amount of data
-When you want to find patterns in your data
-When you want to make predictions based on your data

Conclusion

Descriptive statistics are a powerful tool that can help us to understand data sets. By summarizing the data, we can identify patterns and relationships that would be difficult to discern by looking at the raw data. However, it is important to remember that descriptive statistics do not allow us to make conclusions about causality. If you want to understand how two variables are related, you will need to use inferential statistics. But if you just want to get a general sense of what your data looks like and what kind of patterns it contains, descriptive statistics are the way to go.