Complete the CDC E-learning activities “Create an Epi Curve” and “Using an Epi Curve to determine mode of spread” at https://www.cdc.gov/training/quicklearns/
1. Complete the CDC E-learning activities “Create an Epi Curve” and “Using an Epi Curve to determine mode of spread” at https://www.cdc.gov/training/quicklearns/
2. Create an epi-graph based on the data in exercise 1 of “Create an Epi Curve.” This can be made in MS Excel or another graphing utility.
3. Submit it with your answers to the exercises in the module (questions start on slide 8) in a MS RTF file or PDF.
Requirements: complete all
Assignment 2 This assignment should be done in SAS Studio using the dataset SASHELP.HEART. You will need to export the results into a PDF or Word document in order to copy/screenshot your results into this file. Please save your assignment as a PDF before submitting. Hint: You will need to use the options tab to only provide what is asked in the question. Remember classification variables are used when you want to divide the data into certain categories/groups. 1. Create a combined histogram and boxplot for Height. Include the measures in the 5-Number Summary as inset statistics. Hint: Use summary statistics. 1a. Insert graph (histogram with boxplot) here: 1b. Based on the boxplot in #1, are there any outliers? If so, how many? Answer: There are 2 outliers. The calculated IQR is found to be 5.25, and the quartile values are found to be Q1 62.2500000 and Q3 67.5000000. Using the IQR method, we considered an outlier any value that falls five quartile values below the lower quartile, which is 54.375, approximately or above the upper quartile by approximately 5 quartile values, which comes to about 75.375. Therefore, there are 2 outliers.
1c. Is the mean height greater or less than the median? 2. Create a table comparing Height for males and females. Include the following measures in the table: Number of observations, mean, standard deviation, minimum and maximum. Include comparative histograms and boxplots. Hint: Use summary statistics. 2a. Insert table here: Answer: After comparing the mean (64.8131847) and the median (64.5000000), we can conclude that the mean is slightly more significant than the median. Hence, we can define this distribution pattern as having a slightly right-skewed distribution, meaning potential outliers on the right side of the distribution can pull the mean slightly higher.
2b. Insert comparative histograms here:
2c. Insert comparative boxplots here:
3. Create a table for Age at Start and Age at Death. Include n, mean, standard deviation, and CV. Hint: Use summary statistics. 3a. Insert table here: 3b. Interpret the CV values for these two variables in 3a. 3c. Write a statement about how the relative variability for these two variables compares. Answer: Another way to measure relative variability is to calculate the coefficient of variation (CV), the standard deviation divided by the mean, expressed as a percentage. For the age at start variable, for example, the Standard deviation is about 8.55 years, which is approximately 19.48% of the mean of 44.07 years. In other words, there is a moderate relative variability of the ages at the start as the ages at the start are somewhat dispersed around the mean age of 44.07 years. In the case of the age at death, the standard deviation is about 10.58 years, which is a 14.99% proportion. Consequently, the ages at death have a slightly smaller degree of dispersion relative to their mean age of 70.54 years than the ages at the start. Answer: The coefficient of variation (CV) gives insights into the relative variability of both variables. In this dataset, ‘Age at Start’ demonstrates a higher CV of approximately 19.48% compared with ‘Age at Death,’ with a CV value of 14.99%. This implies that ‘Age at Start’ has more relative variability, which indicates that ages at the start do deviate more from the mean age, which is approximately equal to 44.07 years when compared with ‘Age at Death,’ where the ages do lie closer to the mean age of 70.54 years. Hence, both the variables show moderate relative variability; this suggests a kind of dispersion in ages at the start of alcohol use and ages at death but to different extents.
4. Create a frequency table for Smoking_Status with counts (frequency) and relative frequencies (percent) for each class. Include a bar chart representing the distribution of Smoking Status. (Numbers in bar chart should match numbers in the frequency table.) Hint: Use One-Way Frequencies. 4a. Insert table here: 4b. Insert bar chart here:
Statistics Question.
Student’s Name:
Course Name and Number:
Institutional Affiliation:
Instructor’s Name:
Date Due:
Question One
Option a.
Option b. Answer: There are 2 outliers.
The calculated IQR is found to be 5.25, and the quartile values are found to be Q1 62.2500000 and Q3 67.5000000. Using the IQR method, we considered an outlier any value that falls five quartile values below the lower quartile, which is 54.375, approximately or above the upper quartile by approximately 5 quartile values, which comes to about 75.375. Therefore, there are 2 outliers.
Option c.
After comparing the mean (64.8131847) and the median (64.5000000), we can conclude that the mean is slightly more significant than the median. Hence, we can define this distribution pattern as having a slightly right-skewed distribution, meaning potential outliers on the right side of the distribution can pull the mean slightly higher.
Question two
Option a.
Option b
Option c:
Question Three
Option a.
Option b.
Another way to measure relative variability is to calculate the coefficient of variation (CV), the standard deviation divided by the mean, expressed as a percentage. For the age at start variable, for example, the Standard deviation is about 8.55 years, which is approximately 19.48% of the mean of 44.07 years. In other words, there is a moderate relative variability of the ages at the start as the ages at the start are somewhat dispersed around the mean age of 44.07 years. In the case of the age at death, the standard deviation is about 10.58 years, which is a 14.99% proportion. Consequently, the ages at death have a slightly smaller degree of dispersion relative to their mean age of 70.54 years than the ages at the start.
Option c.
The coefficient of variation (CV) gives insights into the relative variability of both variables. In this dataset, ‘Age at Start’ demonstrates a higher CV of approximately 19.48% compared with ‘Age at Death,’ with a CV value of 14.99%. This implies that ‘Age at Start’ has more relative variability, which indicates that ages at the start do deviate more from the mean age, which is approximately equal to 44.07 years when compared with ‘Age at Death,’ where the ages do lie closer to the mean age of 70.54 years. Hence, both the variables show moderate relative variability; this suggests a kind of dispersion in ages at the start of alcohol use and ages at death but to different extents.
Question Four
Option a.
Option b
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