Healthcare Data Analytics Homework: Calculations and Analysis

This document presents a solved homework assignment in healthcare data analytics. It includes detailed solutions to various problems, such as unit conversions, dosage calculations, and determining the number of capfuls needed for a specific amount of water. The assignment covers statistical concepts like calculating the mean, identifying the absolute and relative frequencies of data, and determining the number of classes based on class widths. Furthermore, the document provides a comprehensive analysis of a dataset of patient ages, including calculations of mean, median, mode, range, and variance. It also includes frequency distribution tables and calculations of variance within specific age ranges. The document is available on Desklib, a platform offering AI-based study tools.

Running head: HEALTHCARE DATA ANALYTICS
HEALTHCARE DATA ANALYTICS
Name of the Student
Name of the University
Author Note

1HEALTHCARE DATA ANALYTICS
8. 25 mg = 25*10^-3 g = 0.025 g.
9. 1 lb = 453.592 grams.
And 1 oz = 28.3495 grams.
Now, the given weight of baby is 8 lbs 1 oz.
Hence, the weight of the baby in grams = 453.592*8 + 28.3495*1 = 3657.0855 grams.
10. 1 capful is needed to be added to every 8 ounces of water.
Hence, for 64 ounces of water capfuls needed = 64/8 = 8 capfuls.
11. Every coder is budgeted for three sets of references.
Now, for 25 coders the total sets of references needed = 25*3 = 75 sets.
12. Calculation of mean tells the average of quantities which indicates the amount per unit
quantity. The main advantage of mean is that it can be calculated for any type of numeric
variables which includes both continuous and discrete values. The mean always have a single
value unlike median and mode which gives an appropriate estimate of average of data. The
disadvantages are that mean fails to measure categorical data as the values cannot be added.
The mean is also influenced by the outliers in the data which is not desired sometimes as
outliers often indicates error values in data.
13. The absolute frequency is the number of occurrence of a specific value or item in a
dataset. Hence, the absolute frequency of male patients is 50 and absolute frequency of
female patients is 86. Now, the relative frequency of males is 50/(50+86) = 0.3676 = 36.76%
and relative frequency of females = 86/(50+86) = 0.6324 or 63.24%. The ratio of men to
women is 50:86 = 25:43.
14. Minimum value in the range = $85000

2HEALTHCARE DATA ANALYTICS
Maximum value in the range = $160000
Hence, if the entire range is broken into equal class widths of $5000 then the number of
classes will be (160000 – 85000)/5000 = 15 classes. Now, when widths of $20000 is used
then the number of classes will be = (160000 – 85000)/20000 = 3.75 ~ 4 classes.
15. The list of ages of the 50 patients are given below.
14, 15, 15, 16, 16, 17, 17, 17, 17, 18, 18, 18, 19, 19, 19, 19, 19, 19, 20, 20, 20, 21, 21, 21, 22,
22, 24, 24, 24, 25, 26, 26, 26, 27, 28, 30, 31, 32, 36, 40, 41, 41, 47, 52, 52, 59, 59, 60, 65, 72.
a. The mean age = sum of ages/50 = 28.52 years.
b. Median is the middlemost value in the order = 22 years.
c. Mode is the age with highest number of occurrence in the data = 19 (6 times)
d. The range of the data = maximum – minimum = 72 – 14 = 58
e. The variance of the ages is obtained from the frequency distribution as given below.
class limits Frequency(f) Midpoint(M
)
M^2*f
11 to 20 21 15.5 5045.25
21 to 30 15 25.5 9753.75
31 to 40 4 35.5 5041
41 to 50 3 45.5 6210.75
51 to 60 5 55.5 15401.25
61 to 70 1 65.5 4290.25
71 to 80 1 75.5 5700.25
Total 50 318.5 51442.5
Variance(S^2) = (sum(f*M^2) - n*(mean)^2)/(n-1) = 219.8567347

3HEALTHCARE DATA ANALYTICS
Where, n = number of observations = 50.
f. Now, the frequency of number in the class interval 14 to 35 is 38. Thus the variance of ages
in this class is calculated from the following table.
class
interval
frequency(f) Midpoint M^2*f Mean
14 to 35 38 24.5 22809.5 21.10526
Variance (sum(f*M^2) -
n*(mean)^2)/(n-1)
159.002133
7
Now, the class interval is set to 1. Hence, the entire dataset is divided with one unique value
in each class. Thus the standard deviation becomes the standard deviation of all the ages. This
is calculated by excel as 14.914.