C784 Final Exam Formulas and Key Concepts in Healthcare Statistics

C784 Final Exam Formulas and Key Concepts in Healthcare Statistics C784 Pre-Assessment Guide

Student Name

Western Governors University

C784 Applied Healthcare Statistics

Prof. Name:

Date

C784: Formulas Applied in Healthcare Statistics

Healthcare professionals frequently rely on mathematical and statistical formulas to analyze data, interpret results, and make informed clinical decisions. The following sections summarize key formulas, conversions, and statistical principles essential for success in the C784: Applied Healthcare Statistics course. Memorizing these formulas is crucial for the objective assessment.

Module 2: Commonly Used Metric Prefixes

Understanding metric prefixes is fundamental to interpreting measurements in healthcare, such as dosage calculations, laboratory results, and medical imaging data.

Metric Prefixes Table

Prefix	Symbol	Value	Meaning/Conversion
kilo	k	1,000	One thousand units
hecto	h	100	One hundred units
deka	da	10	Ten units
base	–	1	The base unit (e.g., meter, liter, gram)
deci	d	0.1	One-tenth of the base unit
centi	c	0.01	One-hundredth of the base unit
milli	m	0.001	One-thousandth of the base unit

A helpful mnemonic for remembering these prefixes is:
“King Henry Danced Basically Drinking Chocolate Milk.”

Unit Conversion Example

1 kilogram (kg) = 2.2 pounds (lbs)

This conversion is frequently used in clinical practice to calculate patient weights or medication dosages.

Temperature Conversions

Temperature measurement is critical in patient care. The following equations convert between Celsius and Fahrenheit:

Conversion Type	Formula	Example
Celsius to Fahrenheit	F = (1.8 × C) + 32	37°C = 98.6°F
Fahrenheit to Celsius	C = (F – 32) ÷ 1.8	98.6°F = 37°C

Module 3: Linear Equations and Inequalities

Linear equations and inequalities are essential for modeling relationships between healthcare variables, such as heart rate and exercise intensity.

Slope-Intercept Form

The general form of a linear equation is:
y = mx + b

Where:

m represents the slope, or rate of change (rise/run).
b represents the y-intercept, or the point where the line crosses the y-axis (0, b).

Inequalities in One Variable

When solving inequalities, graphical representation helps visualize the solution set.

Inequality Type	Graphical Representation	Rule
`<` or `>`	Open circle	Value not included
`≤` or `≥`	Closed (filled) circle	Value included
Multiply or divide by a negative number	Flip inequality sign	Ensures correct relationship

Module 4: Measures of Central Tendency and Spread

Statistical measures describe and summarize healthcare data, such as patient outcomes or lab results.

Measures of Center

Measure	Definition
Mean	The sum of all data points divided by the total number of data points.
Median	The middle value when all data are arranged in order.
Mode	The most frequently occurring data point.

Five-Number Summary

A five-number summary includes:

Minimum value
First quartile (Q1)
Median (Q2)
Third quartile (Q3)
Maximum value

These values assist in identifying data spread and outliers.

Identifying Outliers

To detect outliers:

Calculate the interquartile range (IQR = Q3 – Q1)
A data point is considered an outlier if:
- It is less than Q1 – 1.5(IQR), or
- Greater than Q3 + 1.5(IQR)

Measures of Spread

Measure	Formula/Description
Range	Maximum – Minimum
Interquartile Range (IQR)	Q3 – Q1
Standard Deviation	Indicates how spread out the data is around the mean

In a normal distribution:

68% of data fall within 1 standard deviation of the mean
95% within 2 standard deviations
99.7% within 3 standard deviations

Module 5: Graphical Displays for Data Sets

Visual representation of data helps identify trends, relationships, and distributions in healthcare analytics.

One-Variable Data

Data Type	Display Method
Categorical	Pie Chart, Bar Chart
Quantitative	Histogram, Stem Plot, Box Plot, Dot Plot

Two-Variable Data

Variable Relationship	Graph Type	Statistical Measure
Categorical → Categorical (C → C)	Two-way Table	Conditional Percentages
Categorical → Quantitative (C → Q)	Side-by-Side Boxplot	Five-Number Summary
Quantitative → Quantitative (Q → Q)	Scatterplot	Correlation Coefficient

Module 6: Correlation and the Correlation Coefficient

The correlation coefficient (r) quantifies the strength and direction of a linear relationship between two variables.

Correlation Type	Trend	r-Value Range
Positive	Variables increase together	0 < r ≤ 1
Negative	One variable increases while the other decreases	–1 ≤ r < 0
No Correlation	No relationship	r ≈ 0

Removing outliers often improves the accuracy of the correlation coefficient since extreme data points can distort the trend.

Module 7: Probability Formulas

Probability is essential in healthcare for assessing risk, predicting outcomes, and making data-driven decisions.

Rule	Operation	Formula	Key Words/Indicators
Addition Rule	Add & Subtract overlap	P(A or B) = P(A) + P(B) – P(A and B)	“or,” “either”
Multiplication Rule (Independent)	Multiply	P(A and B) = P(A) × P(B)	“and,” “both”
Multiplication Rule (Conditional)	Multiply conditional probability	P(A and B) = P(A) × P(B	A)
Conditional Probability	Divide	P(B	A) = P(A and B) ÷ P(A)
Complement Rule	Subtract	P(not A) = 1 – P(A)	“not”

References

American Psychological Association. (2020). Publication manual of the American Psychological Association (7th ed.). APA.

OpenStax. (2023). Introductory statistics. OpenStax. https://openstax.org/details/books/introductory-statistics