C784 Final Exam Formulas and Key Concepts in Healthcare Statistics
Student Name
Western Governors University
C784 Applied Healthcare Statistics
Prof. Name
Date
C784: Formulas Applied Healthcare Statistics
This guide presents essential formulas and unit conversions crucial for understanding applied healthcare statistics. Memorizing these formulas is vital for objective assessments in this subject.
Module 2: What are the Commonly Used Metric Prefixes?
Metric prefixes are used to express multiples or fractions of base units in measurements. Below is a table summarizing the most common prefixes, their symbols, and their meanings:
| Prefix | Symbol | Meaning |
|---|---|---|
| kilo | k | 1,000 |
| hecto | h | 100 |
| deka | da | 10 |
| base | – | 1 (unit) |
| deci | d | 0.1 |
| centi | c | 0.01 |
| milli | m | 0.001 |
A helpful mnemonic to remember the order of these prefixes is: King Henry Danced Basically Drinking Chocolate Milk.
Additionally, unit conversion between kilograms and pounds is essential in healthcare:
- 1 kilogram (kg) = 2.2 pounds (lbs)
How Do You Convert Temperatures Between Celsius and Fahrenheit?
Temperature conversion formulas between Celsius (C) and Fahrenheit (F) scales are:
- To convert Celsius to Fahrenheit:
[
F = 1.8C + 32
] - To convert Fahrenheit to Celsius:
[
C = \frac{F – 32}{1.8}
]
These conversions are critical when interpreting patient data from different systems.
Module 3: What is the Slope-Intercept Form of a Line?
The slope-intercept form of a linear equation is expressed as:
[
y = mx + b
]
where:
- (m) represents the slope, calculated as the ratio of the rise over the run ((\frac{\text{rise}}{\text{run}})),
- (b) is the y-intercept, the value of (y) when (x=0).
This form is frequently used in regression analysis and predictive modeling in healthcare statistics.
What are the Basic Tips for Graphing Inequalities in One Variable?
When graphing inequalities:
- Use an open circle (°) to represent strict inequalities ((<) or (>)),
- Use a filled circle for inclusive inequalities ((\leq) or (\geq)),
- Remember to flip the inequality sign when multiplying or dividing by a negative number.
Understanding this concept aids in correctly visualizing statistical constraints.
Module 4: What are the Measures of Center in Data?
Measures of central tendency summarize a data set by identifying a central value:
- Mean: The arithmetic average, calculated as the sum of all data points divided by the number of points.
- Median: The middle data point when values are ordered from least to greatest.
- Mode: The data point that occurs most frequently.
C784 Final Exam Formulas and Key Concepts in Healthcare Statistics
What Does the 5-Number Summary Include?
The five-number summary provides a concise description of data spread and includes:
- Minimum value,
- First quartile (Q1),
- Median (Q2),
- Third quartile (Q3),
- Maximum value.
How Are Outliers Identified?
Outliers are extreme values that differ significantly from other observations. To detect them:
- Calculate the quartiles (Q1 and Q3).
- Compute the Interquartile Range (IQR):
[
\text{IQR} = Q3 – Q1
] - Any data point less than (Q1 – 1.5 \times \text{IQR}) or greater than (Q3 + 1.5 \times \text{IQR}) is considered an outlier.
What are the Measures of Spread?
Measures that describe variability in data include:
- Range: Difference between the maximum and minimum values.
- Interquartile Range (IQR): Spread of the middle 50% of data, calculated as (Q3 – Q1).
- Standard Deviation (SD): Describes the average distance of data points from the mean.
For normally distributed data, the empirical rule applies:
| Standard Deviations from Mean | Percentage of Data within Range |
|---|---|
| 1 SD | 68% |
| 2 SD | 95% |
| 3 SD | 99.7% |
Module 5: How Do You Determine Graphical Displays for One-Variable Data?
Graphical representations depend on the data type:
| Data Type | Recommended Graphical Display |
|---|---|
| Categorical | Pie Chart, Bar Chart |
| Quantitative | Histogram, Stem Plot, Box Plot, Dot Plot |
What Are the Graphical Displays for Two-Variable Data Sets?
For two-variable datasets, the following displays and measures are commonly used:
| Variable Types | Graphical Display or Measure |
|---|---|
| Categorical → Categorical | Two-way Table with Conditional Percentages |
| Categorical → Quantitative | Side-by-side Boxplots with 5-Number Summary |
| Quantitative → Quantitative | Scatterplot with Correlation Coefficient |
Module 6: What Does the Correlation Coefficient Indicate?
The correlation coefficient, denoted as (r), measures the strength and direction of the linear relationship between two quantitative variables:
- (r) ranges from (-1) to (1),
- Positive (r) indicates a positive trend (variables increase together),
- Negative (r) indicates a negative trend (one variable increases while the other decreases).
Removing outliers from the dataset can significantly affect the value of (r).
Module 7: What Are the Basic Probability Formulas?
Probability rules include:
C784 Final Exam Formulas and Key Concepts in Healthcare Statistics
| Rule | Operation | Formula | Keywords |
|---|---|---|---|
| Addition Rule | Add & subtract overlap | (P(A \text{ or } B) = P(A) + P(B) – P(A \text{ and } B)) | or, either |
| Multiplication Rule | Multiply | Not Conditional: (P(A \text{ and } B) = P(A) \times P(B)) Conditional: (P(A \text{ and } B) = P(A) \times P(B | A)) |
| Conditional Probability | Divide | (\text{P(B | A)} = \frac{P(A \text{ and } B)}{P(A)}) |
| Complement Rule | Subtraction | (P(\text{not } A) = 1 – P(A)) | not |
Understanding these rules is fundamental for calculating event probabilities in healthcare analytics.