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: How Do You Convert Temperatures Between Celsius and Fahrenheit? Temperature conversion formulas between Celsius (C) and Fahrenheit (F) scales are: 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: 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: 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: 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: How Are Outliers Identified? Outliers are extreme values that differ significantly from other observations. To detect them: What are the Measures of Spread? Measures that describe variability in data include: 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: 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.