Healthcare Statistics Formulas and Unit Conversions, Summaries of Statistics

A compilation of key formulas and unit conversions for applied healthcare statistics. It covers topics such as metric prefixes, temperature conversions, slope-intercept form, inequalities, measures of center and spread, graphical displays for one and two variable data sets, and probability formulas. It includes practical tips and rules for statistical analysis, making it a useful reference for students studying healthcare statistics. Well-organized and presents the information in a clear and concise manner, facilitating easy memorization and application of the formulas.

Typology: Summaries

2024/2025

Uploaded on 10/31/2025

patricia-yqy
patricia-yqy ๐Ÿ‡บ๐Ÿ‡ธ

1 document

1 / 5

Toggle sidebar

This page cannot be seen from the preview

Don't miss anything!

bg1
1
C784: Formulas
Applied Healthcare Statistics
These are the main formulas and unit conversions we recommend focusing
on. You will have to memorize all formulas for the objective assessment.
Module 2:
Commonly Used Metric Prefixes:
Prefix
Symbol
Meaning
kilo
k
1000
hecto
h
100
deka
da
10
base
-
1
deci
d
0.1
centi
c
0.01
milli
m
0.001
*Remember:
King Henry Danced Basically Drinking Chocolate Milk
1 kg = 2.2 lbs
Temperature Conversions:
F = 1.8C+32
C = (F - 32)รท1.8
Module 3:
Slope intercept form of a line:
y=mx + b
โ€ข m=slope=๐‘Ÿ๐‘–๐‘ ๐‘’
๐‘Ÿ๐‘ข๐‘›
โ€ข b=y-intercept=the point (0, b)
pf3
pf4
pf5

Partial preview of the text

Download Healthcare Statistics Formulas and Unit Conversions and more Summaries Statistics in PDF only on Docsity!

C784: Formulas

Applied Healthcare Statistics These are the main formulas and unit conversions we recommend focusing on. You will have to memorize all formulas for the objective assessment. Module 2: Commonly Used Metric Prefixes: Prefix Symbol Meaning kilo k 1000 hecto h 100 deka da 10 base - 1 deci d 0. centi c 0. milli m 0. *Remember: K ing H enry D anced B asically D rinking C hocolate M ilk 1 kg = 2.2 lbs Temperature Conversions: F = 1.8C+

C = (F - 32)รท1.

Module 3: Slope intercept form of a line: y=mx + b

  • m=slope= ๐‘Ÿ๐‘–๐‘ ๐‘’ ๐‘Ÿ๐‘ข๐‘›
  • b=y-intercept=the point (0, b)

Basic tips for inequalities in one-variable: < or > is graphed using an open circle ยฐ โ‰ค or โ‰ฅ is graphed using a filled circle โ€ข Flip the direction of the inequality sign when multiplying or dividing by a negative Module 4 Measures of Center:

  • Mean = the sum of all the data points, divided by the number of data points.
  • Median =the middle number when all the data points are written in order from least to greatest
  • Mode =the most frequent data point 5 Number Summary: Includes the minimum, maximum, Q1, Q2 (median), and Q Finding Outliers:
  • Find the quartiles.
  • Any data value less than Q 1 - 1.5(IQR) is an outlier.
  • Any data value greater than Q 3 + 1.5(IQR) is an outlier. Measures of Spread:
  • Range = max - min
  • IQR = Q 3 โ€“ Q 1

Module 5: Determining graphical displays for two variables data set Classification Display Numerical Measures ๐ถ โ†’ ๐ถ (^) Two-way Table Conditional Percentages ๐ถ โ†’ ๐‘„ (^) Side-by-side Boxplot 5 - Number Summary ๐‘„ โ†’ ๐‘„ (^) Scatterplot Correlation Coefficient Module 6 Correlation Coefficient (r): R is always between โ€“ 1 and 1. Positive trend= positive slope= positive r Negative trend = negative slope= negative r

Remove Outlier Effect on R-Value: Module 7: Probability formulas: Rule Operation Formula Key Word Addition Rule Add & Subtract any overlap P(A or B)= P(A)+P(B)-P(A and B) or, either Multiplication Rule Multiply Not Conditional: P(A and B)=P(A)P(B) Conditional: P(A and B)=P(A)P(B|A) and, both Conditional Probability Divide OR if, of, given Complement Rule Subtraction P(not A)=1-P(A) not