biometrics notes

0.0(0)
studied byStudied by 0 people
learnLearn
examPractice Test
spaced repetitionSpaced Repetition
heart puzzleMatch
flashcardsFlashcards
Card Sorting

1/17

flashcard set

Earn XP

Description and Tags

module four

Study Analytics
Name
Mastery
Learn
Test
Matching
Spaced

No study sessions yet.

18 Terms

1
New cards

Bivariate Data

Data consisting of two variables measured on the same experimental unit.

2
New cards

Types of Bivariate Data

  1. Two Qualitative Variables (e.g., hair color and hair type). 2. One Qualitative and One Quantitative Variable (e.g., gender and height). 3. Two Quantitative Variables (e.g., height and shoe size).

3
New cards

Scatterplots

Used to visualize relationships between two quantitative variables, identifying patterns, trends, and outliers.

4
New cards

Correlation Coefficient (r)

Measures the strength and direction of a linear relationship between two quantitative variables, ranging from -1 to +1.

5
New cards

Perfect Positive Correlation

Represented by r = +1, indicating that as one variable increases, the other also increases.

6
New cards

Perfect Negative Correlation

Represented by r = -1, indicating that as one variable increases, the other decreases.

7
New cards

No Correlation

Indicated by r = 0, meaning no linear relationship between the variables.

8
New cards

Correlation ≠ Causation

A strong correlation between two variables does not imply that one variable causes the other to change.

9
New cards

Third Variable Problem

The influence of a lurking variable that affects both variables in a correlation.

10
New cards

Impact of Outliers

Outliers can distort correlation values and may require justification to remove valid data.

11
New cards

Extrapolation

Making predictions beyond the range of observed data, which can be unreliable.

12
New cards

Nonlinear Relationships

A correlation coefficient of r = 0 does not necessarily mean there is no relationship; the relationship may be nonlinear.

13
New cards

Coefficient of Determination (R²)

Measures the proportion of variance in the dependent variable explained by the independent variable, ranging from 0 to 1.

14
New cards

Linear Regression

Used to predict a value for a dependent variable (y) given a value of an independent variable (x).

15
New cards

Best Fit Line

A line that minimizes the deviations between the line and actual data points in regression analysis.

16
New cards

Slope Interpretation

A positive slope indicates an increase in x leads to an increase in y; a negative slope indicates an increase in x leads to a decrease in y.

17
New cards

Regression vs. Correlation

Correlation measures association (r), while regression predicts a dependent variable (y) from an independent variable (x) using R².

18
New cards

Lawrence Garfinkel

A statistician known for establishing links between smoking and lung cancer through crucial correlation studies.