Stats Vocab List

Question 1

Sensitivity

Answer 1

A test’s ability to identify someone as positive, or true positives: (Positive Given Disease Present)

Question 2

Specificity

Answer 2

A test’s ability to identify exclusively the agent, to not be misled by alternative cases. (Negative Given Disease Absent)

Question 3

False Postive

Answer 3

A test’s chance of overcoverecting, detecting disease that is not present. (Positive Given Disease Absent)

Question 4

False Negative

Answer 4

A test’s chance of missing disease, of undershooting. (Negative Given Disease Absent.)

Question 5

Disjointed

Answer 5

Mutually exclusive outcomes. Ex: Getting a Head and a Tail on the same coin flip.

Question 6

OR

Answer 6

One or the other, not both. Contrast: At least.

Question 6

Addition Rule

Answer 6

Chance of either event happening (non-disjointed): P (A or B) = P(A) + P(B) - P(A & B).

Disjoint: P (A or B) = P(A) +P (B)

Question 6

Dependent

Answer 6

Used in conditional probability to describe when events influence each other. Someone who plays basketball is more likely to be taller then the average American. P (A & B) = P (A) times P(B given A) or the chance that A & B happen is equal to the chance A happens times B happens when A has already happened.

Question 7

Complete

Answer 7

Every possible outcome is in the sample space - you’ve represented everything.

Question 7

Two-Way Table

Answer 7

A type of table where two variables are represented in frequencies i.e. Women who like pokemon vs digimon as a yes/no question.

Question 8

Stem & Leaf Plot

Answer 8

Type of quantitative plot where a stem plot representing the ten’s place, possibly with a half-stem section, and the rest of the data behind it as leaves. Like an enumerated dot plot. Best for single quantifiable variables with small amount of values, and good individual values, with options for comparing groups and shapes.

Question 8

Histogram

Answer 8

Type of graph for two quantitative variables, commonly including frequency. Big bars. Best for large amounts of numbers in small distributions where we care most about the shape.

Question 9

Independent

Answer 9

Events that do not impact the other. The probability of P(A given B) = P(A) * P(B).

Question 9

Cases

Answer 9

Experimental units, what data is collected from.

Question 9

Variable

Answer 9

The value (quantity/quality) changing and measured through statistics.

Question 10

Simulation

Answer 10

Running a theoretical match to compare to real data, and the likelihood of that occurring, several times.

Question 11

Skew

Answer 11

Data significantly trailing off from the median. Side of the trail, often against walls.

Question 12

Normal Distribution

Answer 12

Describes a common distribution pattern with a single median/mode peak and an equivalent mean (affected by skew), distribution symmetrical. SD: 68, 95, 99.7 (Empirical Rule)

Question 13

Uniform

Answer 13

Describes a distribution pattern of flatness, with no real mode or more likely distributions.

Question 14

Standard Normal Distribution

Answer 14

Idealized normal curve with equivalent m’s at 0, an SD of 1, and perfect symmetry.

Question 15

Standard Deviation

Answer 15

Describes the distance of a point from the center (median), as a measure of spread. For a normal curve, this is the distance to the inflection point from the center.

Calculated as the sum of difference from mean for each value squared (squared first, then added), all over some n or n-1, dependent on sample versus population.

Question 16

Uni/Bimodal Distribution

Answer 16

Pattern of distribution with several peaks or modes. A common sign of several distinct groups smushed into a sample space.

Question 17

Quartiles

Answer 17

Separations of the normal distribution. Lower quartile is 25 to 50%, and the upper quartile is 50 to 75%, or Q1-Med-Q3.

Question 18

Quantitative

Answer 18

Distinctions in variables that are quantifiable. Running speed of predators.

Question 19

Categorical

Answer 19

Distinctions in variables that are typal. Species of predator.

Question 20

Cumulative Frequency

Answer 20

Type of graph where cumulative or total frequency forms the y-axis. Easier to find quartiles, median and such.

Question 21

Bar Graph

Answer 21

Histogram look-a-like using categorical data.

Question 22

Rescaling

Answer 22

Multiplying every value in a graph by the same non-zero number - center is old center * d, spread is changed (shrink or stretch). Shape constant

Question 23

Recentering

Answer 23

Adding a constant to all values. Median and mean grow by c. Shape and spread constant.

Question 24

Sensitivity to Outliers

Answer 24

Vulnerable summary statistics are highly affected - i.e. mean, standard distribution. Median, quartiles and IQR are resilient.

Question 25

Trend, Strength, Linerality:

Answer 25

Used for two variable quantitative plots like scattergrams. Trend describes relationship as positive or negative, linearity for what shape the relationship takes (line, curved) , and strength how close the relationship fits to the data. May vary - heteroscedasticity.

Question 26

Lurking Variable

Answer 26

A hidden third variable muddling relationship between dependent and independent.

Question 27

Midrange

Answer 27

Midpoint between minimum and maximum in a data set.

Question 28

Range

Answer 28

Numerical distance between minimum and maximum

Question 29

Binomial Distribution

Answer 29

X is acting as the number of successes in n independent trials, with p probability of success. P (x=K successe) = nCx p^k (1-p)^n-k.

Question 30

Binomial Mean (Expected Value)

Answer 30

=np.

Question 31

Binomial Standard Deviation

Answer 31

= (np(1-p))^0.5

Question 32

Geometric Distribution:

Answer 32

X acts as the number of trials until success. With P (x=k successes) = (1-p)^k-1 p

Question 33

Geometric Mean (Expected Value)

Answer 33

=1/p. Trials till nth success is mean * n.

Question 34

Geometric Standard Deviation

Answer 34

= (1-p)^0.5 / p

Question 35

Confidence Interval

Answer 35

A P confident interval consists of those population percentages p where sample proportion p hat is reasonably likely. = p hat + or minus margin of error.

Question 36

Rate of Capture

Answer 36

For 95%, 95 out of 100 times the true proportion of the population should be within the calculated interval.

Question 37

Margin of Error:

Answer 37

= Z score of desired confidence interval * (pq/n) ^0.5.