CQS 112 Exam 1

Variable

something that can change

Independent Variable

what is manipulated

Dependent Variable

what changes as a result

Data

measurements collected

Value

the actual number or category a subject has

Population

the entire group of interest

Sample

a subset of the population used for study

Parameter

a number describing the population

Statistic

a number describing the sample

Categorical (Nominal) Variable

groups or categories without any order

Rank-Order (Ordinal) Variable

categories that can be organized, but the difference between them aren’t equal

Interval Variable

numbers where the differences are meaningful, but there’s no true zero

Ratio Variable

numbers where the differences are meaningful and there is a true zero

Bar Chart

used for categorical variables and shows categories

Pie Chart

used for categorical variables and shows proportions of a whole

Histogram

used for numeric variables and shows the frequency of values

Stem-and-Leaf Plot

used for numeric variables and shows organized values

Boxplot

used for numeric variables and shows spread, quartiles, and outliers

Interquartile Range

Q3-Q1

Standard Deviation

a measure of how spread out the data is around the mean

Z Score

a way of standardizing a value to show how far it is away from the mean in terms of standard deviations

Probability

the expected relative frequency of an outcome over many trials

Discrete Random Variables

takes on countable values

Continuous Random Variables

takes any value within a range, including decimals

Random Variable

a variable whose value is determined by chance

Central Limit Theorem

if you take big enough random samples from any population, the averages of those samples will form a normal distribution

Law of Large Numbers

as the number of trials increases, your observed relative frequency approaches the true probability

OR Rule

P(A or B) = P(A) + P(B) - P(A and B)

And Rule

P(A and B) = P(A) * P(B)

Sampling Distributions

the distribution you get when you take a statistic and calculate it over and over again from many random samples of the same size from the same population

Sampling Variability

the variation that occurs in sample statistics from one sample to another due to random chance

Statistical Inference

using a sample to say something about a population

3 Types of Statistical Inferences

1. Point Estimation

2. Interval Estimation

3. Hypothesis Testing

Point Estimation

use on number from the sample as your best guess

Interval Estimation

give a range of plausible values

Hypothesis Testing

test a claim using sample data to determine if there is enough evidence to support a specific hypothesis

Confidence Interval

an interval that gives a range of likely values