Statistics Unit 1 exam practice

Categorical

(or qualitative or attribute) data consist of placing units or individuals into groups. some examples are political party, race, and color

Quantitative

(Or numerical) data consist of numbers representing counts or measurements, some examples are age, weight, and miles per gallon

Unit/ subject

Represents an individual person, animal, or object upon which the response variable or variable of interest is measured (Unit is always what you are randomly selecting)

Response Variable

Measures the outcome of a study and is what is measured or recorded for each unit

Explanatory Variable

Explains or causes changes in the response Variable

Census

is an attempt to contact every individual in the entire population

A simple Random sample (SRS)

A sample that is selected from the population in such a way that every SRS of units has an equal chance of being selected, this type of sample works well when the units are homogeneous with respect to the variable of interest, (is a random of probability-based sample.)

Voluntary Response Sample

Is a sample which consists of people who choose themselves by responding to a general appeal, this is a nonrandom of convivence sample

A stratified Random sampling 

Divide the population into subgroup(s) based on characteristics relevant to the study, then draw randomly from the subgroups. • Ex: I divide the class into Males and Females, then randomly grab 5 students from each group. 

Random or probability- based sampling

Where the sample is selected is such a way that each unit in the population has a non-zero chance of being chosen

Non- random or convenience sampling

sample of subjects used because they are convenient and available • Ex: I pick the 3 students who are closest to me to sample

Parameter 

If we took the average height of everyone in the world that would be a parameter. (Parameters are often unknown)

Statistic

If we took the average height of everyone in the sample that would be a statistic. (Statistics are knowable but random)

Population

the set of all the subjects of interest. •Look for keywords: “all” or “every”

Sample

a subset of the population for whom we have (or plan to have) data, often randomly selected. •Look for key phrase: “a sample of …” or “a survey”

Unimodal

•If there is a single peak in the distribution then it is called u

Bimodal

•If there are two peaks in the distribution then it is called

what Graphs for QUANTITATIVE

Histogram, Dot plot, stem & leaf

What graphs work for QUALITATIVE

Pie Chart, Bar Graph, Pareto Chart

Undercoverage Bias

If the sampling design systematically excludes a portion of the population and the excluded portion systematically differs with respect to the response from those units/individuals that are available for sampling, undercoverage sampling bias will be introduced into the study, Another way to look at undercoverage is first define the “sampling Frame”, the sampling frame is the list of units/ individuals included in the sampling frame and that is when undercovrage bias happens.

Non response Bias

Nonresponse happens when information cannot be found on a selected unit/individual either because the information is not available for that unit/individual. the individual cannot be contacted, or the person refuses to cooperate, sampling bias will be introduced into the study if the units/individual from which information was not obtained differ with respect to the response to those from which information was obtained

Response Bias

When a respondent lies or a recording error happens for a unity/individual, response bias is present, the behavior of the interviewer and the wording of a question can cause response bias.

Cofounding

Happens when many variables have an impact on another variable making it difficult to determine which one causes the greatest change

Lurking Variable

A variable that is not among the explanatory or response variables in a study yet many influence the interpretation of relationships among those variables

treatments

are specific experimental condition applied to the units

Factors

Are specific variables being tested

Factor level

A specific level of a factor

Completely Randomized design

Thet treatments are randomly assigned to the experimental units without restriction, all experimental units are assigned at random among all treatments

Randomized Block Design

The units are first divided up based on prior information and random assignment is done separately within the groups

Variables

Are characteristics of the units/individuals

Distribution of a variable

Describes what values a variable takes and how often it takes those values