Chapter 1

Question 1

Variable

Answer 1

A variable is any characteristic of an individual or case. Variables can be categorical in which they are placed in a group or category, or quantitative in which they are given as a number. For example, Professor Wilkinson assigned a student survey to get to know her class. Two of the variables included on the survey were age (quantitative) and favorite color (categorical).

Question 2

Response Variable

Answer 2

Also known as the “dependent variable”. The response variable is the one that we are trying to understand or predict, in relation to what occurs with the independent variable.

Question 3

Causation

Answer 3

Two variables are considered to be casually associated with each other if changing the value of one variable influences the value of the other variable. Causation can only be determined through a properly designed experiment. Just because there is a correlation or association between two variables does not mean you have causation, association is not causation

Question 4

Sample Design

Answer 4

Method used to select sample from the population

Question 5

Association

Answer 5

Association is when values of one variable tend to be related to the values of the other variable.

Question 6

Observational Study

Answer 6

A study in which the researcher does not actively control the value of any variable but simply observes the values as they naturally exist.
Example- Review medical records of patients who elected different treatments for high blood pressure.

Question 7

confounding variable

Answer 7

A confounding variable is an unknown variable, or third variable that is connected to the explanatory and response variables.It’s an confounding variable because it is not represented in the dataset, but that does not mean a confounding variable will not affect the data that is presented. Example- in a lecture about how on 1 graph, sales in ice cream increased, as did drownings. The confounding variable would be summertime activities (ice cream eating & swimming).

Question 8

Statistical Inference

Answer 8

Statistical inference is the process of using data analysis to infer properties of an underlying distribution of probability. Inferential statistical analysis infers properties of a population, for example by testing hypotheses and deriving estimates. The purpose of statistical inferences is to draw a conclusion or provide an explanation for data that has been derived.

Question 9

Simple Random Sample

Answer 9

A Simple Random Sample refers to a sampling method in which a sample is taken from an overall population randomly, where each member of said population has an equal chance of being selected, inorder to represent the entire data set in an unbiased fashion. Examples of this include the lottery method (drawing from a hat, etc.) or the random number method.