OpenIntro 1

Question 1

target population

Answer 1

Ex: all swordifsh in the Atlantic Ocean

Question 2

Sample

Answer 2

a subset of the cases

Question 3

Sample

Answer 3

a subset of the cases

Question 4

It is essential to draw a _________ sample, and avoid _____

Answer 4

representative, bias

Question 5

simple random sample

Answer 5

most basic type of random sample

Question 6

from a sample, you should be cautious if the

Answer 6

non-response is high

Question 7

convenience sample

Answer 7

where individuals who are easily accessible are more likely to be included in the sample, and is generally a bad basis for making conclusions about the population

Question 8

explanatory variable : federal spending or high rates of poverty? “Is federal spending on average, higher or lower in counties with high rates of poverty?”

Answer 8

high rates of poverty

Question 9

response variable: federal spending or high rates of poverty? “Is federal spending on average, higher or lower in counties with high rates of poverty?”

Answer 9

federal spending

Question 10

Association does not imply

Answer 10

causation

Question 11

observational study

Answer 11

collecting data in a way that does not directly interfere with how the data arise

Question 12

experiment

Answer 12

when researchers want to investigate the possibility of a causal connection

Question 13

control groups

Answer 13

does not recieve the “treatment” and may recieve a placebo

Question 14

causal conclusion

confounding variable

Answer 14

a conclusion based on observational variable. not good.

Question 15

histogram

Answer 15

a bar graph

Question 16

scatterplots

Answer 16

provides a case-by-case view of data for two numerical data

Question 17

placebo effect

Answer 17

a placebo results in a slight but real improvement in patients

Question 18

stratified sampling steps

Answer 18

strata created, then random sampling within the stratum

Question 19

strata

Answer 19

similar groups grouped together

Question 20

retrospective studies

Answer 20

collect data after events have taken place

Question 21

prospective studies

Answer 21

identifies individuals and collects information as events unfold

Question 22

confounding variable

Answer 22

a variable that is correlated with the explanatory and response variables

Question 23

confounding variable

Answer 23

a variable that is correlated with the explanatory and response variables

Question 24

the mean

Answer 24

the center of a distribution of data

Question 25

bin

Answer 25

a range of values

Question 26

Contingency Tables

Answer 26

summarize data for two categorical variables (ex: totals)

Question 27

multimodal

Answer 27

I statistikker er en multimodal fordeling en sandsynlighedsfordeling med to forskellige tilstande, kan også benævnes en bimodal fordeling. Disse fremstår som tydelige toppe i sandsynlighedsdensitetsfunktionen,

Question 28

bimodal

Answer 28

two prominent histogram peaks

Question 29

unimodal

Answer 29

one prominent histogram peak

Question 30

symmetric

Answer 30

data set histrograms with equally trailing tails on both sides

Question 31

left skewed

Answer 31

data sets histograms with a long thin tail to the left

Question 32

right skewed

Answer 32

a histogram with a longer right trail

Question 33

categorical variable

Answer 33

a variable that can take on one of a limited, and usually fixed, number of possible values, assigning each individual or other unit of observation to a particular group or nominal category on the basis of some qualitative property. ex: format : html/text

Question 34

continuous variable

Answer 34

is quantitative data that can be measured. • it has an infinite number of possible values within. a selected range e.g. temperature range.

Question 35

discrete data

Answer 35

is quantitative data that can be counted. and has a finite number of possible values. e.g. days of the week.

Question 36

null hypothesis

Answer 36

hypothesis of no difference between variable outcomes

Question 37

alternative hypothesis

Answer 37

not a null hypothesis

Question 38

frequency table

Answer 38

table for a single variable

Question 39

categorical

Answer 39

have values that describe a 'quality' or 'characteristic' of a data unit, like 'what type' or 'which category'. Categorical variables fall into mutually exclusive (in one category or in another) and exhaustive (include all possible options) categories. Therefore, categorical variables are qualitative variables and tend to be represented by a non-numeric value.

Question 40

nominal variable

Answer 40

is a categorical variable. Observations can take a value that is not able to be organised in a logical sequence. Examples of nominal categorical variables include sex, business type, eye colour, religion and brand.

Question 41

ordinal variable

Answer 41

is a categorical variable. Observations can take a value that can be logically ordered or ranked. The categories associated with ordinal variables can be ranked higher or lower than another, but do not necessarily establish a numeric difference between each category. Examples of ordinal categorical variables include academic grades (i.e. A, B, C), clothing size (i.e. small, medium, large, extra large) and attitudes (i.e. strongly agree, agree, disagree, strongly disagree).

Question 42

continuous

Answer 42

is a numeric variable. Observations can take any value between a certain set of real numbers. The value given to an observation for a continuous variable can include values as small as the instrument of measurement allows. Examples of continuous variables include height, time, age, and temperature.

Question 43

discrete

Answer 43

is a numeric variable. Observations can take a value based on a count from a set of distinct whole values. A discrete variable cannot take the value of a fraction between one value and the next closest value. Examples of discrete variables include the number of registered cars, number of business locations, and number of children in a family, all of of which measured as whole units (i.e. 1, 2, 3 cars).