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ENVS 203 > exam 1 > Flashcards

exam 1 Flashcards

1
Q

statistics

A

a field of mathematics that develops and studies methods to collect, analyze, interpret, and present empirical evidence

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2
Q

empirical vs anecdotal evidence

A

empirical - information received from the observation or measurements of patterns using experimentation
anecdotal - evidence collected in a casual or informal manner that relies heavily on personal testimony or conclusions (not statistical data collection)

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3
Q

data

A

a collection of numerical facts or information from which conclusions can be drawn

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4
Q

raw data

A

unformatted data (numerical measurements, instrument readings, text) that has not been processed or analyzed

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5
Q

replicates

A

parallel measurements of a phenomenon to estimate variability in your sample (the number of replicates = n)

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6
Q

sampling effort

A

how much data do we need?

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7
Q

precision and accuracy

A

precision - how fine the divisions on a scale of measurement are
accuracy - how close to the truth our measurement is
(accuracy is the priority)

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8
Q

descriptive statistics

A

quantitative description of observations sampled from a population (mathematically summarizing patterns, data centers, and variability without making conclusions about overall meaning of data)

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9
Q

data distribution (histogram)

A

sampled populations arranged by rank order and graphically presented

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10
Q

normal distribution

A

an arrangement of data in which most values cluster in the middle of the range and the rest taper off symmetrically toward either extreme

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11
Q

log-normal distribution

A

data are clustered at low values, but there are some much higher values (positive skew)
(can be made normal by applying a logarithm function)

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12
Q

central tendency

A

numeric value describing a central position in a dataset. mean, median, and mode are valid measures

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13
Q

skew

A

positive, negative, or normal

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14
Q

central limit theorem

A

if a population with finite variants is sufficiently sampled, the mean of all the samples from the population will be = approximately equal to the mean of the population, AND the means from the samples will approach a normal distribution

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15
Q

main steps in the scientific method

A

planning - what are you going to do? learn the system, develop ideas about how the system works (maybe do a pilot study), decide hypothesis, figure out what data you will need
recording - collect and properly accord data, can take many forms, must record extremely carefully
analysis - interrogate data to test hypothesis, analysis cannot be successful if data gathering was not designed with analysis in mind, should allow you to accept or reject null
reporting - disseminating methods and media will depend on the type of work and audience, statistical results must be reported using proper conventions, graphs must be properly labelled

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16
Q

types of data

A

continuous - data that can take any value (usually measured)
discrete - numerical data that can take a limited number of values (often counted)
ordinal - data in categories that can be placed in order, but magnitude of difference between categories is not fixed
categorical - data in categories that can’t be usefully ordered

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17
Q

null and alternative hypothesis

A

null hypothesis - no change (Ho)
alternative hypothesis - what you want to show (Ha or H1)

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18
Q

sampling strategies

A

random - best choice
systematic - transects (sampling on a created line)
mixed - stratified random sampling
haphazardly - when you are unable to randomly sample because of practicality

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19
Q

mean, median, mode

A

mean - sum of observations is divided by number of observations in the sample
median - the middle score for the sampled data that has been arranged by order of magnitude
mode - the most frequent score in a sampled dataset
(equations)

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20
Q

data in quartiles

A

divide data into quarters and use five number summary
steps -
rank data from smallest to largest
smallest is first number, largest is 5th
median is third
middle of first and third is second, middle of fifth and third is fourth

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21
Q

dividing n-1 to calculate variance

A

penalty for having a small amount of replicates

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22
Q

shapiro-wilk test and how to interpret

A

takes a data distribution and determines whether it is significantly different to normal
p-value of <.05 = not normal, reject Ho

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23
Q

standard error of the mean (SEM) (def and equation)

A

estimate of how close the sample mean is compared to the true population mean
standard deviation of resampled mean
=Sx/sqrt n

24
Q

types of project

A

descriptive -
differences - is a different to b, bar charts and box and whisker plots, categorical variable and want to know if the response variable differs between categories
correlations - links between variables, usually quantitative variables are independent and quantitative variables are dependent
associations - similar to correlations but with categorical data

25
Q

how to calculate mean

A

bar x = (E^n i=1 * xi)/n

26
Q

how to calculate median

A

the middle value

27
Q

how to calculate mode

A

most often occurring data

28
Q

how to calculate range

A

rank-order observations - highest - lowest

29
Q

how to calculate variance

A

=(E^n i=1(xi - bar x)^2)/n-1 OR = SS/n-1

30
Q

how to calculate standard deviation

A

= sqrt(E^n i=1 (xi - bar x)^2/n-1) OR = sqrt (SS/n-1)

31
Q

how to calculate standard error

A

=Sx/sqrt n

32
Q

copy>paste special> paste values in excel

A

makes values the actual number rather than the equation in a cell

33
Q

$ in excel

A

keeps a number the same to make a cell value unchangeable

34
Q

sum in excel

A

=SUM(array)

35
Q

count in excel

A

=COUNT(array)

36
Q

mean in excel

A

=AVERAGE(array)

37
Q

median in excel

A

=MEDIAN(array)

38
Q

mode in excel

A

=MODE(array)

39
Q

variance in excel

A

=VAR(array)

40
Q

standard deviation in excel

A

=STDEV(array)

41
Q

standard error of the mean in excel

A

=AVERAGE(array)/SQRT(count(array))

42
Q

list in R

A

ls()

43
Q

remove in R

A

rm(objectname)

44
Q

quit in R

A

q()

45
Q

import .csv in R

A

read.csv(file.choose())

46
Q

sum of values in a column in R

A

> sum(objectname$variablename)

47
Q

number of values in a column in R

A

> length(objectname$variablename)

48
Q

mean of values in a column in R

A

> mean(objectname$variablename)

49
Q

median of values in a column in R

A

> median(objectname$variablename)

50
Q

quartiles in R

A

> summary(objectname$variablename)

51
Q

plot quartiles as boxplot in R

A

> boxplot(objectname$variablename)

52
Q

plot histogram in R

A

> hist(objectname$variablename)

53
Q

variance in R

A

> var(objectname$variablename)

54
Q

standard deviation in R

A

> sd(objectname$variablename)

55
Q

shapiro-wilk test for normality in R

A

> shapiro-test(objectname$variablename)

ENVS 203 (3 decks)

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