Open Notebook: Initial analysis of metric system, scientific literacy and attitude survey result

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> #2013 April 2
> # analysis of metrics, scientific literacy and attitude survey
> # Hong Qin
>
> rm(list=ls())
> list.files()
[1] "analysis20130402.R" "old" "out.txt"
[4] "response20130401.csv" "Rplots.pdf"
> tb.ori = read.csv("response20130401.csv")
> str(tb.ori)
'data.frame': 196 obs. of 24 variables:
$ Timestamp : Factor w/ 196 levels "3/13/2013 14:58:00",..: 172 173 174 175 176 177 178 179 180 181 ...
$ Please.indicate.your.gender : Factor w/ 4 levels "","Do not wish to answer",..: 2 4 3 2 3 3 3 3 3 4 ...
$ Please.indicate.your.age.category : Factor w/ 8 levels "18-22","23-30",..: 1 1 3 8 5 6 1 4 3 3 ...
$ What.is.the.highest.education.that.you.received.or.are.pursing. : Factor w/ 6 levels "Bachelor Degree in Arts or equivalent",..: 2 3 3 2 3 1 3 5 6 6 ...
$ Please.indicate.the.country.in.which.you.grew.up. : Factor w/ 24 levels "","Australia",..: 24 24 24 24 24 24 24 24 24 6 ...
$ Light.is.both.a.wave.and.a.particle : Factor w/ 4 levels "","I don't know.",..: 3 3 3 4 2 4 3 4 3 3 ...
$ A.man.is.2.16.meters.tall..Is.this.person.suited.to.be.a.good.professional.basketball.player. : Factor w/ 3 levels "I don't know.",..: 3 2 2 3 2 2 1 2 3 2 ...
$ A.30.year.old.scientist.found.a.6.million.year.old.fossil..When.this.scientist.becomes.35.years.old..the.age.of.his.fossil.should.be..: Factor w/ 4 levels "","6 million and 5 years old",..: 2 2 2 4 2 4 2 4 4 2 ...
$ X.Kilo..means : Factor w/ 5 levels "","10 x","100 x",..: 4 4 3 4 2 4 2 4 4 4 ...
$ X145.mm...___.m : num 0.145 0.145 1.45 0.145 0.145 0.145 1.45 0.145 0.145 1.45 ...
$ Do.you.agree.that.organic.food.should.be.DNA.free.food. : Factor w/ 3 levels "Agree","Dis-agree",..: 3 2 2 2 2 2 1 2 2 2 ...
$ A.person.s.pant.inseam.measures.35.centimeters. : Factor w/ 4 levels "","I don't know",..: 4 3 3 3 4 3 2 3 3 3 ...
$ The.weather.forecast.shows.a.high.of.32.degrees.Celcius..what.should.you.wear. : Factor w/ 4 levels "A light jacket",..: 3 2 1 3 3 2 3 2 2 2 ...
$ What.is.an.electron.attracted.to. : Factor w/ 5 levels "","Electricity¬†",..: 3 5 5 5 2 5 5 5 5 5 ...
$ Early.human.once.lived.with.dinosaurs. : Factor w/ 3 levels "FALSE","I do not know.",..: 1 1 3 1 1 1 1 1 1 1 ...
$ Lasers.work.by.focusing.sound.waves : Factor w/ 3 levels "FALSE","I do not know. ",..: 3 1 1 1 1 1 2 1 3 1 ...
$ The.continents.have.been.moving.their.location.for.millions.of.years.and.will.continue.to.move. : Factor w/ 4 levels "","FALSE","I do not know. ",..: 4 4 4 4 2 4 4 4 4 4 ...
$ Antibiotics.kills.viruses.as.well.as.bacteria. : Factor w/ 3 levels "FALSE","I do not know.",..: 1 1 1 1 3 1 1 1 1 1 ...
$ Electrons.are.smaller.than.atoms : Factor w/ 4 levels "","FALSE","I do no know. ",..: 4 4 4 4 4 4 4 4 4 4 ...
$ The.center.of.the.earth.is.very.hot. : Factor w/ 3 levels "FALSE","I do not know.",..: 3 3 3 3 2 3 3 3 3 3 ...
$ My.religious.views.are.more.important.than.scientific.views. : Factor w/ 4 levels "","I do not know",..: 4 4 4 3 3 3 3 3 3 3 ...
$ For.me..in.my.daily.life..it.is.not.important.to.know.about.science. : Factor w/ 4 levels "FALSE","Maybe",..: 1 1 3 1 1 1 4 1 1 1 ...
$ Science.and.technology.are.making.our.lives.healthier..easier.and.more.comfortable. : Factor w/ 3 levels "FALSE","Not sure",..: 3 3 3 3 3 3 3 3 3 3 ...
$ The.benefits.of.science.are.greater.than.any.harmful.effects.it.may.have. : Factor w/ 3 levels "FALSE","Not sure",..: 3 3 1 2 2 3 1 2 3 3 ...
> tb = tb.ori
> #rename the columns for convenience
> names(tb) = c("time","gender", "age", "degree", "country", "light", "shaq", "fossil", "kilo", "mm",
+ "food","inseam", "weather","electronCharge","earlyHuman",
+ "laser", "continents", "antibiotics", "electronSize","earthCenter",
+ "religiousView","dailyLife","SciOnLife", "SciEffect")
>
> #visual check of the renaming
> head(tb)[1:10]
time gender age
1 3/5/2013 14:34:19 Do not wish to answer 18-22
2 3/5/2013 14:47:37 Male 18-22
3 3/5/2013 14:53:48 Female 31-40
4 3/5/2013 15:01:34 Do not wish to answer Option 5
5 3/5/2013 15:03:33 Female 51-55
6 3/5/2013 16:21:51 Female 56-60
degree country light shaq
1 Bachelor Degree in Science or equivalent United States TRUE Yes
2 High School or equivalent United States TRUE No
3 High School or equivalent United States TRUE No
4 Bachelor Degree in Science or equivalent United States Wrong Yes
5 High School or equivalent United States I don't know. No
6 Bachelor Degree in Arts or equivalent United States Wrong No
fossil kilo mm
1 6 million and 5 years old 1000 x 0.145
2 6 million and 5 years old 1000 x 0.145
3 6 million and 5 years old 100 x 1.450
4 Still about 6 million years old. 1000 x 0.145
5 6 million and 5 years old 10 x 0.145
6 Still about 6 million years old. 1000 x 0.145
> head(tb.ori)[1:10]
Timestamp Please.indicate.your.gender
1 3/5/2013 14:34:19 Do not wish to answer
2 3/5/2013 14:47:37 Male
3 3/5/2013 14:53:48 Female
4 3/5/2013 15:01:34 Do not wish to answer
5 3/5/2013 15:03:33 Female
6 3/5/2013 16:21:51 Female
Please.indicate.your.age.category
1 18-22
2 18-22
3 31-40
4 Option 5
5 51-55
6 56-60
What.is.the.highest.education.that.you.received.or.are.pursing.
1 Bachelor Degree in Science or equivalent
2 High School or equivalent
3 High School or equivalent
4 Bachelor Degree in Science or equivalent
5 High School or equivalent
6 Bachelor Degree in Arts or equivalent
Please.indicate.the.country.in.which.you.grew.up.
1 United States
2 United States
3 United States
4 United States
5 United States
6 United States
Light.is.both.a.wave.and.a.particle
1 TRUE
2 TRUE
3 TRUE
4 Wrong
5 I don't know.
6 Wrong
A.man.is.2.16.meters.tall..Is.this.person.suited.to.be.a.good.professional.basketball.player.
1 Yes
2 No
3 No
4 Yes
5 No
6 No
A.30.year.old.scientist.found.a.6.million.year.old.fossil..When.this.scientist.becomes.35.years.old..the.age.of.his.fossil.should.be..
1 6 million and 5 years old
2 6 million and 5 years old
3 6 million and 5 years old
4 Still about 6 million years old.
5 6 million and 5 years old
6 Still about 6 million years old.
X.Kilo..means X145.mm...___.m
1 1000 x 0.145
2 1000 x 0.145
3 100 x 1.450
4 1000 x 0.145
5 10 x 0.145
6 1000 x 0.145
> head(tb)[10:15]
mm food inseam weather electronCharge
1 0.145 I don't know This person is tall A winter coat Negative charge
2 0.145 Dis-agree This person is short A Short sleeve shirt Positive charge
3 1.450 Dis-agree This person is short A light jacket Positive charge
4 0.145 Dis-agree This person is short A winter coat Positive charge
5 0.145 Dis-agree This person is tall A winter coat Electricity¬†
6 0.145 Dis-agree This person is short A Short sleeve shirt Positive charge
earlyHuman
1 FALSE
2 FALSE
3 TRUE
4 FALSE
5 FALSE
6 FALSE
> head(tb.ori)[10:15]
X145.mm...___.m Do.you.agree.that.organic.food.should.be.DNA.free.food.
1 0.145 I don't know
2 0.145 Dis-agree
3 1.450 Dis-agree
4 0.145 Dis-agree
5 0.145 Dis-agree
6 0.145 Dis-agree
A.person.s.pant.inseam.measures.35.centimeters.
1 This person is tall
2 This person is short
3 This person is short
4 This person is short
5 This person is tall
6 This person is short
The.weather.forecast.shows.a.high.of.32.degrees.Celcius..what.should.you.wear.
1 A winter coat
2 A Short sleeve shirt
3 A light jacket
4 A winter coat
5 A winter coat
6 A Short sleeve shirt
What.is.an.electron.attracted.to. Early.human.once.lived.with.dinosaurs.
1 Negative charge FALSE
2 Positive charge FALSE
3 Positive charge TRUE
4 Positive charge FALSE
5 Electricity¬† FALSE
6 Positive charge FALSE
> head(tb)[16:20]
laser continents antibiotics electronSize earthCenter
1 TRUE TRUE FALSE True¬† TRUE
2 FALSE TRUE FALSE True¬† TRUE
3 FALSE TRUE FALSE True¬† TRUE
4 FALSE TRUE FALSE True¬† TRUE
5 FALSE FALSE TRUE True¬† I do not know.
6 FALSE TRUE FALSE True¬† TRUE
> head(tb.ori)[16:20]
Lasers.work.by.focusing.sound.waves
1 TRUE
2 FALSE
3 FALSE
4 FALSE
5 FALSE
6 FALSE
The.continents.have.been.moving.their.location.for.millions.of.years.and.will.continue.to.move.
1 TRUE
2 TRUE
3 TRUE
4 TRUE
5 FALSE
6 TRUE
Antibiotics.kills.viruses.as.well.as.bacteria.
1 FALSE
2 FALSE
3 FALSE
4 FALSE
5 TRUE
6 FALSE
Electrons.are.smaller.than.atoms The.center.of.the.earth.is.very.hot.
1 True¬† TRUE
2 True¬† TRUE
3 True¬† TRUE
4 True¬† TRUE
5 True¬† I do not know.
6 True¬† TRUE
> head(tb)[21:24]
religiousView dailyLife SciOnLife SciEffect
1 Yes FALSE TRUE TRUE
2 Yes FALSE TRUE TRUE
3 Yes Neutral TRUE FALSE
4 No FALSE TRUE Not sure
5 No FALSE TRUE Not sure
6 No FALSE TRUE TRUE
> head(tb.ori)[21:24]
My.religious.views.are.more.important.than.scientific.views.
1 Yes
2 Yes
3 Yes
4 No
5 No
6 No
For.me..in.my.daily.life..it.is.not.important.to.know.about.science.
1 FALSE
2 FALSE
3 Neutral
4 FALSE
5 FALSE
6 FALSE
Science.and.technology.are.making.our.lives.healthier..easier.and.more.comfortable.
1 TRUE
2 TRUE
3 TRUE
4 TRUE
5 TRUE
6 TRUE
The.benefits.of.science.are.greater.than.any.harmful.effects.it.may.have.
1 TRUE
2 TRUE
3 FALSE
4 Not sure
5 Not sure
6 TRUE
>
> head(tb)
time gender age
1 3/5/2013 14:34:19 Do not wish to answer 18-22
2 3/5/2013 14:47:37 Male 18-22
3 3/5/2013 14:53:48 Female 31-40
4 3/5/2013 15:01:34 Do not wish to answer Option 5
5 3/5/2013 15:03:33 Female 51-55
6 3/5/2013 16:21:51 Female 56-60
degree country light shaq
1 Bachelor Degree in Science or equivalent United States TRUE Yes
2 High School or equivalent United States TRUE No
3 High School or equivalent United States TRUE No
4 Bachelor Degree in Science or equivalent United States Wrong Yes
5 High School or equivalent United States I don't know. No
6 Bachelor Degree in Arts or equivalent United States Wrong No
fossil kilo mm food
1 6 million and 5 years old 1000 x 0.145 I don't know
2 6 million and 5 years old 1000 x 0.145 Dis-agree
3 6 million and 5 years old 100 x 1.450 Dis-agree
4 Still about 6 million years old. 1000 x 0.145 Dis-agree
5 6 million and 5 years old 10 x 0.145 Dis-agree
6 Still about 6 million years old. 1000 x 0.145 Dis-agree
inseam weather electronCharge earlyHuman laser
1 This person is tall A winter coat Negative charge FALSE TRUE
2 This person is short A Short sleeve shirt Positive charge FALSE FALSE
3 This person is short A light jacket Positive charge TRUE FALSE
4 This person is short A winter coat Positive charge FALSE FALSE
5 This person is tall A winter coat Electricity¬† FALSE FALSE
6 This person is short A Short sleeve shirt Positive charge FALSE FALSE
continents antibiotics electronSize earthCenter religiousView dailyLife
1 TRUE FALSE True¬† TRUE Yes FALSE
2 TRUE FALSE True¬† TRUE Yes FALSE
3 TRUE FALSE True¬† TRUE Yes Neutral
4 TRUE FALSE True¬† TRUE No FALSE
5 FALSE TRUE True¬† I do not know. No FALSE
6 TRUE FALSE True¬† TRUE No FALSE
SciOnLife SciEffect
1 TRUE TRUE
2 TRUE TRUE
3 TRUE FALSE
4 TRUE Not sure
5 TRUE Not sure
6 TRUE TRUE
> table(tb$gender)

Do not wish to answer Female
1 3 107
Male
85
> tb$gender[tb$gender=='']='Do not wish to answer'
>
> table(tb$age)

18-22 23-30 31-40
75 32 23
41-50 51-55 56-60
18 12 15
More than 60 years old Option 5
20 1
> tb$age[tb$age=="Option 5"] = NA
> table(tb$age, tb$gender)

Do not wish to answer Female Male
18-22 0 1 59 15
23-30 0 0 20 12
31-40 0 0 9 14
41-50 0 0 7 11
51-55 0 1 2 9
56-60 0 1 4 10
More than 60 years old 0 0 6 14
Option 5 0 0 0 0
>
> for( i in 5:length(tb[, 1])) {
+ for( j in 5:length(tb[1, ])) {
+ if ( is.na(tb[i, j]) ) {
+ # tb[i,j] = NA #do nothing
+ } else if (tb[i,j]=='') {
+ tb[i,j] = NA
+ }
+ }
+ }
>
> summary(tb)
time gender
3/13/2013 14:58:00: 1 : 0
3/18/2013 12:21:55: 1 Do not wish to answer: 4
3/25/2013 15:19:50: 1 Female :107
3/25/2013 15:29:20: 1 Male : 85
3/25/2013 15:29:24: 1
3/25/2013 16:41:29: 1
(Other) :190
age degree
18-22 :75 Bachelor Degree in Arts or equivalent :38
23-30 :32 Bachelor Degree in Science or equivalent:39
31-40 :23 High School or equivalent :58
More than 60 years old:20 M.D. or equivalent : 1
41-50 :18 Master Degree or equivalent :25
(Other) :27 Ph.D. or equivalent :35
NA's : 1
country light shaq
United States :155 : 0 I don't know.: 27
United Kingdom: 8 I don't know.: 9 No : 29
Australia : 3 TRUE :151 Yes :140
Canada : 3 Wrong : 34
China : 2 NA's : 2
(Other) : 23
NA's : 2
fossil kilo mm
: 0 : 0 Min. : 0.01
6 million and 5 years old : 67 10 x : 6 1st Qu.: 0.14
I don't know : 8 100 x : 13 Median : 0.14
Still about 6 million years old.:120 1000 x:173 Mean : 1495.29
NA's : 1 5 x : 1 3rd Qu.: 1.12
NA's : 3 Max. :145000.00
NA's :2
food inseam weather
Agree : 33 : 0 A light jacket : 25
Dis-agree :118 I don't know : 32 A Short sleeve shirt:131
I don't know: 45 This person is short:127 A winter coat : 26
This person is tall : 36 I don't know : 14
NA's : 1

electronCharge earlyHuman laser
: 0 FALSE :153 FALSE :134
Electricity¬† : 5 I do not know.: 16 I do not know. : 37
Negative charge: 29 TRUE : 27 TRUE : 25
Neutron : 22
Positive charge:139
NA's : 1

continents antibiotics electronSize
: 0 FALSE :133 : 0
FALSE : 4 I do not know.: 11 FALSE : 33
I do not know. : 7 TRUE : 52 I do no know. : 14
TRUE :184 True¬† :148
NA's : 1 NA's : 1


earthCenter religiousView dailyLife SciOnLife
FALSE : 6 : 0 FALSE :138 FALSE : 8
I do not know.: 9 I do not know: 15 Maybe : 12 Not sure: 18
TRUE :181 No :111 Neutral: 25 TRUE :170
Yes : 68 TRUE : 21
NA's : 2


SciEffect
FALSE : 31
Not sure: 56
TRUE :109

>
> table(tb$weather)

A light jacket A Short sleeve shirt A winter coat
25 131 26
I don't know
14
> tbGenWeather = table(tb$gender, tb$weather)
> tbGenWeather

A light jacket A Short sleeve shirt A winter coat
0 0 0
Do not wish to answer 1 1 2
Female 21 59 17
Male 3 71 7

I don't know
0
Do not wish to answer 0
Female 10
Male 4
> fisher.test( tbGenWeather)

Fisher's Exact Test for Count Data

data: tbGenWeather
p-value = 9.176e-05
alternative hypothesis: two.sided

>
> head(tb)
time gender age
1 3/5/2013 14:34:19 Do not wish to answer 18-22
2 3/5/2013 14:47:37 Male 18-22
3 3/5/2013 14:53:48 Female 31-40
4 3/5/2013 15:01:34 Do not wish to answer <NA>
5 3/5/2013 15:03:33 Female 51-55
6 3/5/2013 16:21:51 Female 56-60
degree country light shaq
1 Bachelor Degree in Science or equivalent United States TRUE Yes
2 High School or equivalent United States TRUE No
3 High School or equivalent United States TRUE No
4 Bachelor Degree in Science or equivalent United States Wrong Yes
5 High School or equivalent United States I don't know. No
6 Bachelor Degree in Arts or equivalent United States Wrong No
fossil kilo mm food
1 6 million and 5 years old 1000 x 0.145 I don't know
2 6 million and 5 years old 1000 x 0.145 Dis-agree
3 6 million and 5 years old 100 x 1.450 Dis-agree
4 Still about 6 million years old. 1000 x 0.145 Dis-agree
5 6 million and 5 years old 10 x 0.145 Dis-agree
6 Still about 6 million years old. 1000 x 0.145 Dis-agree
inseam weather electronCharge earlyHuman laser
1 This person is tall A winter coat Negative charge FALSE TRUE
2 This person is short A Short sleeve shirt Positive charge FALSE FALSE
3 This person is short A light jacket Positive charge TRUE FALSE
4 This person is short A winter coat Positive charge FALSE FALSE
5 This person is tall A winter coat Electricity¬† FALSE FALSE
6 This person is short A Short sleeve shirt Positive charge FALSE FALSE
continents antibiotics electronSize earthCenter religiousView dailyLife
1 TRUE FALSE True¬† TRUE Yes FALSE
2 TRUE FALSE True¬† TRUE Yes FALSE
3 TRUE FALSE True¬† TRUE Yes Neutral
4 TRUE FALSE True¬† TRUE No FALSE
5 FALSE TRUE True¬† I do not know. No FALSE
6 TRUE FALSE True¬† TRUE No FALSE
SciOnLife SciEffect
1 TRUE TRUE
2 TRUE TRUE
3 TRUE FALSE
4 TRUE Not sure
5 TRUE Not sure
6 TRUE TRUE
> names(tb)
[1] "time" "gender" "age" "degree"
[5] "country" "light" "shaq" "fossil"
[9] "kilo" "mm" "food" "inseam"
[13] "weather" "electronCharge" "earlyHuman" "laser"
[17] "continents" "antibiotics" "electronSize" "earthCenter"
[21] "religiousView" "dailyLife" "SciOnLife" "SciEffect"
> metrics = c("shaq", "kilo", "mm", "inseam", "weather")
> sciLiteracy = c("light", "fossil", "food", "electronCharge",
+ "earlyHuman", "laser", "continents", "antibiotics", "electronSize", "earthCenter")
> sciAttitude = c("religiousView", "dailyLife", "SciOnLife", "SciEffect")
>
> ##### create a second table, convert factors to numerics
> tb2 = tb[,2:5] #this is the score table
>
> ###country
> tb2$country = 0
> tb2$country[tb$country=='United States'] = 1
> table( tb2$country )

0 1
41 155
> table( tb$country )

Australia Bahamas Canada
0 3 1 3
China Croatia France Ghana
2 1 1 1
Guyana India Jamaica Kenya
1 1 1 2
Lebanon Mexico Norway Poland
1 1 1 2
Russian Federation Rwanda Senegal South Africa
2 2 1 2
Syria Trinidad & Tobago United Kingdom United States
1 1 8 155
>
> ########calculate the metric scores
> tb2$shaq = 0.5
> tb2$shaq[ tb$shaq=='Yes' ] = 1
> tb2$shaq[ tb$shaq=='No' ] = 0
> table(tb2$shaq)

0 0.5 1
29 27 140
>
> tb2$kilo = 0
> tb2$kilo[ tb$kilo=='1000 x' ] = 1
> table(tb2$kilo)

0 1
23 173
>
> tb2$mm=0
> tb2$mm[ tb$mm==0.145 ] = 1
> table(tb2$mm)

0 1
79 117
> table(tb$mm)

0.0145 0.145 1.45 145000
28 117 47 2
>
> tb2$inseam = 0.5
> tb2$inseam[tb$inseam=="This person is short"] = 1
> tb2$inseam[tb$inseam=="This person is tall"] = 0
> table(tb2$inseam)

0 0.5 1
36 33 127
>
> tb2$weather = 0.5
> tb2$weather[tb$weather=="A Short sleeve shirt"] = 1
> tb2$weather[tb$weather=="A winter coat"] = 0
> table(tb$weather)

A light jacket A Short sleeve shirt A winter coat
25 131 26
I don't know
14
> table(tb2$weather)

0 0.5 1
26 39 131
>
> #testing the grep function
> #tb$weather[ grep("shirt", tb$weather) ]
>
> #######calcualte the science attitude scores
> #sciAttitude = c("religiousView", "dailyLife", "SciOnLife", "SciEffect")
>
> tb2$religiousView = 0.5
> tb2$religiousView[grep("No", tb$religiousView)] = 1
> tb2$religiousView[grep("Yes", tb$religiousView)] = 0
> table(tb2$religiousView)

0 0.5 1
68 17 111
>
> tb2$dailyLife = 0.5
> tb2$dailyLife[ tb$dailyLife=='TRUE' ] = 0
> tb2$dailyLife[ tb$dailyLife=='FALSE' ] = 1
> table(tb2$dailyLife)

0 0.5 1
21 37 138
>
> tb2$SciOnLife = 0.5
> tb2$SciOnLife[ tb$SciOnLife=='TRUE' ] = 1
> tb2$SciOnLife[ tb$SciOnLife=='FALSE' ] = 0
> table(tb2$SciOnLife)

0 0.5 1
8 18 170
>
> tb2$SciEffect = 0.5
> tb2$SciEffect[ tb$SciEffect=='TRUE' ] = 1
> tb2$SciEffect[ tb$SciEffect=='FALSE' ] = 0
> table( tb2$SciEffect )

0 0.5 1
31 56 109
>
> ###########calculate scientific literacy
> #sciLiteracy = c("light", "fossil", "food", "electronCharge",
> # "earlyHuman", "laser", "continents", "antibiotics", "electronSize", "earthCenter")
> tb2$light = 0.5
> tb2$light[ tb$light=='TRUE' ] =1
> tb2$light[ tb$light=='Wrong' ] =0
> table(tb$light)

I don't know. TRUE Wrong
0 9 151 34
> table(tb2$light)

0 0.5 1
34 11 151
>
> tb2$fossil = 0.5
> tb2$fossil[ tb$fossil=='6 million and 5 years old' ] = 0
> tb2$fossil[grep('Still', tb$fossil)] = 1;
> table(tb$fossil)

6 million and 5 years old
0 67
I don't know Still about 6 million years old.
8 120
> table(tb2$fossil)

0 0.5 1
67 9 120
>
> tb2$food = 0.5
> tb2$food[ tb$food=='Dis-agree' ] = 1
> tb2$food[grep('Agree', tb$food)] = 0;
> table(tb$food)

Agree Dis-agree I don't know
33 118 45
> table(tb2$food)

0 0.5 1
33 45 118
>
> tb2$electronCharge = 0
> tb2$electronCharge[grep('Positive', tb$electronCharge)] = 1;
> table(tb$electronCharge)

Electricity¬† Negative charge Neutron Positive charge
0 5 29 22 139
> table(tb2$electronCharge)

0 1
57 139
>
> tb2$earlyHuman = 0.5
> tb2$earlyHuman[grep('TRUE', tb$earlyHuman)] = 0;
> tb2$earlyHuman[grep('FALSE', tb$earlyHuman)] = 1;
> table(tb$earlyHuman)

FALSE I do not know. TRUE
153 16 27
> table(tb2$earlyHuman)

0 0.5 1
27 16 153
>
> tb2$earlyHuman = 0.5
> tb2$earlyHuman[grep('TRUE', tb$earlyHuman)] = 0;
> tb2$earlyHuman[grep('FALSE', tb$earlyHuman)] = 1;
> table(tb$earlyHuman)

FALSE I do not know. TRUE
153 16 27
> table(tb2$earlyHuman)

0 0.5 1
27 16 153
>
> tb2$laser = 0.5
> tb2$laser[grep('TRUE', tb$laser)] = 0;
> tb2$laser[grep('FALSE', tb$laser)] = 1;
> table(tb$laser)

FALSE I do not know. TRUE
134 37 25
> table(tb2$laser)

0 0.5 1
25 37 134
>
> tb2$continents = 0.5
> tb2$continents[grep('TRUE', tb$continents)] = 1;
> tb2$continents[grep('FALSE', tb$continents)] = 0;
> table(tb$continents)

FALSE I do not know. TRUE
0 4 7 184
> table(tb2$continents)

0 0.5 1
4 8 184
>
> tb2$antibiotics = 0.5
> tb2$antibiotics[grep('TRUE', tb$antibiotics)] = 0;
> tb2$antibiotics[grep('FALSE', tb$antibiotics)] = 1;
> table(tb$antibiotics)

FALSE I do not know. TRUE
133 11 52
> table(tb2$antibiotics)

0 0.5 1
52 11 133
>
> tb2$electronSize = 0.5
> tb2$electronSize[grep('True', tb$electronSize)] = 1;
> tb2$electronSize[grep('FALSE', tb$electronSize)] = 0;
> table(tb$electronSize)

FALSE I do no know. True¬†
0 33 14 148
> table(tb2$electronSize)

0 0.5 1
33 15 148
>
> tb2$earthCenter = 0.5
> tb2$earthCenter[grep('TRUE', tb$earthCenter)] = 1;
> tb2$earthCenter[grep('FALSE', tb$earthCenter)] = 0;
> table(tb$earthCenter)

FALSE I do not know. TRUE
6 9 181
> table(tb2$earthCenter)

0 0.5 1
6 9 181
>
> #sciLiteracy = c("light", "fossil", "food", "electronCharge",
> # "earlyHuman", "laser", "continents", "antibiotics", "electronSize", "earthCenter")
>
> tb2$SciLitScore = apply( tb2[, sciLiteracy], MARGIN=1, FUN=sum ) #by row
> hist(tb2$SciLitScore, br=20)
>
> #sciAttitude = c("religiousView", "dailyLife", "SciOnLife", "SciEffect")
> #Attitude total score
> tb2$SciAttitude = apply( tb2[, sciAttitude], MARGIN=1, FUN=sum)
>
> #metrics = c("shaq", "kilo", "mm", "inseam", "weather")
> #metric total score
> tb2$metric = apply( tb2[, metrics], MARGIN=1, FUN=sum )
> hist(tb2$metric, br=20)
>
> summary(tb)
time gender
3/13/2013 14:58:00: 1 : 0
3/18/2013 12:21:55: 1 Do not wish to answer: 4
3/25/2013 15:19:50: 1 Female :107
3/25/2013 15:29:20: 1 Male : 85
3/25/2013 15:29:24: 1
3/25/2013 16:41:29: 1
(Other) :190
age degree
18-22 :75 Bachelor Degree in Arts or equivalent :38
23-30 :32 Bachelor Degree in Science or equivalent:39
31-40 :23 High School or equivalent :58
More than 60 years old:20 M.D. or equivalent : 1
41-50 :18 Master Degree or equivalent :25
(Other) :27 Ph.D. or equivalent :35
NA's : 1
country light shaq
United States :155 : 0 I don't know.: 27
United Kingdom: 8 I don't know.: 9 No : 29
Australia : 3 TRUE :151 Yes :140
Canada : 3 Wrong : 34
China : 2 NA's : 2
(Other) : 23
NA's : 2
fossil kilo mm
: 0 : 0 Min. : 0.01
6 million and 5 years old : 67 10 x : 6 1st Qu.: 0.14
I don't know : 8 100 x : 13 Median : 0.14
Still about 6 million years old.:120 1000 x:173 Mean : 1495.29
NA's : 1 5 x : 1 3rd Qu.: 1.12
NA's : 3 Max. :145000.00
NA's :2
food inseam weather
Agree : 33 : 0 A light jacket : 25
Dis-agree :118 I don't know : 32 A Short sleeve shirt:131
I don't know: 45 This person is short:127 A winter coat : 26
This person is tall : 36 I don't know : 14
NA's : 1

electronCharge earlyHuman laser
: 0 FALSE :153 FALSE :134
Electricity¬† : 5 I do not know.: 16 I do not know. : 37
Negative charge: 29 TRUE : 27 TRUE : 25
Neutron : 22
Positive charge:139
NA's : 1

continents antibiotics electronSize
: 0 FALSE :133 : 0
FALSE : 4 I do not know.: 11 FALSE : 33
I do not know. : 7 TRUE : 52 I do no know. : 14
TRUE :184 True¬† :148
NA's : 1 NA's : 1


earthCenter religiousView dailyLife SciOnLife
FALSE : 6 : 0 FALSE :138 FALSE : 8
I do not know.: 9 I do not know: 15 Maybe : 12 Not sure: 18
TRUE :181 No :111 Neutral: 25 TRUE :170
Yes : 68 TRUE : 21
NA's : 2


SciEffect
FALSE : 31
Not sure: 56
TRUE :109

> summary(tb2)
gender age
: 0 18-22 :75
Do not wish to answer: 4 23-30 :32
Female :107 31-40 :23
Male : 85 More than 60 years old:20
41-50 :18
(Other) :27
NA's : 1
degree country
Bachelor Degree in Arts or equivalent :38 Min. :0.0000
Bachelor Degree in Science or equivalent:39 1st Qu.:1.0000
High School or equivalent :58 Median :1.0000
M.D. or equivalent : 1 Mean :0.7908
Master Degree or equivalent :25 3rd Qu.:1.0000
Ph.D. or equivalent :35 Max. :1.0000

shaq kilo mm inseam
Min. :0.0000 Min. :0.0000 Min. :0.0000 Min. :0.0000
1st Qu.:0.5000 1st Qu.:1.0000 1st Qu.:0.0000 1st Qu.:0.5000
Median :1.0000 Median :1.0000 Median :1.0000 Median :1.0000
Mean :0.7832 Mean :0.8827 Mean :0.5969 Mean :0.7321
3rd Qu.:1.0000 3rd Qu.:1.0000 3rd Qu.:1.0000 3rd Qu.:1.0000
Max. :1.0000 Max. :1.0000 Max. :1.0000 Max. :1.0000

weather religiousView dailyLife SciOnLife
Min. :0.0000 Min. :0.0000 Min. :0.0000 Min. :0.0000
1st Qu.:0.5000 1st Qu.:0.0000 1st Qu.:0.5000 1st Qu.:1.0000
Median :1.0000 Median :1.0000 Median :1.0000 Median :1.0000
Mean :0.7679 Mean :0.6097 Mean :0.7985 Mean :0.9133
3rd Qu.:1.0000 3rd Qu.:1.0000 3rd Qu.:1.0000 3rd Qu.:1.0000
Max. :1.0000 Max. :1.0000 Max. :1.0000 Max. :1.0000

SciEffect light fossil food
Min. :0.000 Min. :0.0000 Min. :0.0000 Min. :0.0000
1st Qu.:0.500 1st Qu.:1.0000 1st Qu.:0.0000 1st Qu.:0.5000
Median :1.000 Median :1.0000 Median :1.0000 Median :1.0000
Mean :0.699 Mean :0.7985 Mean :0.6352 Mean :0.7168
3rd Qu.:1.000 3rd Qu.:1.0000 3rd Qu.:1.0000 3rd Qu.:1.0000
Max. :1.000 Max. :1.0000 Max. :1.0000 Max. :1.0000

electronCharge earlyHuman laser continents
Min. :0.0000 Min. :0.0000 Min. :0.0000 Min. :0.0000
1st Qu.:0.0000 1st Qu.:1.0000 1st Qu.:0.5000 1st Qu.:1.0000
Median :1.0000 Median :1.0000 Median :1.0000 Median :1.0000
Mean :0.7092 Mean :0.8214 Mean :0.7781 Mean :0.9592
3rd Qu.:1.0000 3rd Qu.:1.0000 3rd Qu.:1.0000 3rd Qu.:1.0000
Max. :1.0000 Max. :1.0000 Max. :1.0000 Max. :1.0000

antibiotics electronSize earthCenter SciLitScore
Min. :0.0000 Min. :0.0000 Min. :0.0000 Min. : 3.000
1st Qu.:0.0000 1st Qu.:1.0000 1st Qu.:1.0000 1st Qu.: 6.500
Median :1.0000 Median :1.0000 Median :1.0000 Median : 8.000
Mean :0.7066 Mean :0.7934 Mean :0.9464 Mean : 7.865
3rd Qu.:1.0000 3rd Qu.:1.0000 3rd Qu.:1.0000 3rd Qu.: 9.000
Max. :1.0000 Max. :1.0000 Max. :1.0000 Max. :10.000

SciAttitude metric
Min. :1.00 Min. :1.000
1st Qu.:2.50 1st Qu.:3.000
Median :3.00 Median :4.000
Mean :3.02 Mean :3.763
3rd Qu.:4.00 3rd Qu.:5.000
Max. :4.00 Max. :5.000

> str(tb2);
'data.frame': 196 obs. of 26 variables:
$ gender : Factor w/ 4 levels "","Do not wish to answer",..: 2 4 3 2 3 3 3 3 3 4 ...
$ age : Factor w/ 8 levels "18-22","23-30",..: 1 1 3 NA 5 6 1 4 3 3 ...
$ degree : Factor w/ 6 levels "Bachelor Degree in Arts or equivalent",..: 2 3 3 2 3 1 3 5 6 6 ...
$ country : num 1 1 1 1 1 1 1 1 1 0 ...
$ shaq : num 1 0 0 1 0 0 0.5 0 1 0 ...
$ kilo : num 1 1 0 1 0 1 0 1 1 1 ...
$ mm : num 1 1 0 1 1 1 0 1 1 0 ...
$ inseam : num 0 1 1 1 0 1 0.5 1 1 1 ...
$ weather : num 0 1 0.5 0 0 1 0 1 1 1 ...
$ religiousView : num 0 0 0 1 1 1 1 1 1 1 ...
$ dailyLife : num 1 1 0.5 1 1 1 0 1 1 1 ...
$ SciOnLife : num 1 1 1 1 1 1 1 1 1 1 ...
$ SciEffect : num 1 1 0 0.5 0.5 1 0 0.5 1 1 ...
$ light : num 1 1 1 0 0.5 0 1 0 1 1 ...
$ fossil : num 0 0 0 1 0 1 0 1 1 0 ...
$ food : num 0.5 1 1 1 1 1 0 1 1 1 ...
$ electronCharge: num 0 1 1 1 0 1 1 1 1 1 ...
$ earlyHuman : num 1 1 0 1 1 1 1 1 1 1 ...
$ laser : num 0 1 1 1 1 1 0.5 1 0 1 ...
$ continents : num 1 1 1 1 0 1 1 1 1 1 ...
$ antibiotics : num 1 1 1 1 0 1 1 1 1 1 ...
$ electronSize : num 1 1 1 1 1 1 1 1 1 1 ...
$ earthCenter : num 1 1 1 1 0.5 1 1 1 1 1 ...
$ SciLitScore : num 6.5 9 8 9 5 9 7.5 9 9 9 ...
$ SciAttitude : num 3 3 1.5 3.5 3.5 4 2 3.5 4 4 ...
$ metric : num 3 4 1.5 4 1 4 1 4 5 3 ...
> str(tb)
'data.frame': 196 obs. of 24 variables:
$ time : Factor w/ 196 levels "3/13/2013 14:58:00",..: 172 173 174 175 176 177 178 179 180 181 ...
$ gender : Factor w/ 4 levels "","Do not wish to answer",..: 2 4 3 2 3 3 3 3 3 4 ...
$ age : Factor w/ 8 levels "18-22","23-30",..: 1 1 3 NA 5 6 1 4 3 3 ...
$ degree : Factor w/ 6 levels "Bachelor Degree in Arts or equivalent",..: 2 3 3 2 3 1 3 5 6 6 ...
$ country : Factor w/ 24 levels "","Australia",..: 24 24 24 24 24 24 24 24 24 6 ...
$ light : Factor w/ 4 levels "","I don't know.",..: 3 3 3 4 2 4 3 4 3 3 ...
$ shaq : Factor w/ 3 levels "I don't know.",..: 3 2 2 3 2 2 1 2 3 2 ...
$ fossil : Factor w/ 4 levels "","6 million and 5 years old",..: 2 2 2 4 2 4 2 4 4 2 ...
$ kilo : Factor w/ 5 levels "","10 x","100 x",..: 4 4 3 4 2 4 2 4 4 4 ...
$ mm : num 0.145 0.145 1.45 0.145 0.145 0.145 1.45 0.145 0.145 1.45 ...
$ food : Factor w/ 3 levels "Agree","Dis-agree",..: 3 2 2 2 2 2 1 2 2 2 ...
$ inseam : Factor w/ 4 levels "","I don't know",..: 4 3 3 3 4 3 2 3 3 3 ...
$ weather : Factor w/ 4 levels "A light jacket",..: 3 2 1 3 3 2 3 2 2 2 ...
$ electronCharge: Factor w/ 5 levels "","Electricity¬†",..: 3 5 5 5 2 5 5 5 5 5 ...
$ earlyHuman : Factor w/ 3 levels "FALSE","I do not know.",..: 1 1 3 1 1 1 1 1 1 1 ...
$ laser : Factor w/ 3 levels "FALSE","I do not know. ",..: 3 1 1 1 1 1 2 1 3 1 ...
$ continents : Factor w/ 4 levels "","FALSE","I do not know. ",..: 4 4 4 4 2 4 4 4 4 4 ...
$ antibiotics : Factor w/ 3 levels "FALSE","I do not know.",..: 1 1 1 1 3 1 1 1 1 1 ...
$ electronSize : Factor w/ 4 levels "","FALSE","I do no know. ",..: 4 4 4 4 4 4 4 4 4 4 ...
$ earthCenter : Factor w/ 3 levels "FALSE","I do not know.",..: 3 3 3 3 2 3 3 3 3 3 ...
$ religiousView : Factor w/ 4 levels "","I do not know",..: 4 4 4 3 3 3 3 3 3 3 ...
$ dailyLife : Factor w/ 4 levels "FALSE","Maybe",..: 1 1 3 1 1 1 4 1 1 1 ...
$ SciOnLife : Factor w/ 3 levels "FALSE","Not sure",..: 3 3 3 3 3 3 3 3 3 3 ...
$ SciEffect : Factor w/ 3 levels "FALSE","Not sure",..: 3 3 1 2 2 3 1 2 3 3 ...
>
> pairs(tb2[, c("metric", "SciLitScore", "SciAttitude")])
> summary(lm(tb2$SciLitScore ~ tb2$metric )) #significant

Call:
lm(formula = tb2$SciLitScore ~ tb2$metric)

Residuals:
Min 1Q Median 3Q Max
-3.8633 -0.8633 0.1367 1.1367 3.5579

Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 4.82796 0.36510 13.224 < 2e-16 ***
tb2$metric 0.80708 0.09289 8.688 1.52e-15 ***
---
Signif. codes: 0 ‚Äò***‚Äô 0.001 ‚Äò**‚Äô 0.01 ‚Äò*‚Äô 0.05 ‚Äò.‚Äô 0.1 ‚Äò ‚Äô 1

Residual standard error: 1.477 on 194 degrees of freedom
Multiple R-squared: 0.2801, Adjusted R-squared: 0.2764
F-statistic: 75.49 on 1 and 194 DF, p-value: 1.524e-15

> summary(lm(tb2$SciAttitude ~ tb2$metric )) #significant

Call:
lm(formula = tb2$SciAttitude ~ tb2$metric)

Residuals:
Min 1Q Median 3Q Max
-2.0931 -0.4802 0.1004 0.6004 1.3263

Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.86726 0.18759 9.954 < 2e-16 ***
tb2$metric 0.30646 0.04773 6.421 1.02e-09 ***
---
Signif. codes: 0 ‚Äò***‚Äô 0.001 ‚Äò**‚Äô 0.01 ‚Äò*‚Äô 0.05 ‚Äò.‚Äô 0.1 ‚Äò ‚Äô 1

Residual standard error: 0.7587 on 194 degrees of freedom
Multiple R-squared: 0.1753, Adjusted R-squared: 0.171
F-statistic: 41.23 on 1 and 194 DF, p-value: 1.018e-09

> summary(lm(tb2$SciAttitude ~ tb2$SciLitScore + tb2$metric )) #significant

Call:
lm(formula = tb2$SciAttitude ~ tb2$SciLitScore + tb2$metric)

Residuals:
Min 1Q Median 3Q Max
-1.99696 -0.47506 0.08768 0.59403 1.35597

Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.64149 0.25826 6.356 1.46e-09 ***
tb2$SciLitScore 0.04676 0.03683 1.270 0.206
tb2$metric 0.26872 0.05616 4.785 3.40e-06 ***
---
Signif. codes: 0 ‚Äò***‚Äô 0.001 ‚Äò**‚Äô 0.01 ‚Äò*‚Äô 0.05 ‚Äò.‚Äô 0.1 ‚Äò ‚Äô 1

Residual standard error: 0.7575 on 193 degrees of freedom
Multiple R-squared: 0.1821, Adjusted R-squared: 0.1736
F-statistic: 21.49 on 2 and 193 DF, p-value: 3.761e-09

> ## metric -> SciAttitude and SciLitScore
>
> summary(lm(tb2$SciLitScore ~ tb2$metric + tb2$age + tb2$gender + tb2$country )) #significant

Call:
lm(formula = tb2$SciLitScore ~ tb2$metric + tb2$age + tb2$gender +
tb2$country)

Residuals:
Min 1Q Median 3Q Max
-3.5596 -0.9806 0.1084 1.0762 2.8657

Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 5.6267 0.9216 6.105 5.95e-09 ***
tb2$metric 0.6897 0.1009 6.832 1.19e-10 ***
tb2$age23-30 0.6247 0.3052 2.046 0.04213 *
tb2$age31-40 1.0488 0.3630 2.889 0.00433 **
tb2$age41-50 0.4278 0.3944 1.085 0.27946
tb2$age51-55 0.7283 0.4777 1.525 0.12909
tb2$age56-60 -0.1232 0.4323 -0.285 0.77601
tb2$ageMore than 60 years old 0.5206 0.3792 1.373 0.17149
tb2$genderFemale -1.1432 0.8633 -1.324 0.18706
tb2$genderMale -0.6237 0.8623 -0.723 0.47042
tb2$country 0.2313 0.2686 0.861 0.39029
---
Signif. codes: 0 ‚Äò***‚Äô 0.001 ‚Äò**‚Äô 0.01 ‚Äò*‚Äô 0.05 ‚Äò.‚Äô 0.1 ‚Äò ‚Äô 1

Residual standard error: 1.428 on 184 degrees of freedom
(1 observation deleted due to missingness)
Multiple R-squared: 0.3602, Adjusted R-squared: 0.3254
F-statistic: 10.36 on 10 and 184 DF, p-value: 8.714e-14

> summary(lm(tb2$SciLitScore ~ tb2$metric + tb2$age + tb2$gender + tb2$country + tb2$SciAttitude )) #significant

Call:
lm(formula = tb2$SciLitScore ~ tb2$metric + tb2$age + tb2$gender +
tb2$country + tb2$SciAttitude)

Residuals:
Min 1Q Median 3Q Max
-3.5702 -0.9920 0.0905 1.0577 2.8572

Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 5.58235 0.94501 5.907 1.66e-08 ***
tb2$metric 0.68266 0.10598 6.441 1.02e-09 ***
tb2$age23-30 0.61666 0.30810 2.001 0.04682 *
tb2$age31-40 1.04196 0.36524 2.853 0.00483 **
tb2$age41-50 0.42584 0.39547 1.077 0.28299
tb2$age51-55 0.71218 0.48430 1.471 0.14313
tb2$age56-60 -0.13642 0.43747 -0.312 0.75552
tb2$ageMore than 60 years old 0.50055 0.39054 1.282 0.20157
tb2$genderFemale -1.16071 0.86905 -1.336 0.18333
tb2$genderMale -0.65142 0.87337 -0.746 0.45671
tb2$country 0.23106 0.26929 0.858 0.39201
tb2$SciAttitude 0.03283 0.14661 0.224 0.82303
---
Signif. codes: 0 ‚Äò***‚Äô 0.001 ‚Äò**‚Äô 0.01 ‚Äò*‚Äô 0.05 ‚Äò.‚Äô 0.1 ‚Äò ‚Äô 1

Residual standard error: 1.432 on 183 degrees of freedom
(1 observation deleted due to missingness)
Multiple R-squared: 0.3603, Adjusted R-squared: 0.3219
F-statistic: 9.371 on 11 and 183 DF, p-value: 2.844e-13

>
> summary(lm(tb2$SciAttitude ~ tb2$metric + tb2$age + tb2$gender + tb2$country )) #significant

Call:
lm(formula = tb2$SciAttitude ~ tb2$metric + tb2$age + tb2$gender +
tb2$country)

Residuals:
Min 1Q Median 3Q Max
-1.9827 -0.4279 0.1154 0.5238 1.4062

Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.351907 0.464635 2.910 0.00407 **
tb2$metric 0.214443 0.050894 4.213 3.93e-05 ***
tb2$age23-30 0.243695 0.153886 1.584 0.11500
tb2$age31-40 0.208247 0.183022 1.138 0.25667
tb2$age41-50 0.058699 0.198815 0.295 0.76814
tb2$age51-55 0.490047 0.240836 2.035 0.04331 *
tb2$age56-60 0.402791 0.217971 1.848 0.06622 .
tb2$ageMore than 60 years old 0.609465 0.191176 3.188 0.00168 **
tb2$genderFemale 0.532760 0.435236 1.224 0.22249
tb2$genderMale 0.843844 0.434750 1.941 0.05379 .
tb2$country 0.007119 0.135413 0.053 0.95813
---
Signif. codes: 0 ‚Äò***‚Äô 0.001 ‚Äò**‚Äô 0.01 ‚Äò*‚Äô 0.05 ‚Äò.‚Äô 0.1 ‚Äò ‚Äô 1

Residual standard error: 0.7199 on 184 degrees of freedom
(1 observation deleted due to missingness)
Multiple R-squared: 0.2946, Adjusted R-squared: 0.2562
F-statistic: 7.684 on 10 and 184 DF, p-value: 3.169e-10

>
> ########test
> testTwoFactorTb2 = function( fac1, fac2) {
+ tbTwo = table( tb2[,fac1], tb2[,fac2] )
+ print(tbTwo)
+ f = fisher.test(tbTwo)
+ }
>
> #metrics = c("shaq", "kilo", "mm", "inseam", "weather")
> #sciLiteracy = c("light", "fossil", "food", "electronCharge",
> # "earlyHuman", "laser", "continents", "antibiotics", "electronSize", "earthCenter")
> #sciAttitude = c("religiousView", "dailyLife", "SciOnLife", "SciEffect")
>
> f = testTwoFactorTb2( "shaq", "religiousView"); f

0 0.5 1
0 15 1 13
0.5 11 4 12
1 42 12 86

Fisher's Exact Test for Count Data

data: tbTwo
p-value = 0.09939
alternative hypothesis: two.sided

> f = testTwoFactorTb2( "shaq", "dailyLife"); f

0 0.5 1
0 2 6 21
0.5 3 10 14
1 16 21 103

Fisher's Exact Test for Count Data

data: tbTwo
p-value = 0.1099
alternative hypothesis: two.sided

> f = testTwoFactorTb2( "shaq", "SciOnLife"); f

0 0.5 1
0 2 0 27
0.5 0 3 24
1 6 15 119

Fisher's Exact Test for Count Data

data: tbTwo
p-value = 0.2275
alternative hypothesis: two.sided

>
> f = testTwoFactorTb2( "shaq", "SciEffect"); f #significant effect!!!!

0 0.5 1
0 7 9 13
0.5 7 12 8
1 17 35 88

Fisher's Exact Test for Count Data

data: tbTwo
p-value = 0.009812
alternative hypothesis: two.sided

> f = testTwoFactorTb2( "kilo", "SciEffect"); f #significant effect!!!

0 0.5 1
0 8 7 8
1 23 49 101

Fisher's Exact Test for Count Data

data: tbTwo
p-value = 0.02352
alternative hypothesis: two.sided

> f = testTwoFactorTb2( "mm", "SciEffect"); f #significant effect!!!

0 0.5 1
0 20 22 37
1 11 34 72

Fisher's Exact Test for Count Data

data: tbTwo
p-value = 0.009951
alternative hypothesis: two.sided

> f = testTwoFactorTb2( "inseam", "SciEffect"); f #significant effect!!!

0 0.5 1
0 9 7 20
0.5 8 14 11
1 14 35 78

Fisher's Exact Test for Count Data

data: tbTwo
p-value = 0.01253
alternative hypothesis: two.sided

> f = testTwoFactorTb2( "weather", "SciEffect"); f #p=0.078

0 0.5 1
0 6 10 10
0.5 10 9 20
1 15 37 79

Fisher's Exact Test for Count Data

data: tbTwo
p-value = 0.07768
alternative hypothesis: two.sided

> f = testTwoFactorTb2( "country", "SciEffect"); f #p=0.24

0 0.5 1
0 3 12 26
1 28 44 83

Fisher's Exact Test for Count Data

data: tbTwo
p-value = 0.2366
alternative hypothesis: two.sided

>
> summary(lm(tb2$SciEffect ~ tb2$kilo + tb2$country + tb2$gender + tb2$age + tb2$degree )) #significant kilo

Call:
lm(formula = tb2$SciEffect ~ tb2$kilo + tb2$country + tb2$gender +
tb2$age + tb2$degree)

Residuals:
Min 1Q Median 3Q Max
-0.8335 -0.2211 0.1061 0.2569 0.6007

Coefficients:
Estimate Std. Error t value
(Intercept) 0.549685 0.239429 2.296
tb2$kilo 0.202271 0.085085 2.377
tb2$country -0.058298 0.069758 -0.836
tb2$genderFemale -0.057088 0.222510 -0.257
tb2$genderMale 0.089409 0.221791 0.403
tb2$age23-30 0.004917 0.081407 0.060
tb2$age31-40 0.076479 0.099473 0.769
tb2$age41-50 0.058817 0.108455 0.542
tb2$age51-55 0.209009 0.125430 1.666
tb2$age56-60 0.088502 0.112724 0.785
tb2$ageMore than 60 years old 0.124934 0.108513 1.151
tb2$degreeBachelor Degree in Science or equivalent -0.038077 0.087300 -0.436
tb2$degreeHigh School or equivalent -0.034963 0.078640 -0.445
tb2$degreeM.D. or equivalent 0.082156 0.381055 0.216
tb2$degreeMaster Degree or equivalent -0.018872 0.106131 -0.178
tb2$degreePh.D. or equivalent -0.098217 0.095039 -1.033
Pr(>|t|)
(Intercept) 0.0228 *
tb2$kilo 0.0185 *
tb2$country 0.4044
tb2$genderFemale 0.7978
tb2$genderMale 0.6873
tb2$age23-30 0.9519
tb2$age31-40 0.4430
tb2$age41-50 0.5883
tb2$age51-55 0.0974 .
tb2$age56-60 0.4334
tb2$ageMore than 60 years old 0.2511
tb2$degreeBachelor Degree in Science or equivalent 0.6632
tb2$degreeHigh School or equivalent 0.6571
tb2$degreeM.D. or equivalent 0.8295
tb2$degreeMaster Degree or equivalent 0.8591
tb2$degreePh.D. or equivalent 0.3028
---
Signif. codes: 0 ‚Äò***‚Äô 0.001 ‚Äò**‚Äô 0.01 ‚Äò*‚Äô 0.05 ‚Äò.‚Äô 0.1 ‚Äò ‚Äô 1

Residual standard error: 0.3629 on 179 degrees of freedom
(1 observation deleted due to missingness)
Multiple R-squared: 0.1335, Adjusted R-squared: 0.06091
F-statistic: 1.839 on 15 and 179 DF, p-value: 0.03247

> summary(lm(tb2$SciOnLife ~ tb2$kilo + tb2$country + tb2$gender + tb2$age + tb2$degree )) #no effect

Call:
lm(formula = tb2$SciOnLife ~ tb2$kilo + tb2$country + tb2$gender +
tb2$age + tb2$degree)

Residuals:
Min 1Q Median 3Q Max
-0.95129 0.01410 0.07902 0.11129 0.20841

Coefficients:
Estimate Std. Error t value
(Intercept) 1.017651 0.159053 6.398
tb2$kilo 0.037528 0.056522 0.664
tb2$country -0.051927 0.046340 -1.121
tb2$genderFemale -0.131418 0.147813 -0.889
tb2$genderMale -0.114197 0.147335 -0.775
tb2$age23-30 0.060025 0.054079 1.110
tb2$age31-40 -0.015880 0.066080 -0.240
tb2$age41-50 -0.032831 0.072046 -0.456
tb2$age51-55 0.035270 0.083323 0.423
tb2$age56-60 -0.001425 0.074883 -0.019
tb2$ageMore than 60 years old 0.047061 0.072085 0.653
tb2$degreeBachelor Degree in Science or equivalent 0.036818 0.057993 0.635
tb2$degreeHigh School or equivalent 0.019434 0.052240 0.372
tb2$degreeM.D. or equivalent 0.074898 0.253135 0.296
tb2$degreeMaster Degree or equivalent 0.064758 0.070503 0.919
tb2$degreePh.D. or equivalent -0.047408 0.063134 -0.751
Pr(>|t|)
(Intercept) 1.33e-09 ***
tb2$kilo 0.508
tb2$country 0.264
tb2$genderFemale 0.375
tb2$genderMale 0.439
tb2$age23-30 0.269
tb2$age31-40 0.810
tb2$age41-50 0.649
tb2$age51-55 0.673
tb2$age56-60 0.985
tb2$ageMore than 60 years old 0.515
tb2$degreeBachelor Degree in Science or equivalent 0.526
tb2$degreeHigh School or equivalent 0.710
tb2$degreeM.D. or equivalent 0.768
tb2$degreeMaster Degree or equivalent 0.360
tb2$degreePh.D. or equivalent 0.454
---
Signif. codes: 0 ‚Äò***‚Äô 0.001 ‚Äò**‚Äô 0.01 ‚Äò*‚Äô 0.05 ‚Äò.‚Äô 0.1 ‚Äò ‚Äô 1

Residual standard error: 0.241 on 179 degrees of freedom
(1 observation deleted due to missingness)
Multiple R-squared: 0.05604, Adjusted R-squared: -0.02307
F-statistic: 0.7084 on 15 and 179 DF, p-value: 0.774

> summary(lm(tb2$religiousView ~ tb2$kilo + tb2$country + tb2$gender + tb2$age + tb2$degree )) #age effect

Call:
lm(formula = tb2$religiousView ~ tb2$kilo + tb2$country + tb2$gender +
tb2$age + tb2$degree)

Residuals:
Min 1Q Median 3Q Max
-0.8435 -0.3345 0.0795 0.3265 0.7034

Coefficients:
Estimate Std. Error t value
(Intercept) 0.25085 0.27787 0.903
tb2$kilo -0.06561 0.09875 -0.664
tb2$country -0.03791 0.08096 -0.468
tb2$genderFemale 0.34987 0.25823 1.355
tb2$genderMale 0.51980 0.25740 2.019
tb2$age23-30 0.06767 0.09448 0.716
tb2$age31-40 0.15464 0.11544 1.340
tb2$age41-50 0.25771 0.12587 2.047
tb2$age51-55 0.31666 0.14557 2.175
tb2$age56-60 0.47689 0.13082 3.645
tb2$ageMore than 60 years old 0.33475 0.12593 2.658
tb2$degreeBachelor Degree in Science or equivalent -0.16877 0.10132 -1.666
tb2$degreeHigh School or equivalent -0.20056 0.09126 -2.198
tb2$degreeM.D. or equivalent 0.14033 0.44223 0.317
tb2$degreeMaster Degree or equivalent -0.08137 0.12317 -0.661
tb2$degreePh.D. or equivalent -0.08513 0.11030 -0.772
Pr(>|t|)
(Intercept) 0.36787
tb2$kilo 0.50725
tb2$country 0.64014
tb2$genderFemale 0.17716
tb2$genderMale 0.04493 *
tb2$age23-30 0.47475
tb2$age31-40 0.18210
tb2$age41-50 0.04207 *
tb2$age51-55 0.03091 *
tb2$age56-60 0.00035 ***
tb2$ageMore than 60 years old 0.00857 **
tb2$degreeBachelor Degree in Science or equivalent 0.09750 .
tb2$degreeHigh School or equivalent 0.02926 *
tb2$degreeM.D. or equivalent 0.75137
tb2$degreeMaster Degree or equivalent 0.50972
tb2$degreePh.D. or equivalent 0.44121
---
Signif. codes: 0 ‚Äò***‚Äô 0.001 ‚Äò**‚Äô 0.01 ‚Äò*‚Äô 0.05 ‚Äò.‚Äô 0.1 ‚Äò ‚Äô 1

Residual standard error: 0.4211 on 179 degrees of freedom
(1 observation deleted due to missingness)
Multiple R-squared: 0.2485, Adjusted R-squared: 0.1855
F-statistic: 3.945 on 15 and 179 DF, p-value: 3.962e-06

> summary(lm(tb2$dailyLife ~ tb2$kilo + tb2$country + tb2$gender + tb2$age + tb2$degree )) #gender

Call:
lm(formula = tb2$dailyLife ~ tb2$kilo + tb2$country + tb2$gender +
tb2$age + tb2$degree)

Residuals:
Min 1Q Median 3Q Max
-0.91792 -0.18296 0.09572 0.22627 0.62474

Coefficients:
Estimate Std. Error t value
(Intercept) 0.062095 0.213746 0.291
tb2$kilo 0.041672 0.075958 0.549
tb2$country 0.109441 0.062275 1.757
tb2$genderFemale 0.498227 0.198642 2.508
tb2$genderMale 0.553555 0.197999 2.796
tb2$age23-30 -0.015271 0.072675 -0.210
tb2$age31-40 0.026855 0.088802 0.302
tb2$age41-50 -0.205710 0.096821 -2.125
tb2$age51-55 0.070667 0.111975 0.631
tb2$age56-60 0.003735 0.100632 0.037
tb2$ageMore than 60 years old 0.068966 0.096873 0.712
tb2$degreeBachelor Degree in Science or equivalent 0.162050 0.077936 2.079
tb2$degreeHigh School or equivalent 0.036532 0.070204 0.520
tb2$degreeM.D. or equivalent 0.315824 0.340180 0.928
tb2$degreeMaster Degree or equivalent 0.275690 0.094746 2.910
tb2$degreePh.D. or equivalent 0.137518 0.084844 1.621
Pr(>|t|)
(Intercept) 0.77176
tb2$kilo 0.58395
tb2$country 0.08056 .
tb2$genderFemale 0.01303 *
tb2$genderMale 0.00574 **
tb2$age23-30 0.83381
tb2$age31-40 0.76269
tb2$age41-50 0.03499 *
tb2$age51-55 0.52878
tb2$age56-60 0.97043
tb2$ageMore than 60 years old 0.47744
tb2$degreeBachelor Degree in Science or equivalent 0.03902 *
tb2$degreeHigh School or equivalent 0.60345
tb2$degreeM.D. or equivalent 0.35445
tb2$degreeMaster Degree or equivalent 0.00408 **
tb2$degreePh.D. or equivalent 0.10681
---
Signif. codes: 0 ‚Äò***‚Äô 0.001 ‚Äò**‚Äô 0.01 ‚Äò*‚Äô 0.05 ‚Äò.‚Äô 0.1 ‚Äò ‚Äô 1

Residual standard error: 0.3239 on 179 degrees of freedom
(1 observation deleted due to missingness)
Multiple R-squared: 0.1558, Adjusted R-squared: 0.08502
F-statistic: 2.202 on 15 and 179 DF, p-value: 0.007798

>
> summary(lm(tb2$religiousView ~ tb2$kilo + tb2$country + tb2$gender + tb2$age + tb2$degree )) #significant age, gender

Call:
lm(formula = tb2$religiousView ~ tb2$kilo + tb2$country + tb2$gender +
tb2$age + tb2$degree)

Residuals:
Min 1Q Median 3Q Max
-0.8435 -0.3345 0.0795 0.3265 0.7034

Coefficients:
Estimate Std. Error t value
(Intercept) 0.25085 0.27787 0.903
tb2$kilo -0.06561 0.09875 -0.664
tb2$country -0.03791 0.08096 -0.468
tb2$genderFemale 0.34987 0.25823 1.355
tb2$genderMale 0.51980 0.25740 2.019
tb2$age23-30 0.06767 0.09448 0.716
tb2$age31-40 0.15464 0.11544 1.340
tb2$age41-50 0.25771 0.12587 2.047
tb2$age51-55 0.31666 0.14557 2.175
tb2$age56-60 0.47689 0.13082 3.645
tb2$ageMore than 60 years old 0.33475 0.12593 2.658
tb2$degreeBachelor Degree in Science or equivalent -0.16877 0.10132 -1.666
tb2$degreeHigh School or equivalent -0.20056 0.09126 -2.198
tb2$degreeM.D. or equivalent 0.14033 0.44223 0.317
tb2$degreeMaster Degree or equivalent -0.08137 0.12317 -0.661
tb2$degreePh.D. or equivalent -0.08513 0.11030 -0.772
Pr(>|t|)
(Intercept) 0.36787
tb2$kilo 0.50725
tb2$country 0.64014
tb2$genderFemale 0.17716
tb2$genderMale 0.04493 *
tb2$age23-30 0.47475
tb2$age31-40 0.18210
tb2$age41-50 0.04207 *
tb2$age51-55 0.03091 *
tb2$age56-60 0.00035 ***
tb2$ageMore than 60 years old 0.00857 **
tb2$degreeBachelor Degree in Science or equivalent 0.09750 .
tb2$degreeHigh School or equivalent 0.02926 *
tb2$degreeM.D. or equivalent 0.75137
tb2$degreeMaster Degree or equivalent 0.50972
tb2$degreePh.D. or equivalent 0.44121
---
Signif. codes: 0 ‚Äò***‚Äô 0.001 ‚Äò**‚Äô 0.01 ‚Äò*‚Äô 0.05 ‚Äò.‚Äô 0.1 ‚Äò ‚Äô 1

Residual standard error: 0.4211 on 179 degrees of freedom
(1 observation deleted due to missingness)
Multiple R-squared: 0.2485, Adjusted R-squared: 0.1855
F-statistic: 3.945 on 15 and 179 DF, p-value: 3.962e-06

> summary(lm(tb2$SciOnLife ~ tb2$kilo + tb2$country + tb2$gender + tb2$age + tb2$degree )) #no effect

Call:
lm(formula = tb2$SciOnLife ~ tb2$kilo + tb2$country + tb2$gender +
tb2$age + tb2$degree)

Residuals:
Min 1Q Median 3Q Max
-0.95129 0.01410 0.07902 0.11129 0.20841

Coefficients:
Estimate Std. Error t value
(Intercept) 1.017651 0.159053 6.398
tb2$kilo 0.037528 0.056522 0.664
tb2$country -0.051927 0.046340 -1.121
tb2$genderFemale -0.131418 0.147813 -0.889
tb2$genderMale -0.114197 0.147335 -0.775
tb2$age23-30 0.060025 0.054079 1.110
tb2$age31-40 -0.015880 0.066080 -0.240
tb2$age41-50 -0.032831 0.072046 -0.456
tb2$age51-55 0.035270 0.083323 0.423
tb2$age56-60 -0.001425 0.074883 -0.019
tb2$ageMore than 60 years old 0.047061 0.072085 0.653
tb2$degreeBachelor Degree in Science or equivalent 0.036818 0.057993 0.635
tb2$degreeHigh School or equivalent 0.019434 0.052240 0.372
tb2$degreeM.D. or equivalent 0.074898 0.253135 0.296
tb2$degreeMaster Degree or equivalent 0.064758 0.070503 0.919
tb2$degreePh.D. or equivalent -0.047408 0.063134 -0.751
Pr(>|t|)
(Intercept) 1.33e-09 ***
tb2$kilo 0.508
tb2$country 0.264
tb2$genderFemale 0.375
tb2$genderMale 0.439
tb2$age23-30 0.269
tb2$age31-40 0.810
tb2$age41-50 0.649
tb2$age51-55 0.673
tb2$age56-60 0.985
tb2$ageMore than 60 years old 0.515
tb2$degreeBachelor Degree in Science or equivalent 0.526
tb2$degreeHigh School or equivalent 0.710
tb2$degreeM.D. or equivalent 0.768
tb2$degreeMaster Degree or equivalent 0.360
tb2$degreePh.D. or equivalent 0.454
---
Signif. codes: 0 ‚Äò***‚Äô 0.001 ‚Äò**‚Äô 0.01 ‚Äò*‚Äô 0.05 ‚Äò.‚Äô 0.1 ‚Äò ‚Äô 1

Residual standard error: 0.241 on 179 degrees of freedom
(1 observation deleted due to missingness)
Multiple R-squared: 0.05604, Adjusted R-squared: -0.02307
F-statistic: 0.7084 on 15 and 179 DF, p-value: 0.774

> summary(lm(tb2$dailyLife ~ tb2$kilo + tb2$country + tb2$gender + tb2$age + tb2$degree )) #gender effect, education

Call:
lm(formula = tb2$dailyLife ~ tb2$kilo + tb2$country + tb2$gender +
tb2$age + tb2$degree)

Residuals:
Min 1Q Median 3Q Max
-0.91792 -0.18296 0.09572 0.22627 0.62474

Coefficients:
Estimate Std. Error t value
(Intercept) 0.062095 0.213746 0.291
tb2$kilo 0.041672 0.075958 0.549
tb2$country 0.109441 0.062275 1.757
tb2$genderFemale 0.498227 0.198642 2.508
tb2$genderMale 0.553555 0.197999 2.796
tb2$age23-30 -0.015271 0.072675 -0.210
tb2$age31-40 0.026855 0.088802 0.302
tb2$age41-50 -0.205710 0.096821 -2.125
tb2$age51-55 0.070667 0.111975 0.631
tb2$age56-60 0.003735 0.100632 0.037
tb2$ageMore than 60 years old 0.068966 0.096873 0.712
tb2$degreeBachelor Degree in Science or equivalent 0.162050 0.077936 2.079
tb2$degreeHigh School or equivalent 0.036532 0.070204 0.520
tb2$degreeM.D. or equivalent 0.315824 0.340180 0.928
tb2$degreeMaster Degree or equivalent 0.275690 0.094746 2.910
tb2$degreePh.D. or equivalent 0.137518 0.084844 1.621
Pr(>|t|)
(Intercept) 0.77176
tb2$kilo 0.58395
tb2$country 0.08056 .
tb2$genderFemale 0.01303 *
tb2$genderMale 0.00574 **
tb2$age23-30 0.83381
tb2$age31-40 0.76269
tb2$age41-50 0.03499 *
tb2$age51-55 0.52878
tb2$age56-60 0.97043
tb2$ageMore than 60 years old 0.47744
tb2$degreeBachelor Degree in Science or equivalent 0.03902 *
tb2$degreeHigh School or equivalent 0.60345
tb2$degreeM.D. or equivalent 0.35445
tb2$degreeMaster Degree or equivalent 0.00408 **
tb2$degreePh.D. or equivalent 0.10681
---
Signif. codes: 0 ‚Äò***‚Äô 0.001 ‚Äò**‚Äô 0.01 ‚Äò*‚Äô 0.05 ‚Äò.‚Äô 0.1 ‚Äò ‚Äô 1

Residual standard error: 0.3239 on 179 degrees of freedom
(1 observation deleted due to missingness)
Multiple R-squared: 0.1558, Adjusted R-squared: 0.08502
F-statistic: 2.202 on 15 and 179 DF, p-value: 0.007798

>
> summary(lm(tb2$SciEffect ~ tb2$mm + tb2$country + tb2$gender + tb2$age + tb2$degree )) #no effect

Call:
lm(formula = tb2$SciEffect ~ tb2$mm + tb2$country + tb2$gender +
tb2$age + tb2$degree)

Residuals:
Min 1Q Median 3Q Max
-0.84191 -0.22587 0.09511 0.28914 0.48110

Coefficients:
Estimate Std. Error t value
(Intercept) 0.62067 0.23807 2.607
tb2$mm 0.10057 0.05905 1.703
tb2$country -0.03996 0.07008 -0.570
tb2$genderFemale -0.03053 0.22370 -0.136
tb2$genderMale 0.12393 0.22251 0.557
tb2$age23-30 0.03514 0.08286 0.424
tb2$age31-40 0.06967 0.10020 0.695
tb2$age41-50 0.04963 0.10936 0.454
tb2$age51-55 0.15955 0.12735 1.253
tb2$age56-60 0.05654 0.11439 0.494
tb2$ageMore than 60 years old 0.11230 0.10906 1.030
tb2$degreeBachelor Degree in Science or equivalent -0.01984 0.08727 -0.227
tb2$degreeHigh School or equivalent -0.03128 0.07922 -0.395
tb2$degreeM.D. or equivalent 0.08516 0.38426 0.222
tb2$degreeMaster Degree or equivalent -0.01262 0.10746 -0.117
tb2$degreePh.D. or equivalent -0.08974 0.09573 -0.937
Pr(>|t|)
(Intercept) 0.0099 **
tb2$mm 0.0903 .
tb2$country 0.5693
tb2$genderFemale 0.8916
tb2$genderMale 0.5782
tb2$age23-30 0.6720
tb2$age31-40 0.4878
tb2$age41-50 0.6505
tb2$age51-55 0.2119
tb2$age56-60 0.6217
tb2$ageMore than 60 years old 0.3045
tb2$degreeBachelor Degree in Science or equivalent 0.8204
tb2$degreeHigh School or equivalent 0.6934
tb2$degreeM.D. or equivalent 0.8249
tb2$degreeMaster Degree or equivalent 0.9066
tb2$degreePh.D. or equivalent 0.3498
---
Signif. codes: 0 ‚Äò***‚Äô 0.001 ‚Äò**‚Äô 0.01 ‚Äò*‚Äô 0.05 ‚Äò.‚Äô 0.1 ‚Äò ‚Äô 1

Residual standard error: 0.3656 on 179 degrees of freedom
(1 observation deleted due to missingness)
Multiple R-squared: 0.1204, Adjusted R-squared: 0.04671
F-statistic: 1.634 on 15 and 179 DF, p-value: 0.06882

> summary(lm(tb2$SciEffect ~ tb2$inseam + tb2$country + tb2$gender + tb2$age + tb2$degree )) #random

Call:
lm(formula = tb2$SciEffect ~ tb2$inseam + tb2$country + tb2$gender +
tb2$age + tb2$degree)

Residuals:
Min 1Q Median 3Q Max
-0.8445 -0.2361 0.1071 0.2889 0.4879

Coefficients:
Estimate Std. Error t value
(Intercept) 0.629142 0.238932 2.633
tb2$inseam 0.088970 0.071644 1.242
tb2$country -0.046510 0.070319 -0.661
tb2$genderFemale -0.050022 0.225843 -0.221
tb2$genderMale 0.117379 0.224368 0.523
tb2$age23-30 0.016829 0.082266 0.205
tb2$age31-40 0.048870 0.102153 0.478
tb2$age41-50 0.045992 0.110058 0.418
tb2$age51-55 0.173723 0.127244 1.365
tb2$age56-60 0.057793 0.115406 0.501
tb2$ageMore than 60 years old 0.090152 0.109677 0.822
tb2$degreeBachelor Degree in Science or equivalent -0.002302 0.086559 -0.027
tb2$degreeHigh School or equivalent -0.020527 0.079404 -0.259
tb2$degreeM.D. or equivalent 0.115638 0.385002 0.300
tb2$degreeMaster Degree or equivalent 0.013715 0.106123 0.129
tb2$degreePh.D. or equivalent -0.078537 0.095617 -0.821
Pr(>|t|)
(Intercept) 0.0092 **
tb2$inseam 0.2159
tb2$country 0.5092
tb2$genderFemale 0.8250
tb2$genderMale 0.6015
tb2$age23-30 0.8381
tb2$age31-40 0.6329
tb2$age41-50 0.6765
tb2$age51-55 0.1739
tb2$age56-60 0.6171
tb2$ageMore than 60 years old 0.4122
tb2$degreeBachelor Degree in Science or equivalent 0.9788
tb2$degreeHigh School or equivalent 0.7963
tb2$degreeM.D. or equivalent 0.7643
tb2$degreeMaster Degree or equivalent 0.8973
tb2$degreePh.D. or equivalent 0.4125
---
Signif. codes: 0 ‚Äò***‚Äô 0.001 ‚Äò**‚Äô 0.01 ‚Äò*‚Äô 0.05 ‚Äò.‚Äô 0.1 ‚Äò ‚Äô 1

Residual standard error: 0.367 on 179 degrees of freedom
(1 observation deleted due to missingness)
Multiple R-squared: 0.1138, Adjusted R-squared: 0.03954
F-statistic: 1.532 on 15 and 179 DF, p-value: 0.09782

> summary(lm(tb2$SciEffect ~ tb2$shaq + tb2$country + tb2$gender + tb2$age + tb2$degree )) #p=0.066 shaq

Call:
lm(formula = tb2$SciEffect ~ tb2$shaq + tb2$country + tb2$gender +
tb2$age + tb2$degree)

Residuals:
Min 1Q Median 3Q Max
-0.8601 -0.2339 0.0977 0.2691 0.5307

Coefficients:
Estimate Std. Error t value
(Intercept) 0.553451 0.242835 2.279
tb2$shaq 0.140668 0.076161 1.847
tb2$country -0.029867 0.070380 -0.424
tb2$genderFemale -0.015014 0.223294 -0.067
tb2$genderMale 0.136150 0.221790 0.614
tb2$age23-30 0.004726 0.081970 0.058
tb2$age31-40 0.081995 0.100230 0.818
tb2$age41-50 0.047487 0.109246 0.435
tb2$age51-55 0.176785 0.126132 1.402
tb2$age56-60 0.091013 0.113507 0.802
tb2$ageMore than 60 years old 0.096935 0.108758 0.891
tb2$degreeBachelor Degree in Science or equivalent -0.031329 0.088150 -0.355
tb2$degreeHigh School or equivalent -0.039257 0.079439 -0.494
tb2$degreeM.D. or equivalent 0.087735 0.383519 0.229
tb2$degreeMaster Degree or equivalent 0.004961 0.105772 0.047
tb2$degreePh.D. or equivalent -0.094381 0.095810 -0.985
Pr(>|t|)
(Intercept) 0.0238 *
tb2$shaq 0.0664 .
tb2$country 0.6718
tb2$genderFemale 0.9465
tb2$genderMale 0.5401
tb2$age23-30 0.9541
tb2$age31-40 0.4144
tb2$age41-50 0.6643
tb2$age51-55 0.1628
tb2$age56-60 0.4237
tb2$ageMore than 60 years old 0.3740
tb2$degreeBachelor Degree in Science or equivalent 0.7227
tb2$degreeHigh School or equivalent 0.6218
tb2$degreeM.D. or equivalent 0.8193
tb2$degreeMaster Degree or equivalent 0.9626
tb2$degreePh.D. or equivalent 0.3259
---
Signif. codes: 0 ‚Äò***‚Äô 0.001 ‚Äò**‚Äô 0.01 ‚Äò*‚Äô 0.05 ‚Äò.‚Äô 0.1 ‚Äò ‚Äô 1

Residual standard error: 0.3651 on 179 degrees of freedom
(1 observation deleted due to missingness)
Multiple R-squared: 0.1229, Adjusted R-squared: 0.04938
F-statistic: 1.672 on 15 and 179 DF, p-value: 0.06009

> summary(lm(tb2$SciEffect ~ tb2$weather + tb2$country + tb2$gender + tb2$age + tb2$degree )) #no effect

Call:
lm(formula = tb2$SciEffect ~ tb2$weather + tb2$country + tb2$gender +
tb2$age + tb2$degree)

Residuals:
Min 1Q Median 3Q Max
-0.8569 -0.2395 0.1115 0.2922 0.4976

Coefficients:
Estimate Std. Error t value
(Intercept) 0.60791 0.24135 2.519
tb2$weather 0.09477 0.08287 1.144
tb2$country -0.03086 0.07134 -0.433
tb2$genderFemale -0.05128 0.22637 -0.227
tb2$genderMale 0.10802 0.22598 0.478
tb2$age23-30 0.01852 0.08238 0.225
tb2$age31-40 0.05866 0.10122 0.580
tb2$age41-50 0.05261 0.10983 0.479
tb2$age51-55 0.17717 0.12715 1.393
tb2$age56-60 0.06226 0.11513 0.541
tb2$ageMore than 60 years old 0.09632 0.10946 0.880
tb2$degreeBachelor Degree in Science or equivalent 0.01476 0.08681 0.170
tb2$degreeHigh School or equivalent -0.02334 0.07943 -0.294
tb2$degreeM.D. or equivalent 0.13063 0.38512 0.339
tb2$degreeMaster Degree or equivalent 0.01237 0.10632 0.116
tb2$degreePh.D. or equivalent -0.07918 0.09579 -0.827
Pr(>|t|)
(Intercept) 0.0127 *
tb2$weather 0.2543
tb2$country 0.6659
tb2$genderFemale 0.8211
tb2$genderMale 0.6332
tb2$age23-30 0.8223
tb2$age31-40 0.5629
tb2$age41-50 0.6325
tb2$age51-55 0.1652
tb2$age56-60 0.5893
tb2$ageMore than 60 years old 0.3801
tb2$degreeBachelor Degree in Science or equivalent 0.8652
tb2$degreeHigh School or equivalent 0.7692
tb2$degreeM.D. or equivalent 0.7349
tb2$degreeMaster Degree or equivalent 0.9075
tb2$degreePh.D. or equivalent 0.4096
---
Signif. codes: 0 ‚Äò***‚Äô 0.001 ‚Äò**‚Äô 0.01 ‚Äò*‚Äô 0.05 ‚Äò.‚Äô 0.1 ‚Äò ‚Äô 1

Residual standard error: 0.3672 on 179 degrees of freedom
(1 observation deleted due to missingness)
Multiple R-squared: 0.1126, Adjusted R-squared: 0.03829
F-statistic: 1.515 on 15 and 179 DF, p-value: 0.1038

>
>
> f = testTwoFactorTb2("country", "shaq")

0 0.5 1
0 2 4 35
1 27 23 105
> f

Fisher's Exact Test for Count Data

data: tbTwo
p-value = 0.06516
alternative hypothesis: two.sided

>
> f = testTwoFactorTb2( "country", "shaq")

0 0.5 1
0 2 4 35
1 27 23 105
> f

Fisher's Exact Test for Count Data

data: tbTwo
p-value = 0.06516
alternative hypothesis: two.sided

>
>
>

Open Notebook

Tuesday, April 2, 2013

Initial analysis of metric system, scientific literacy and attitude survey result

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