Knowledge and Attitude Oral Health Mothers Latvia

require(tidyverse)
require(stringr)
require(forcats)
library(knitr)
require(broom)

locale("lv", asciify = TRUE)
options(digits=3)

1 Dataset

df <- read_csv("Dataset/Dati_anketas_dzemdibu_iestades_v2.csv")

renombro columnas

colnames(df) <- str_replace_all(colnames(df), "[? .]", "_")
colnames(df) <- str_replace(colnames(df), "Vieta,_kur_anketa_pildīta", "Pildita")
df$Pildita <- str_replace(df$Pildita, "cccc", "P.Stradiņa klīniskā universitātes slimnīca")

hist(df$Attitude, breaks = 4, 
     main = "Attitude", xlab = "Attitude")

hist(df$Knowledge, breaks = 7, 
     main = "Knowledge", xlab = "Knowledge")

Convertir attitude y knowledge en porcentaje

df <- df %>% 
        mutate(AttPerc = Attitude/4*100)
df <- df %>% 
        mutate(KnoPerc = Knowledge/8*100)

hist(df$AttPerc, breaks = 5, 
     main = "Attitude", xlab = "Attitude percentage")

hist(df$KnoPerc, breaks = 10, 
     main = "Knowledge", xlab = "Knowledge percentage")

2 Descriptive

Descriptivo de todo en porcentaje

2.1 Por variables 6, 7, 8, 9, 10, 12, 13, 17, 18, 19, 20

2.1.1 AGE

	important	not important
< 30 gadi	89.9	10.1
> 30 gadi	93.4	6.6

    Pearson's Chi-squared test with Yates' continuity correction

data:  table(df$Age_groups, df$`6__Is_it_important_to_take_care_about_deciduous_teeth_`)
X-squared = 6, df = 1, p-value = 0.02

	Juice or other drinks with sugar	Just water or herbal tea without sugar
< 30 gadi	12.08	87.9
> 30 gadi	7.45	92.5

    Pearson's Chi-squared test with Yates' continuity correction

data:  table(df$Age_groups, df$`7__Before_going_to_sleep,_what_could_you_offer_for_your_2-year_old_child_`)
X-squared = 9, df = 1, p-value = 0.003

	after eruption of the first milk tooth	don't know
< 30 gadi	36.2	63.8
> 30 gadi	48.3	51.7

    Pearson's Chi-squared test with Yates' continuity correction

data:  table(df$Age_groups, df$`8__In_which_age_you_should_begin_to_brush_your_childs_teeth_`)
X-squared = 20, df = 1, p-value = 1e-06

	don't know	toothbrush and toothpaste
< 30 gadi	74.3	25.7
> 30 gadi	77.1	22.9

    Pearson's Chi-squared test with Yates' continuity correction

data:  table(df$Age_groups, df$`9__What_would_you_have_to_use_for_brushing_your_childs_first_teeth_`)
X-squared = 2, df = 1, p-value = 0.2

	don' t know	toothpaste with fluorides
< 30 gadi	48.2	51.8
> 30 gadi	38.0	62.0

    Pearson's Chi-squared test with Yates' continuity correction

data:  table(df$Age_groups, df$`10__Which_toothpaste_would_you_choose_for_your_childs_first_teeth_`)
X-squared = 20, df = 1, p-value = 7e-05

	do not agree	don't know or agree
< 30 gadi	32.5	67.5
> 30 gadi	42.5	57.5

    Pearson's Chi-squared test with Yates' continuity correction

data:  table(df$Age_groups, df$`12__Fluoride_toothpaste_is_bad_for_my_child`)
X-squared = 20, df = 1, p-value = 5e-05

	1000 ppm	don't know
< 30 gadi	1.279	98.7
> 30 gadi	0.916	99.1

    Pearson's Chi-squared test with Yates' continuity correction

data:  table(df$Age_groups, df$`13__How_much_fluoride_should_a_0-3-year_old_childs_toothpaste_contain_`)
X-squared = 0.2, df = 1, p-value = 0.7

	don't know	pea size or as rice grain
< 30 gadi	10.91	89.1
> 30 gadi	9.01	91.0

    Pearson's Chi-squared test with Yates' continuity correction

data:  table(df$Age_groups, df$`17__How_much_toothpastes_should_be_squeezed_out_on_a_toothbrush_`)
X-squared = 1, df = 1, p-value = 0.2

	don't know	pēc vajadzības kad bērns atļauj tīrīt	twice daily
< 30 gadi	30.8	0.11	69.1
> 30 gadi	28.6	0.00	71.4

Chi-squared approximation may be incorrect

    Pearson's Chi-squared test

data:  table(df$Age_groups, df$`18_1_How_many_times_per_day_you_should_brush_your_childs_teeth_`)
X-squared = 2, df = 2, p-value = 0.5

	do not agree	don' t know or agree
< 30 gadi	60.4	39.6
> 30 gadi	59.3	40.7

    Pearson's Chi-squared test with Yates' continuity correction

data:  table(df$Age_groups, df$`19__Toothbrushing_against_a_childs’_will_is_aggression_towards_a_child_`)
X-squared = 0.2, df = 1, p-value = 0.7

	do not agree	don' t know or agree
< 30 gadi	89.5	10.5
> 30 gadi	89.6	10.4

    Pearson's Chi-squared test with Yates' continuity correction

data:  table(df$Age_groups, df$`20__It_is_better_to_not_to_brush_against_childs_will_as_the_milk_teeth_will_fall_out_anyway_`)
X-squared = 0, df = 1, p-value = 1

2.1.2 Lives in

	important	not important
Lauku teritorija	90.7	9.33
Pilsēta	90.3	9.69
Rīga vai Rīgas rajons	95.2	4.85

    Pearson's Chi-squared test

data:  table(df$Lives_in, df$`6__Is_it_important_to_take_care_about_deciduous_teeth_`)
X-squared = 7, df = 2, p-value = 0.02

	Juice or other drinks with sugar	Just water or herbal tea without sugar
Lauku teritorija	14.51	85.5
Pilsēta	9.75	90.2
Rīga vai Rīgas rajons	4.54	95.5

    Pearson's Chi-squared test

data:  table(df$Lives_in, df$`7__Before_going_to_sleep,_what_could_you_offer_for_your_2-year_old_child_`)
X-squared = 20, df = 2, p-value = 2e-05

	after eruption of the first milk tooth	don't know
Lauku teritorija	36.9	63.1
Pilsēta	39.6	60.4
Rīga vai Rīgas rajons	51.2	48.8

    Pearson's Chi-squared test

data:  table(df$Lives_in, df$`8__In_which_age_you_should_begin_to_brush_your_childs_teeth_`)
X-squared = 20, df = 2, p-value = 1e-04

	don't know	toothbrush and toothpaste
Lauku teritorija	79.0	21.0
Pilsēta	73.9	26.1
Rīga vai Rīgas rajons	73.6	26.4

    Pearson's Chi-squared test

data:  table(df$Lives_in, df$`9__What_would_you_have_to_use_for_brushing_your_childs_first_teeth_`)
X-squared = 5, df = 2, p-value = 0.08

	don' t know	toothpaste with fluorides
Lauku teritorija	48.0	52.0
Pilsēta	42.1	57.9
Rīga vai Rīgas rajons	43.0	57.0

    Pearson's Chi-squared test

data:  table(df$Lives_in, df$`10__Which_toothpaste_would_you_choose_for_your_childs_first_teeth_`)
X-squared = 5, df = 2, p-value = 0.1

	do not agree	don't know or agree
Lauku teritorija	35.3	64.7
Pilsēta	34.0	66.0
Rīga vai Rīgas rajons	44.9	55.1

    Pearson's Chi-squared test

data:  table(df$Lives_in, df$`12__Fluoride_toothpaste_is_bad_for_my_child`)
X-squared = 10, df = 2, p-value = 0.002

	1000 ppm	don't know
Lauku teritorija	1.365	98.6
Pilsēta	0.671	99.3
Rīga vai Rīgas rajons	1.852	98.1

Chi-squared approximation may be incorrect

    Pearson's Chi-squared test

data:  table(df$Lives_in, df$`13__How_much_fluoride_should_a_0-3-year_old_childs_toothpaste_contain_`)
X-squared = 3, df = 2, p-value = 0.2

	don't know	pea size or as rice grain
Lauku teritorija	7.51	92.5
Pilsēta	12.65	87.4
Rīga vai Rīgas rajons	8.36	91.6

    Pearson's Chi-squared test

data:  table(df$Lives_in, df$`17__How_much_toothpastes_should_be_squeezed_out_on_a_toothbrush_`)
X-squared = 10, df = 2, p-value = 0.006

	don't know	pēc vajadzības kad bērns atļauj tīrīt	twice daily
Lauku teritorija	31.5	0.000	68.5
Pilsēta	28.2	0.135	71.7
Rīga vai Rīgas rajons	31.3	0.000	68.7

Chi-squared approximation may be incorrect

    Pearson's Chi-squared test

data:  table(df$Lives_in, df$`18_1_How_many_times_per_day_you_should_brush_your_childs_teeth_`)
X-squared = 3, df = 4, p-value = 0.6

	do not agree	don' t know or agree
Lauku teritorija	56.2	43.8
Pilsēta	56.9	43.1
Rīga vai Rīgas rajons	72.7	27.3

    Pearson's Chi-squared test

data:  table(df$Lives_in, df$`19__Toothbrushing_against_a_childs’_will_is_aggression_towards_a_child_`)
X-squared = 30, df = 2, p-value = 1e-06

	do not agree	don' t know or agree
Lauku teritorija	88.7	11.3
Pilsēta	88.7	11.3
Rīga vai Rīgas rajons	92.9	7.1

    Pearson's Chi-squared test

data:  table(df$Lives_in, df$`20__It_is_better_to_not_to_brush_against_childs_will_as_the_milk_teeth_will_fall_out_anyway_`)
X-squared = 5, df = 2, p-value = 0.09

2.1.3 más hijos

	important	not important
No	92.5	7.47
Yes, first child	89.6	10.36

    Pearson's Chi-squared test with Yates' continuity correction

data:  table(df$`2_Is_it_your_first_child_`, df$`6__Is_it_important_to_take_care_about_deciduous_teeth_`)
X-squared = 4, df = 1, p-value = 0.05

	Juice or other drinks with sugar	Just water or herbal tea without sugar
No	9.88	90.1
Yes, first child	11.02	89.0

    Pearson's Chi-squared test with Yates' continuity correction

data:  table(df$`2_Is_it_your_first_child_`, df$`7__Before_going_to_sleep,_what_could_you_offer_for_your_2-year_old_child_`)
X-squared = 0.4, df = 1, p-value = 0.5

	after eruption of the first milk tooth	don't know
No	48.6	51.4
Yes, first child	29.2	70.8

    Pearson's Chi-squared test with Yates' continuity correction

data:  table(df$`2_Is_it_your_first_child_`, df$`8__In_which_age_you_should_begin_to_brush_your_childs_teeth_`)
X-squared = 60, df = 1, p-value = 1e-14

	don't know	toothbrush and toothpaste
No	74.7	25.3
Yes, first child	76.6	23.4

    Pearson's Chi-squared test with Yates' continuity correction

data:  table(df$`2_Is_it_your_first_child_`, df$`9__What_would_you_have_to_use_for_brushing_your_childs_first_teeth_`)
X-squared = 0.6, df = 1, p-value = 0.4

	don' t know	toothpaste with fluorides
No	37.5	62.5
Yes, first child	54.3	45.7

    Pearson's Chi-squared test with Yates' continuity correction

data:  table(df$`2_Is_it_your_first_child_`, df$`10__Which_toothpaste_would_you_choose_for_your_childs_first_teeth_`)
X-squared = 40, df = 1, p-value = 5e-11

	do not agree	don't know or agree
No	38.9	61.1
Yes, first child	33.3	66.7

    Pearson's Chi-squared test with Yates' continuity correction

data:  table(df$`2_Is_it_your_first_child_`, df$`12__Fluoride_toothpaste_is_bad_for_my_child`)
X-squared = 5, df = 1, p-value = 0.03

	1000 ppm	don't know
No	0.619	99.4
Yes, first child	1.893	98.1

    Pearson's Chi-squared test with Yates' continuity correction

data:  table(df$`2_Is_it_your_first_child_`, df$`13__How_much_fluoride_should_a_0-3-year_old_childs_toothpaste_contain_`)
X-squared = 5, df = 1, p-value = 0.03

	don't know	pea size or as rice grain
No	10.3	89.7
Yes, first child	10.3	89.7

    Pearson's Chi-squared test with Yates' continuity correction

data:  table(df$`2_Is_it_your_first_child_`, df$`17__How_much_toothpastes_should_be_squeezed_out_on_a_toothbrush_`)
X-squared = 5e-30, df = 1, p-value = 1

	don't know	pēc vajadzības kad bērns atļauj tīrīt	twice daily
No	28.2	0.103	71.7
Yes, first child	33.1	0.000	66.9

Chi-squared approximation may be incorrect

    Pearson's Chi-squared test

data:  table(df$`2_Is_it_your_first_child_`, df$`18_1_How_many_times_per_day_you_should_brush_your_childs_teeth_`)
X-squared = 5, df = 2, p-value = 0.09

	do not agree	don' t know or agree
No	56.2	43.8
Yes, first child	65.4	34.6

    Pearson's Chi-squared test with Yates' continuity correction

data:  table(df$`2_Is_it_your_first_child_`, df$`19__Toothbrushing_against_a_childs’_will_is_aggression_towards_a_child_`)
X-squared = 10, df = 1, p-value = 3e-04

	do not agree	don' t know or agree
No	88.3	11.7
Yes, first child	91.5	8.5

    Pearson's Chi-squared test with Yates' continuity correction

data:  table(df$`2_Is_it_your_first_child_`, df$`20__It_is_better_to_not_to_brush_against_childs_will_as_the_milk_teeth_will_fall_out_anyway_`)
X-squared = 4, df = 1, p-value = 0.05

2.1.4 income

	important	not important
300-700 EUR	89.7	10.32
< 300 EUR	77.6	22.38
> 700 EUR	95.9	4.15

    Pearson's Chi-squared test

data:  table(df$`29__Income_for_family`, df$`6__Is_it_important_to_take_care_about_deciduous_teeth_`)
X-squared = 50, df = 2, p-value = 2e-12

	Juice or other drinks with sugar	Just water or herbal tea without sugar
300-700 EUR	13.37	86.6
< 300 EUR	20.28	79.7
> 700 EUR	6.14	93.9

    Pearson's Chi-squared test

data:  table(df$`29__Income_for_family`, df$`7__Before_going_to_sleep,_what_could_you_offer_for_your_2-year_old_child_`)
X-squared = 30, df = 2, p-value = 6e-08

	after eruption of the first milk tooth	don't know
300-700 EUR	37.1	62.9
< 300 EUR	23.6	76.4
> 700 EUR	48.3	51.7

    Pearson's Chi-squared test

data:  table(df$`29__Income_for_family`, df$`8__In_which_age_you_should_begin_to_brush_your_childs_teeth_`)
X-squared = 40, df = 2, p-value = 8e-09

	don't know	toothbrush and toothpaste
300-700 EUR	76.0	24.0
< 300 EUR	73.4	26.6
> 700 EUR	74.4	25.6

    Pearson's Chi-squared test

data:  table(df$`29__Income_for_family`, df$`9__What_would_you_have_to_use_for_brushing_your_childs_first_teeth_`)
X-squared = 0.7, df = 2, p-value = 0.7

	don' t know	toothpaste with fluorides
300-700 EUR	44.2	55.8
< 300 EUR	49.6	50.4
> 700 EUR	41.4	58.6

    Pearson's Chi-squared test

data:  table(df$`29__Income_for_family`, df$`10__Which_toothpaste_would_you_choose_for_your_childs_first_teeth_`)
X-squared = 4, df = 2, p-value = 0.2

	do not agree	don't know or agree
300-700 EUR	34.3	65.7
< 300 EUR	20.4	79.6
> 700 EUR	43.0	57.0

    Pearson's Chi-squared test

data:  table(df$`29__Income_for_family`, df$`12__Fluoride_toothpaste_is_bad_for_my_child`)
X-squared = 30, df = 2, p-value = 4e-07

	1000 ppm	don't know
300-700 EUR	1.01	99.0
< 300 EUR	0.00	100.0
> 700 EUR	1.21	98.8

Chi-squared approximation may be incorrect

    Pearson's Chi-squared test

data:  table(df$`29__Income_for_family`, df$`13__How_much_fluoride_should_a_0-3-year_old_childs_toothpaste_contain_`)
X-squared = 2, df = 2, p-value = 0.4

	don't know	pea size or as rice grain
300-700 EUR	10.8	89.2
< 300 EUR	20.1	79.9
> 700 EUR	7.7	92.3

    Pearson's Chi-squared test

data:  table(df$`29__Income_for_family`, df$`17__How_much_toothpastes_should_be_squeezed_out_on_a_toothbrush_`)
X-squared = 20, df = 2, p-value = 5e-05

	don't know	pēc vajadzības kad bērns atļauj tīrīt	twice daily
300-700 EUR	32.2	0.145	67.6
< 300 EUR	39.0	0.000	61.0
> 700 EUR	25.7	0.000	74.3

Chi-squared approximation may be incorrect

    Pearson's Chi-squared test

data:  table(df$`29__Income_for_family`, df$`18_1_How_many_times_per_day_you_should_brush_your_childs_teeth_`)
X-squared = 10, df = 4, p-value = 0.007

	do not agree	don' t know or agree
300-700 EUR	56.0	44.0
< 300 EUR	46.8	53.2
> 700 EUR	65.7	34.3

    Pearson's Chi-squared test

data:  table(df$`29__Income_for_family`, df$`19__Toothbrushing_against_a_childs’_will_is_aggression_towards_a_child_`)
X-squared = 20, df = 2, p-value = 7e-06

	do not agree	don' t know or agree
300-700 EUR	88.5	11.47
< 300 EUR	76.2	23.78
> 700 EUR	92.6	7.39

    Pearson's Chi-squared test

data:  table(df$`29__Income_for_family`, df$`20__It_is_better_to_not_to_brush_against_childs_will_as_the_milk_teeth_will_fall_out_anyway_`)
X-squared = 30, df = 2, p-value = 5e-08

2.1.5 mother education

	important	not important
basic education	79.6	20.36
college	90.0	10.03
high school	88.9	11.11
university	95.2	4.85

    Pearson's Chi-squared test

data:  table(df$`31__Mothers_education_group`, df$`6__Is_it_important_to_take_care_about_deciduous_teeth_`)
X-squared = 50, df = 3, p-value = 6e-10

	Juice or other drinks with sugar	Just water or herbal tea without sugar
basic education	23.08	76.9
college	9.40	90.6
high school	15.18	84.8
university	6.73	93.3

    Pearson's Chi-squared test

data:  table(df$`31__Mothers_education_group`, df$`7__Before_going_to_sleep,_what_could_you_offer_for_your_2-year_old_child_`)
X-squared = 50, df = 3, p-value = 4e-10

	after eruption of the first milk tooth	don't know
basic education	27.1	72.9
college	36.8	63.2
high school	32.4	67.6
university	49.2	50.8

    Pearson's Chi-squared test

data:  table(df$`31__Mothers_education_group`, df$`8__In_which_age_you_should_begin_to_brush_your_childs_teeth_`)
X-squared = 50, df = 3, p-value = 5e-10

	don't know	toothbrush and toothpaste
basic education	69.2	30.8
college	73.8	26.2
high school	80.7	19.3
university	75.8	24.2

    Pearson's Chi-squared test

data:  table(df$`31__Mothers_education_group`, df$`9__What_would_you_have_to_use_for_brushing_your_childs_first_teeth_`)
X-squared = 9, df = 3, p-value = 0.04

	don' t know	toothpaste with fluorides
basic education	49.7	50.3
college	41.8	58.2
high school	45.2	54.8
university	43.1	56.9

    Pearson's Chi-squared test

data:  table(df$`31__Mothers_education_group`, df$`10__Which_toothpaste_would_you_choose_for_your_childs_first_teeth_`)
X-squared = 3, df = 3, p-value = 0.4

	do not agree	don't know or agree
basic education	18.3	81.7
college	32.7	67.3
high school	27.6	72.4
university	46.1	53.9

    Pearson's Chi-squared test

data:  table(df$`31__Mothers_education_group`, df$`12__Fluoride_toothpaste_is_bad_for_my_child`)
X-squared = 70, df = 3, p-value = 3e-14

	1000 ppm	don't know
basic education	0.617	99.4
college	0.345	99.7
high school	1.325	98.7
university	1.299	98.7

Chi-squared approximation may be incorrect

    Pearson's Chi-squared test

data:  table(df$`31__Mothers_education_group`, df$`13__How_much_fluoride_should_a_0-3-year_old_childs_toothpaste_contain_`)
X-squared = 2, df = 3, p-value = 0.5

	don't know	pea size or as rice grain
basic education	17.28	82.7
college	12.08	87.9
high school	12.17	87.8
university	7.16	92.8

    Pearson's Chi-squared test

data:  table(df$`31__Mothers_education_group`, df$`17__How_much_toothpastes_should_be_squeezed_out_on_a_toothbrush_`)
X-squared = 20, df = 3, p-value = 2e-04

	don't know	pēc vajadzības kad bērns atļauj tīrīt	twice daily
basic education	39.2	0.00	60.8
college	30.7	0.00	69.3
high school	29.3	0.00	70.7
university	27.7	0.13	72.2

Chi-squared approximation may be incorrect

    Pearson's Chi-squared test

data:  table(df$`31__Mothers_education_group`, df$`18_1_How_many_times_per_day_you_should_brush_your_childs_teeth_`)
X-squared = 10, df = 6, p-value = 0.1

	do not agree	don' t know or agree
basic education	48.2	51.8
college	58.8	41.2
high school	50.2	49.8
university	66.2	33.8

    Pearson's Chi-squared test

data:  table(df$`31__Mothers_education_group`, df$`19__Toothbrushing_against_a_childs’_will_is_aggression_towards_a_child_`)
X-squared = 30, df = 3, p-value = 2e-07

	do not agree	don' t know or agree
basic education	79.6	20.36
college	91.7	8.33
high school	87.6	12.38
university	91.0	8.96

    Pearson's Chi-squared test

data:  table(df$`31__Mothers_education_group`, df$`20__It_is_better_to_not_to_brush_against_childs_will_as_the_milk_teeth_will_fall_out_anyway_`)
X-squared = 20, df = 3, p-value = 9e-05

3 Gráficos

df %>% 
        ggplot(aes(fct_infreq(Pildita))) + 
        geom_bar() +
        coord_flip() + 
        ggtitle("Participants") + ylab("Count") + xlab("Pildeta")

NA

df %>% 
        ggplot(aes(fct_infreq(Lives_in))) + 
        geom_bar() +
        coord_flip() + 
        ggtitle("Participants") + ylab("Count") + xlab("Lives_in")

df %>% 
        ggplot(aes(`2_Is_it_your_first_child_`)) + 
        geom_bar() 

df %>% 
        ggplot(aes(`3__Nr__of_children_in_family`)) + 
        geom_bar() 

df %>% 
        ggplot(aes(`4__Oral_health_for_older_siblings_groups`)) + 
        geom_bar() 

df$income <-  factor(df$`29__Income_for_family`, c("< 300 EUR", 
                                                   "300-700 EUR", 
                                                   "> 700 EUR"))
df %>% 
        ggplot(aes(income)) + 
        geom_bar() 

df %>% 
        ggplot(aes(x = `23__Did_you_receive_recommendation_from_your_ginecologist_to_visit_a_dentist_during_pregnancy_`)) + 
        geom_bar()

df$education <- factor(df$`31__Mothers_education_group`, 
                       c("basic education", 
                         "high school", 
                         "college", 
                         "university"))
df %>% 
        ggplot(aes(x = education)) +
        geom_bar()

df %>% 
        ggplot(aes(x = AttPerc, y = KnoPerc, colour = education)) +
        geom_point()

df %>% 
        ggplot(aes(x = education, y = AttPerc)) + 
        geom_boxplot() 

df %>% 
        ggplot(aes(x = Lives_in, y = Attitude)) +
        geom_boxplot()

df %>% 
        ggplot(aes(x = Lives_in, y = KnoPerc)) +
        geom_boxplot()

4 Qué explica Attitude

fit_att <- glm(AttPerc ~
                              Age_groups +
                              Lives_in + 
                              `2_Is_it_your_first_child_` +
                              `21_Do_you_have_your_own_dentist_` + 
                              `23__Did_you_receive_recommendation_from_your_ginecologist_to_visit_a_dentist_during_pregnancy_` +
                              `24__Have_you_had_your_teeth_restored_in_latest_` +
                              `26__Do_you_use_fluoride_toothpaste_` + 
                              `27__Did_you_smoke_until_pregnancy_` +
                              `29__Income_for_family` + 
                              `31__Mothers_education_group` +
                              `32_Do_you_use_phone_with_internet_`, 
                      data = df)
summary(fit_att)

Call:
glm(formula = AttPerc ~ Age_groups + Lives_in + `2_Is_it_your_first_child_` + 
    `21_Do_you_have_your_own_dentist_` + `23__Did_you_receive_recommendation_from_your_ginecologist_to_visit_a_dentist_during_pregnancy_` + 
    `24__Have_you_had_your_teeth_restored_in_latest_` + `26__Do_you_use_fluoride_toothpaste_` + 
    `27__Did_you_smoke_until_pregnancy_` + `29__Income_for_family` + 
    `31__Mothers_education_group` + `32_Do_you_use_phone_with_internet_`, 
    data = df)

Deviance Residuals: 
   Min      1Q  Median      3Q     Max  
-72.74  -16.07    2.44   17.76   52.52  

Coefficients:
                                                                                                    Estimate Std. Error t value Pr(>|t|)
(Intercept)                                                                                           57.011      2.841   20.07  < 2e-16
Age_groups> 30 gadi                                                                                    0.147      1.399    0.11  0.91613
Lives_inPilsēta                                                                                       -0.979      1.427   -0.69  0.49291
Lives_inRīga vai Rīgas rajons                                                                          3.356      1.827    1.84  0.06643
`2_Is_it_your_first_child_`Yes, first child                                                            0.202      1.351    0.15  0.88093
`21_Do_you_have_your_own_dentist_`Yes                                                                  1.893      1.560    1.21  0.22500
`23__Did_you_receive_recommendation_from_your_ginecologist_to_visit_a_dentist_during_pregnancy_`Yes    0.246      1.295    0.19  0.84922
`24__Have_you_had_your_teeth_restored_in_latest_`Yes                                                   0.326      1.288    0.25  0.80036
`26__Do_you_use_fluoride_toothpaste_`Yes                                                               4.413      1.253    3.52  0.00045
`27__Did_you_smoke_until_pregnancy_`Yes                                                               -0.779      1.528   -0.51  0.61044
`29__Income_for_family`< 300 EUR                                                                      -8.752      2.345   -3.73  0.00020
`29__Income_for_family`> 700 EUR                                                                       4.122      1.365    3.02  0.00258
`31__Mothers_education_group`college                                                                   5.649      2.477    2.28  0.02276
`31__Mothers_education_group`high school                                                               3.020      2.406    1.25  0.20971
`31__Mothers_education_group`university                                                                9.558      2.384    4.01  6.4e-05
`32_Do_you_use_phone_with_internet_`Yes                                                                0.320      1.590    0.20  0.84077
                                                                                                       
(Intercept)                                                                                         ***
Age_groups> 30 gadi                                                                                    
Lives_inPilsēta                                                                                        
Lives_inRīga vai Rīgas rajons                                                                       .  
`2_Is_it_your_first_child_`Yes, first child                                                            
`21_Do_you_have_your_own_dentist_`Yes                                                                  
`23__Did_you_receive_recommendation_from_your_ginecologist_to_visit_a_dentist_during_pregnancy_`Yes    
`24__Have_you_had_your_teeth_restored_in_latest_`Yes                                                   
`26__Do_you_use_fluoride_toothpaste_`Yes                                                            ***
`27__Did_you_smoke_until_pregnancy_`Yes                                                                
`29__Income_for_family`< 300 EUR                                                                    ***
`29__Income_for_family`> 700 EUR                                                                    ** 
`31__Mothers_education_group`college                                                                *  
`31__Mothers_education_group`high school                                                               
`31__Mothers_education_group`university                                                             ***
`32_Do_you_use_phone_with_internet_`Yes                                                                
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for gaussian family taken to be 505)

    Null deviance: 738354  on 1334  degrees of freedom
Residual deviance: 666552  on 1319  degrees of freedom
  (325 observations deleted due to missingness)
AIC: 12117

Number of Fisher Scoring iterations: 2

4.1 Graf att

td_att <- tidy(fit_att, conf.int = TRUE)
td_att %>% 
        ggplot(aes(term, estimate)) +
        geom_point() +
        geom_pointrange(aes(ymin = conf.low, ymax = conf.high)) +
        labs(title = "Coefficients of a linear regression model")

5 Qué explica knowledge?

fit_know <- glm(KnoPerc ~
                              Age_groups +
                              Lives_in + 
                              `2_Is_it_your_first_child_` +
                              `21_Do_you_have_your_own_dentist_` + 
                              `23__Did_you_receive_recommendation_from_your_ginecologist_to_visit_a_dentist_during_pregnancy_` +
                              `24__Have_you_had_your_teeth_restored_in_latest_` +
                              `26__Do_you_use_fluoride_toothpaste_` + 
                              `27__Did_you_smoke_until_pregnancy_` +
                              `29__Income_for_family` + 
                              `31__Mothers_education_group` +
                              `32_Do_you_use_phone_with_internet_`, 
                      data = df)
summary(fit_know)

Call:
glm(formula = KnoPerc ~ Age_groups + Lives_in + `2_Is_it_your_first_child_` + 
    `21_Do_you_have_your_own_dentist_` + `23__Did_you_receive_recommendation_from_your_ginecologist_to_visit_a_dentist_during_pregnancy_` + 
    `24__Have_you_had_your_teeth_restored_in_latest_` + `26__Do_you_use_fluoride_toothpaste_` + 
    `27__Did_you_smoke_until_pregnancy_` + `29__Income_for_family` + 
    `31__Mothers_education_group` + `32_Do_you_use_phone_with_internet_`, 
    data = df)

Deviance Residuals: 
   Min      1Q  Median      3Q     Max  
 -48.0   -10.2    -0.1    10.4    42.4  

Coefficients:
                                                                                                    Estimate Std. Error t value Pr(>|t|)
(Intercept)                                                                                           41.418      1.902   21.78  < 2e-16
Age_groups> 30 gadi                                                                                   -0.457      0.937   -0.49  0.62590
Lives_inPilsēta                                                                                        1.906      0.956    1.99  0.04628
Lives_inRīga vai Rīgas rajons                                                                          2.659      1.223    2.17  0.02988
`2_Is_it_your_first_child_`Yes, first child                                                           -5.201      0.905   -5.75  1.1e-08
`21_Do_you_have_your_own_dentist_`Yes                                                                  0.332      1.044    0.32  0.75028
`23__Did_you_receive_recommendation_from_your_ginecologist_to_visit_a_dentist_during_pregnancy_`Yes    0.924      0.867    1.07  0.28650
`24__Have_you_had_your_teeth_restored_in_latest_`Yes                                                   1.053      0.863    1.22  0.22228
`26__Do_you_use_fluoride_toothpaste_`Yes                                                               3.438      0.839    4.10  4.5e-05
`27__Did_you_smoke_until_pregnancy_`Yes                                                               -0.657      1.023   -0.64  0.52102
`29__Income_for_family`< 300 EUR                                                                      -3.647      1.570   -2.32  0.02035
`29__Income_for_family`> 700 EUR                                                                       2.324      0.914    2.54  0.01112
`31__Mothers_education_group`college                                                                   3.795      1.659    2.29  0.02232
`31__Mothers_education_group`high school                                                               2.483      1.611    1.54  0.12355
`31__Mothers_education_group`university                                                                5.697      1.596    3.57  0.00037
`32_Do_you_use_phone_with_internet_`Yes                                                                0.440      1.065    0.41  0.67961
                                                                                                       
(Intercept)                                                                                         ***
Age_groups> 30 gadi                                                                                    
Lives_inPilsēta                                                                                     *  
Lives_inRīga vai Rīgas rajons                                                                       *  
`2_Is_it_your_first_child_`Yes, first child                                                         ***
`21_Do_you_have_your_own_dentist_`Yes                                                                  
`23__Did_you_receive_recommendation_from_your_ginecologist_to_visit_a_dentist_during_pregnancy_`Yes    
`24__Have_you_had_your_teeth_restored_in_latest_`Yes                                                   
`26__Do_you_use_fluoride_toothpaste_`Yes                                                            ***
`27__Did_you_smoke_until_pregnancy_`Yes                                                                
`29__Income_for_family`< 300 EUR                                                                    *  
`29__Income_for_family`> 700 EUR                                                                    *  
`31__Mothers_education_group`college                                                                *  
`31__Mothers_education_group`high school                                                               
`31__Mothers_education_group`university                                                             ***
`32_Do_you_use_phone_with_internet_`Yes                                                                
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for gaussian family taken to be 227)

    Null deviance: 330635  on 1334  degrees of freedom
Residual deviance: 298863  on 1319  degrees of freedom
  (325 observations deleted due to missingness)
AIC: 11046

Number of Fisher Scoring iterations: 2

5.1 Graf knowledge

td_know <- tidy(fit_know, conf.int = TRUE)
td_know %>% 
        ggplot(aes(term, estimate)) +
        geom_point() +
        coord_flip() +
        geom_pointrange(aes(ymin = conf.low, ymax = conf.high)) +
        labs(title = "Coefficients of a linear regression model")

5.2 BMI

summary(df_BMI$BMI)

   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
   15.4    20.6    22.6    23.4    25.4    44.2