Assignment 4
Meg Handley
November 19, 2015
I loaded and required the necessary packages and data to complete the assignment.
## [1] "work" "hoursw" "child6" "child618" "agew"
## [6] "educw" "hearnw" "wagew" "hoursh" "ageh"
## [11] "educh" "wageh" "income" "educwm" "educwf"
## [16] "unemprate" "city" "experience"## work hoursw child6 child618
## yes:325 Min. : 0.0 Min. :0.0000 Min. :0.000
## no :428 1st Qu.: 0.0 1st Qu.:0.0000 1st Qu.:0.000
## Median : 288.0 Median :0.0000 Median :1.000
## Mean : 740.6 Mean :0.2377 Mean :1.353
## 3rd Qu.:1516.0 3rd Qu.:0.0000 3rd Qu.:2.000
## Max. :4950.0 Max. :3.0000 Max. :8.000
## agew educw hearnw wagew
## Min. :30.00 Min. : 5.00 Min. : 0.000 Min. :0.00
## 1st Qu.:36.00 1st Qu.:12.00 1st Qu.: 0.000 1st Qu.:0.00
## Median :43.00 Median :12.00 Median : 1.625 Median :0.00
## Mean :42.54 Mean :12.29 Mean : 2.375 Mean :1.85
## 3rd Qu.:49.00 3rd Qu.:13.00 3rd Qu.: 3.788 3rd Qu.:3.58
## Max. :60.00 Max. :17.00 Max. :25.000 Max. :9.98
## hoursh ageh educh wageh
## Min. : 175 Min. :30.00 Min. : 3.00 Min. : 0.4121
## 1st Qu.:1928 1st Qu.:38.00 1st Qu.:11.00 1st Qu.: 4.7883
## Median :2164 Median :46.00 Median :12.00 Median : 6.9758
## Mean :2267 Mean :45.12 Mean :12.49 Mean : 7.4822
## 3rd Qu.:2553 3rd Qu.:52.00 3rd Qu.:15.00 3rd Qu.: 9.1667
## Max. :5010 Max. :60.00 Max. :17.00 Max. :40.5090
## income educwm educwf unemprate
## Min. : 1500 Min. : 0.000 Min. : 0.000 Min. : 3.000
## 1st Qu.:15428 1st Qu.: 7.000 1st Qu.: 7.000 1st Qu.: 7.500
## Median :20880 Median :10.000 Median : 7.000 Median : 7.500
## Mean :23081 Mean : 9.251 Mean : 8.809 Mean : 8.624
## 3rd Qu.:28200 3rd Qu.:12.000 3rd Qu.:12.000 3rd Qu.:11.000
## Max. :96000 Max. :17.000 Max. :17.000 Max. :14.000
## city experience
## no :269 Min. : 0.00
## yes:484 1st Qu.: 4.00
## Median : 9.00
## Mean :10.63
## 3rd Qu.:15.00
## Max. :45.00Select Four Continuous Variables
I created a table dataframe for dataset.
Mroz <- tbl_df(Mroz)
Mroz## Source: local data frame [753 x 18]
##
## work hoursw child6 child618 agew educw hearnw wagew hoursh ageh educh
## 1 no 1610 1 0 32 12 3.3540 2.65 2708 34 12
## 2 no 1656 0 2 30 12 1.3889 2.65 2310 30 9
## 3 no 1980 1 3 35 12 4.5455 4.04 3072 40 12
## 4 no 456 0 3 34 12 1.0965 3.25 1920 53 10
## 5 no 1568 1 2 31 14 4.5918 3.60 2000 32 12
## 6 no 2032 0 0 54 12 4.7421 4.70 1040 57 11
## 7 no 1440 0 2 37 16 8.3333 5.95 2670 37 12
## 8 no 1020 0 0 54 12 7.8431 9.98 4120 53 8
## 9 no 1458 0 2 48 12 2.1262 0.00 1995 52 4
## 10 no 1600 0 2 39 12 4.6875 4.15 2100 43 12
## .. ... ... ... ... ... ... ... ... ... ... ...
## Variables not shown: wageh (dbl), income (int), educwm (int), educwf
## (int), unemprate (dbl), city (fctr), experience (int)I selected four variables and used the select command to only show the four variables I selected. Then I ran a summary of Mroz1.
Mroz1 <- Mroz %>%
select(hoursw, hoursh, income, ageh)
Mroz1## Source: local data frame [753 x 4]
##
## hoursw hoursh income ageh
## 1 1610 2708 16310 34
## 2 1656 2310 21800 30
## 3 1980 3072 21040 40
## 4 456 1920 7300 53
## 5 1568 2000 27300 32
## 6 2032 1040 19495 57
## 7 1440 2670 21152 37
## 8 1020 4120 18900 53
## 9 1458 1995 20405 52
## 10 1600 2100 20425 43
## .. ... ... ... ...summary(Mroz1)## hoursw hoursh income ageh
## Min. : 0.0 Min. : 175 Min. : 1500 Min. :30.00
## 1st Qu.: 0.0 1st Qu.:1928 1st Qu.:15428 1st Qu.:38.00
## Median : 288.0 Median :2164 Median :20880 Median :46.00
## Mean : 740.6 Mean :2267 Mean :23081 Mean :45.12
## 3rd Qu.:1516.0 3rd Qu.:2553 3rd Qu.:28200 3rd Qu.:52.00
## Max. :4950.0 Max. :5010 Max. :96000 Max. :60.00Estimate Pearson Product-Moment Correlations for four pairs of variables.
I ran a Pearson Correlation for four pairs of the variables.
rquery.cormat(Mroz1)
## $r
## hoursh ageh hoursw income
## hoursh 1
## ageh -0.095 1
## hoursw -0.056 -0.031 1
## income 0.13 0.041 0.15 1
##
## $p
## hoursh ageh hoursw income
## hoursh 0
## ageh 0.0088 0
## hoursw 0.12 0.39 0
## income 0.00042 0.27 5.6e-05 0
##
## $sym
## hoursh ageh hoursw income
## hoursh 1
## ageh 1
## hoursw 1
## income 1
## attr(,"legend")
## [1] 0 ' ' 0.3 '.' 0.6 ',' 0.8 '+' 0.9 '*' 0.95 'B' 1Test null hypotheses that the population correlations = 0 for the four pairs of variables you selected.
Hypothesis (null) The population correlations equals zero for hoursh and ageh
Hypothesis (alternative) The population correlations do not equal zero for hoursh and ageh
A Pearsons Correlation will be used to test the Hypothesis. The null hypothesis is rejected at the specified .05 level, r=-.095, p <.05
Hypothesis (null) The population correlations equals zero for hoursh and income
Hypothesis (alternative) The population correlations do not equal zero for hoursh and income
A Pearsons Correlation will be used to test the Hypothesis. The null hypothesis is rejected at the specified .05 level, r=.13, p <.05
Hypothesis (null) The population correlations equals zero for ageh and income
Hypothesis (alternative) The population correlations do not equal zero for ageh and income
A Pearsons Correlation will be used to test the Hypothesis. We fail to reject the null hypothesis at the specified .05 level, r=.041, p >.05
Hypothesis (null) The population correlations equals zero for hoursw and income
Hypothesis (alternative) The population correlations do not equal zero for hoursw and income
A Pearsons Correlation will be used to test the Hypothesis. The null hypothesis is rejected at the specified .05 level, r=.15, p <.05
Using ggvis, plot scatterplots containing points and a smooth line for the four pairs of variable you selected.
Scatterplot for hoursh and ageh
Mroz1 %>%
ggvis(x = ~hoursh, y = ~ageh) %>% layer_points() %>% layer_smooths() %>% add_axis("x", title = "hoursh", title_offset = 50) %>%
add_axis("y", title = "ageh", title_offset = 50)Scatterplot for hoursh and income
Mroz1 %>%
ggvis(x = ~hoursh, y = ~income) %>% layer_points() %>% layer_smooths() %>% add_axis("x", title = "hoursh", title_offset = 50) %>%
add_axis("y", title = "income", title_offset = 50)Scatterplot for ageh and income
Mroz1 %>%
ggvis(x = ~ageh, y = ~income) %>% layer_points() %>% layer_smooths() %>% add_axis("x", title = "hoursh", title_offset = 50) %>%
add_axis("y", title = "income", title_offset = 50)Scatterplost for hoursw and income
Mroz1 %>%
ggvis(x = ~hoursw, y = ~income) %>% layer_points() %>% layer_smooths() %>% add_axis("x", title = "hoursh", title_offset = 50) %>%
add_axis("y", title = "income", title_offset = 50)Heat Map
The following code creates a heat map of these correlations: (see correlogram above)
cormat<-rquery.cormat(Mroz1, graphType="heatmap") 