Assignment 4
Ali
November 30, 2015
ASSIGNMENT 4, WFED 540
## [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. :45.00## Warning in library(package, lib.loc = lib.loc, character.only = TRUE,
## logical.return = TRUE, : there is no package called 'corrlot'1. Select four continuous variables from Mroz.
Mroz <- tbl_df(Mroz)
Mroz## Source: local data frame [753 x 18]
##
## work hoursw child6 child618 agew educw hearnw wagew hoursh ageh
## (fctr) (int) (int) (int) (int) (int) (dbl) (dbl) (int) (int)
## 1 no 1610 1 0 32 12 3.3540 2.65 2708 34
## 2 no 1656 0 2 30 12 1.3889 2.65 2310 30
## 3 no 1980 1 3 35 12 4.5455 4.04 3072 40
## 4 no 456 0 3 34 12 1.0965 3.25 1920 53
## 5 no 1568 1 2 31 14 4.5918 3.60 2000 32
## 6 no 2032 0 0 54 12 4.7421 4.70 1040 57
## 7 no 1440 0 2 37 16 8.3333 5.95 2670 37
## 8 no 1020 0 0 54 12 7.8431 9.98 4120 53
## 9 no 1458 0 2 48 12 2.1262 0.00 1995 52
## 10 no 1600 0 2 39 12 4.6875 4.15 2100 43
## .. ... ... ... ... ... ... ... ... ... ...
## Variables not shown: educh (int), wageh (dbl), income (int), educwm (int),
## educwf (int), unemprate (dbl), city (fctr), experience (int)roz <- Mroz %>%
select(agew, income, hoursw, experience)
roz## Source: local data frame [753 x 4]
##
## agew income hoursw experience
## (int) (int) (int) (int)
## 1 32 16310 1610 14
## 2 30 21800 1656 5
## 3 35 21040 1980 15
## 4 34 7300 456 6
## 5 31 27300 1568 7
## 6 54 19495 2032 33
## 7 37 21152 1440 11
## 8 54 18900 1020 35
## 9 48 20405 1458 24
## 10 39 20425 1600 21
## .. ... ... ... ...2. Estimate Pearson Product-Moment Correlations for four pairs of variables.
## $r
## hoursw experience agew income
## hoursw 1
## experience 0.4 1
## agew -0.033 0.33 1
## income 0.15 -0.028 0.052 1
##
## $p
## hoursw experience agew income
## hoursw 0
## experience 0 0
## agew 0.36 0 0
## income 5.6e-05 0.45 0.15 0
##
## $sym
## hoursw experience agew income
## hoursw 1
## experience . 1
## agew . 1
## income 1
## attr(,"legend")
## [1] 0 ' ' 0.3 '.' 0.6 ',' 0.8 '+' 0.9 '*' 0.95 'B' 13. Test null hypotheses that the population correlations = 0 for the four pairs of variables you selected.
Alpha = .05
The p-value from the p-value table show that all the values are less than alpha except that for the hoursw and agew which means there is a coorelation only between these two variables (hoursw and income)
so, we reject the null hypthesise that there is no correlation except between these variable hoursw and income.
4. Using ggvis, plot scatterplots containing points and a smooth line for the four pairs of variable you selected.
require (ggvis)## Loading required package: ggvisrequire (magrittr)## Loading required package: magrittrroz %>%
ggvis (x=~hoursw, y= ~experience) %>%
layer_points() %>%
layer_smooths()roz %>%
ggvis (x=~hoursw, y= ~agew) %>%
layer_points() %>%
layer_smooths()roz %>%
ggvis (x=~hoursw, y= ~income) %>%
layer_points() %>%
layer_smooths()roz %>%
ggvis (x=~experience, y= ~income) %>%
layer_points() %>%
layer_smooths()5. Produce correlograms and heat maps for the four pairs of variables you selected.
require(corrgram)## Loading required package: corrgramcor.roz <- cor(roz)
corrgram (cor.roz, upper.panel= NULL)
cormat<-rquery.cormat(roz, graphType="heatmap")