DA
Your Full Name Here
07/23/2015
App on Sex and Love at First Sight
Do the Shiny app for the two-way table on sex and love_first.
How many times did you re-sample? 50000
What percentage of the time did the re sampled chi-square statistic exceed the chi-square statistic in the actual study? 9.87%
Do you think there is overwhelming evidence that in the GC population the two sexes differ in whether they believe in love at first sight?
No, because there is not alot of evidence and the p-value is not closer to one or zero
chisqtestGC() on the sex and love at first sight Question
Here’s the code for a chi-square test to see if sex and belief in love at first sight are related in the GC population. Run the code:
chisqtestGC(~sex+love_first,data=m111survey,
graph=TRUE)## Pearson's Chi-squared test with Yates' continuity correction
##
## Observed Counts:
## love_first
## sex no yes
## female 22 18
## male 23 8
##
## Counts Expected by Null:
## love_first
## sex no yes
## female 25.35 14.65
## male 19.65 11.35
##
## Contributions to the chi-square statistic:
## love_first
## sex no yes
## female 0.44 0.77
## male 0.57 0.99
##
##
## Chi-Square Statistic = 2.0068
## Degrees of Freedom of the table = 1
## P-Value = 0.1566
Now look at the output and answer these question:
What’s the test statistic? 2.0068
About how big should it be if the Null is correct? 1
What’s the P-value? 0.1566
Race and Gun Owndership
Are race and gun owndership related in the U.S. population? In the code chunk below, insert the code needed to use chisqtestGC() to investigate this question. Tip: copy-paste and then modify the code from the previous problem.
chisqtestGC(~race+owngun,data=gss02,
graph=TRUE)## Pearson's Chi-squared test
##
## Observed Counts:
## owngun
## race No Yes
## AfrAm 106 16
## Hispanic 20 3
## Other 25 7
## White 454 284
##
## Counts Expected by Null:
## owngun
## race No Yes
## AfrAm 80.67 41.33
## Hispanic 15.21 7.79
## Other 21.16 10.84
## White 487.97 250.03
##
## Contributions to the chi-square statistic:
## owngun
## race No Yes
## AfrAm 7.96 15.53
## Hispanic 1.51 2.95
## Other 0.70 1.36
## White 2.36 4.61
##
##
## Chi-Square Statistic = 36.9779
## Degrees of Freedom of the table = 3
## P-Value = 0
Looking at the output, answer the following questions.
What’s the test statistic? 36.9779
About how big should it be if the Null is correct? 3
What’s the P-value? 0
Do you think we have strong evidence for a relationship in the population, or could the pattern in the data be due just to chance?
Yes, because the p-value is less than 0.05 so there for can reject the null hypothesis and say there is a relationship between race and gun ownership in the United States.
chisqtestGC(~degree+cappun,data=gss02,
graph=TRUE)## Pearson's Chi-squared test
##
## Observed Counts:
## cappun
## degree Favor Oppose
## Bachelor 135 71
## Graduate 64 50
## HighSchool 511 200
## JunColl 71 16
## NotHs 117 72
##
## Counts Expected by Null:
## cappun
## degree Favor Oppose
## Bachelor 141.54 64.46
## Graduate 78.33 35.67
## HighSchool 488.51 222.49
## JunColl 59.78 27.22
## NotHs 129.86 59.14
##
## Contributions to the chi-square statistic:
## cappun
## degree Favor Oppose
## Bachelor 0.30 0.66
## Graduate 2.62 5.75
## HighSchool 1.04 2.27
## JunColl 2.11 4.63
## NotHs 1.27 2.79
##
##
## Chi-Square Statistic = 23.4509
## Degrees of Freedom of the table = 4
## P-Value = 1e-04
Simulated Sex and Seat
Try simulation on the sex and seating-preference study:
chisqtestGC(~sex+seat,data=m111survey,
simulate.p.value="random",
B=3000)Now try it again, without simulation:
chisqtestGC(~sex+seat,data=m111survey)Compare the P-values: are they about the same, or very different? yes
Sex and Wages
Let’s learn about a new data frame (its form the mosaicData package):
View(CPS85)
help(CPS85)Say that we wnat to know: Who makes more money, on average: a male or a female?
In the code chunk below, write some code that with favstats that will help you answer this question.
favstats(wage~sex, data =CPS85)Complete the chunk below to get a density plot to answer the same question, graphically:
densityplot(~wage|sex,data=CPS85,
main = "wage, by sex",
xlab = "wage by hour")
Who seems to make more? Male
Before we conclude that there is wage discrimination on the basis of sex we should think about possible confounding factors.
Maybe work-setor is a confounding factor. If men and women differ in what ype of work they choose, and men tend to choose higher-paying types of work, then maybe that’s why their wages are higher?
In the code chunk below, produce some numerical output to help see whether men and women choose different types of work.
In the code chunk below, produce some graphical output to help see whether men and women choose different ypes of work.
In the code-chunk below, run a chi-square test to see if the relationship you see in the data provides storng evidence that sex and sector are related in the U.S. population.
In the code chunk below, produce some numerical output to help see whether wages vary by work sector.
In the code chunk below, produce some graphical output to help see whether wages vary by work sector.
Run the code below: what does it tell you?
densityplot(~wage|sector*sex, data =CPS85)
The following code gives the mean salary for each sex in each work sector. What does it tell you?
with(CPS85, tapply(wage, INDEX = list(sex,sector), FUN = mean))## clerical const manag manuf other prof sales service
## F 7.404211 NA 11.05619 5.713750 5.801667 11.10500 5.241765 6.059388
## M 7.489048 9.502 13.72176 9.302727 8.761774 12.77396 9.495714 7.226471Does it appear that the overall difference between men and women can be explained by the fact that they choose different sectors of work? Why or why not?