DA
Kirsten Sallengs
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?
10001
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, there is little to no evidence.
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?
3.01
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?
36.97
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, there is strong evidence because the test statistic is a lot larger than the null.
Sex and Degree
sexdegree <- xtabs(~sex+degree,data=gss02)chisqtestGC(~sex+degree,data=gss02,
graph=TRUE)## Pearson's Chi-squared test
##
## Observed Counts:
## degree
## sex Bachelor Graduate HighSchool JunColl NotHs
## Female 241 114 856 114 208
## Male 202 116 629 88 192
##
## Counts Expected by Null:
## degree
## sex Bachelor Graduate HighSchool JunColl NotHs
## Female 246.06 127.75 824.82 112.2 222.17
## Male 196.94 102.25 660.18 89.8 177.83
##
## Contributions to the chi-square statistic:
## degree
## sex Bachelor Graduate HighSchool JunColl NotHs
## Female 0.10 1.48 1.18 0.03 0.90
## Male 0.13 1.85 1.47 0.04 1.13
##
##
## Chi-Square Statistic = 8.3131
## Degrees of Freedom of the table = 4
## P-Value = 0.0808
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?
We have some evidence to support this relationship.
What’s the test statistic?
8.312
About how big should it be if the Null is correct?
4
What’s the P-value?
0.0808
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?
The simulated p-value is 0.1483, the unsimulated p-value is 0.1546