If we're using the built-in function in R to run a Chi-Squared test, we won't know if data passes the expected counts assumption until after we run the test.

Group of answer choices

True

False

We want to show that Sex and Smoking habits are dependent. What are our hypotheses?

Group of answer choices

Ho: _sex= _smk; Ha: _{sex smk}

Ho: Sex and Smoking habits are dependent; Ha: Sex and Smoking habits are independent

Ho: ²= 0; Ha: ² 0

Ho: Sex and Smoking habits are independent; Ha: Sex and Smoking habits are dependent

What's the easiest way to show that data are frequencies, placed into mutually exclusive cells?

Group of answer choices

QQ plots

Contingency tables

Histograms

Pie charts

When we test to see if Sex and Smoking habits are dependent, the expected counts pass the assumption. Use R calculations below:

Group of answer choices

True

False

R calculations

tbl

Smk

Sex12

1 13 63

2 11 77

> test<-chisq.test(tbl,correct=FALSE)

> test

Pearson's Chi-squared test

data:tbl

X-squared = 0.69232, df = 1, p-value = 0.4054

> test$expected

Smk

Sex12

1 11.12195 64.87805

2 12.87805 75.12195

Test to see if Sex and whether or not subjects partake in Aerobics are dependent. If we set anof 0.10, what would your conclusion be?Use R calculations below:

Group of answer choices

Reject the Null

Fail to Reject the null

R calculations

tbl<-xtabs(~Sex+Aer)

>

> tbl

Aer

Sex12

1 25 51

2 31 57

> test<-chisq.test(tbl,correct=FALSE)

> test

Pearson's Chi-squared test

data:tbl

X-squared = 0.09867, df = 1, p-value = 0.7534

> test$expected

Aer

Sex12

1 25.95122 50.04878

2 30.04878 57.95122

The dependence test between Sex and Caffeine habits passes the expected count assumption.Use R calculations below:

Group of answer choices

True

False

tbl<-xtabs(~Sex+Caf)> tbl

Caf

Sex12

1 22 54

2 31 57

> test<-chisq.test(tbl,correct=FALSE)

> test

Pearson's Chi-squared test

data:tbl

X-squared = 0.73527, df = 1, p-value = 0.3912

test$expected

Caf

Sex12

1 24.56098 51.43902

2 28.43902 59.56098