Getting Started

Load packages

In this lab, we will explore and visualize the data using the tidyverse suite of packages, and perform statistical inference using infer. The data can be found in the companion package for OpenIntro resources, openintro.

Let’s load the packages.

library(tidyverse)
library(openintro)
library(infer)

The data

You will be analyzing the same dataset as in the previous lab, where you delved into a sample from the Youth Risk Behavior Surveillance System (YRBSS) survey, which uses data from high schoolers to help discover health patterns. The dataset is called yrbss.

  1. What are the counts within each category for the amount of days these students have texted while driving within the past 30 days?
view(yrbss)
counts_each<- yrbss %>% 
  count(text_while_driving_30d)
counts_each
## # A tibble: 9 × 2
##   text_while_driving_30d     n
##   <chr>                  <int>
## 1 0                       4792
## 2 1-2                      925
## 3 10-19                    373
## 4 20-29                    298
## 5 3-5                      493
## 6 30                       827
## 7 6-9                      311
## 8 did not drive           4646
## 9 <NA>                     918

4792 youth have reported 0 days. 925 youth have reported one to two days. 373 youth have reported 10-19 days. 298 youth have reported 20-29 days. 493 youth have reported 3-5 days. 827 youth have reported 30 days. 311 youth have reported 6-9 days. 4646 youth did not drive, and 918 reported NA.

  1. What is the proportion of people who have texted while driving every day in the past 30 days and never wear helmets?

Also, it may be easier to calculate the proportion if you create a new variable that specifies whether the individual has texted every day while driving over the past 30 days or not. We will call this variable text_ind_everyday.

no_helmet <- yrbss %>%
  filter(helmet_12m == "never")%>%
  filter(!is.na(text_while_driving_30d))%>%
  mutate(text_ind_everyday_everyday = ifelse(text_while_driving_30d == "30", "yes", "no"))

no_helmet%>%
  count(text_ind_everyday_everyday)
## # A tibble: 2 × 2
##   text_ind_everyday_everyday     n
##   <chr>                      <int>
## 1 no                          6040
## 2 yes                          463

The proportion of people who have texted while driving every day in the past 30 days and never wear helmets The proportion of people who have texted while driving every day in the past 30 days and never wear helmets is 463/6040 or 7.67%.

Inference on proportions

When summarizing the YRBSS, the Centers for Disease Control and Prevention seeks insight into the population parameters. To do this, you can answer the question, “What proportion of people in your sample reported that they have texted while driving each day for the past 30 days?” with a statistic; while the question “What proportion of people on earth have texted while driving each day for the past 30 days?” is answered with an estimate of the parameter.

The inferential tools for estimating population proportion are analogous to those used for means in the last chapter: the confidence interval and the hypothesis test.

no_helmet %>%
  drop_na(text_ind_everyday_everyday) %>%
  specify(response = text_ind_everyday_everyday, success = "yes") %>%
  generate(reps = 1000, type = "bootstrap") %>%
  calculate(stat = "prop") %>%
  get_ci(level = 0.95)
## # A tibble: 1 × 2
##   lower_ci upper_ci
##      <dbl>    <dbl>
## 1   0.0649   0.0773
width_tv<-0.7690949-0.7483444
print(width_tv)
## [1] 0.0207505
margin_of_error_tv <- width_tv / 2 
round(margin_of_error_tv,2)
## [1] 0.01

Note that since the goal is to construct an interval estimate for a proportion, it’s necessary to both include the success argument within specify, which accounts for the proportion of non-helmet wearers than have consistently texted while driving the past 30 days, in this example, and that stat within calculate is here “prop”, signaling that you are trying to do some sort of inference on a proportion.

The 95% Confidence Interval for the proportion of students who text and drive everyday of all students who do not wear a helmet is between 6.5% and 7.7%.

  1. What is the margin of error for the estimate of the proportion of non-helmet wearers that have texted while driving each day for the past 30 days based on this survey?
lower_ci <- 0.0652
upper_ci <- 0.0775
moe <- (upper_ci - lower_ci) / 2
moe
## [1] 0.00615

The margin of error is about 0.62%

  1. Using the infer package, calculate confidence intervals for two other categorical variables (you’ll need to decide which level to call “success”, and report the associated margins of error. Interpet the interval in context of the data. It may be helpful to create new data sets for each of the two countries first, and then use these data sets to construct the confidence intervals.
no_helmet <- no_helmet %>%
  mutate(tv_ind = ifelse(hours_tv_per_school_day <= 5, "yes", "no"))
no_helmet %>%
  drop_na(tv_ind) %>%
  specify(response = tv_ind, success = "yes") %>%
  generate(reps = 1000, type = "bootstrap") %>%
  calculate(stat = "prop") %>%
  get_ci(level = 0.95)
## # A tibble: 1 × 2
##   lower_ci upper_ci
##      <dbl>    <dbl>
## 1    0.749    0.770

How does the proportion affect the margin of error?

Imagine you’ve set out to survey 1000 people on two questions: are you at least 6-feet tall? and are you left-handed? Since both of these sample proportions were calculated from the same sample size, they should have the same margin of error, right? Wrong! While the margin of error does change with sample size, it is also affected by the proportion.

Think back to the formula for the standard error: \(SE = \sqrt{p(1-p)/n}\). This is then used in the formula for the margin of error for a 95% confidence interval:

\[ ME = 1.96\times SE = 1.96\times\sqrt{p(1-p)/n} \,. \] Since the population proportion \(p\) is in this \(ME\) formula, it should make sense that the margin of error is in some way dependent on the population proportion. We can visualize this relationship by creating a plot of \(ME\) vs. \(p\).

Since sample size is irrelevant to this discussion, let’s just set it to some value (\(n = 1000\)) and use this value in the following calculations:

n <- 1000

The first step is to make a variable p that is a sequence from 0 to 1 with each number incremented by 0.01. You can then create a variable of the margin of error (me) associated with each of these values of p using the familiar approximate formula (\(ME = 2 \times SE\)).

p <- seq(from = 0, to = 1, by = 0.01)
me <- 2 * sqrt(p * (1 - p)/n)

Lastly, you can plot the two variables against each other to reveal their relationship. To do so, we need to first put these variables in a data frame that you can call in the ggplot function.

dd <- data.frame(p = p, me = me)
ggplot(data = dd, aes(x = p, y = me)) + 
  geom_line() +
  labs(x = "Population Proportion", y = "Margin of Error")

  1. Describe the relationship between p and me. Include the margin of error vs. population proportion plot you constructed in your answer. For a given sample size, for which value of p is margin of error maximized?

The margin of error increases as the population proportion approaches .50 from both the left and the right. The margin of error is maximized when the population proportion is .5.

Success-failure condition

We have emphasized that you must always check conditions before making inference. For inference on proportions, the sample proportion can be assumed to be nearly normal if it is based upon a random sample of independent observations and if both \(np \geq 10\) and \(n(1 - p) \geq 10\). This rule of thumb is easy enough to follow, but it makes you wonder: what’s so special about the number 10?

The short answer is: nothing. You could argue that you would be fine with 9 or that you really should be using 11. What is the “best” value for such a rule of thumb is, at least to some degree, arbitrary. However, when \(np\) and \(n(1-p)\) reaches 10 the sampling distribution is sufficiently normal to use confidence intervals and hypothesis tests that are based on that approximation.

You can investigate the interplay between \(n\) and \(p\) and the shape of the sampling distribution by using simulations. Play around with the following app to investigate how the shape, center, and spread of the distribution of \(\hat{p}\) changes as \(n\) and \(p\) changes.

  1. Describe the sampling distribution of sample proportions at \(n = 300\) and \(p = 0.1\). Be sure to note the center, spread, and shape.

The center is .1 and the spread is .017. The shape is mostly normal.

  1. Keep \(n\) constant and change \(p\). How does the shape, center, and spread of the sampling distribution vary as \(p\) changes. You might want to adjust min and max for the \(x\)-axis for a better view of the distribution.

The center is .5 and the spread is .029. The shape is mostly normal.

  1. Now also change \(n\). How does \(n\) appear to affect the distribution of \(\hat{p}\)?

The center is .5 the spread is .05 and the shape is also mostly normal.

More Practice

For some of the exercises below, you will conduct inference comparing two proportions. In such cases, you have a response variable that is categorical, and an explanatory variable that is also categorical, and you are comparing the proportions of success of the response variable across the levels of the explanatory variable. This means that when using infer, you need to include both variables within specify.

  1. Is there convincing evidence that those who sleep 10+ hours per day are more likely to strength train every day of the week? As always, write out the hypotheses for any tests you conduct and outline the status of the conditions for inference. If you find a significant difference, also quantify this difference with a confidence interval.
sleep_students <- yrbss %>%
  filter(school_night_hours_sleep == "10+") 
sleep_students %>%
  mutate(exercises_7d = ifelse(physically_active_7d == 7, "yes", "no")) %>%
  drop_na(exercises_7d) %>%
  specify(response = exercises_7d, success = "yes") %>%
  generate(reps = 1000, type = "bootstrap") %>%
  calculate(stat = "prop") %>%
  get_ci(level = 0.95)
## # A tibble: 1 × 2
##   lower_ci upper_ci
##      <dbl>    <dbl>
## 1    0.316    0.422
ci_width_sleep<-0.4217252-0.3162939
print(ci_width_sleep)
## [1] 0.1054313
margin_of_error_sleep2 <- ci_width_sleep / 2 
round(margin_of_error_sleep2,2)
## [1] 0.05

With a 95% confidence interval between about .32-.42, we can say that between 32-42% of youth reported that they sleep at least 10 hours and make it to the gym everyday. This is enough for us to accept the alternative hypothesis, and reject the null hypothesis.

  1. Let’s say there has been no difference in likeliness to strength train every day of the week for those who sleep 10+ hours. What is the probability that you could detect a change (at a significance level of 0.05) simply by chance? Hint: Review the definition of the Type 1 error.

There is a 5% probability of detecting a change by chance.

  1. Suppose you’re hired by the local government to estimate the proportion of residents that attend a religious service on a weekly basis. According to the guidelines, the estimate must have a margin of error no greater than 1% with 95% confidence. You have no idea what to expect for \(p\). How many people would you have to sample to ensure that you are within the guidelines? Hint: Refer to your plot of the relationship between \(p\) and margin of error. This question does not require using a dataset.

A sample of 1000 people