install.packages(‘openintro’) library(openintro)

Heights of adults. (7.7, p. 260) Researchers studying anthropometry collected body girth measurements and skeletal diameter measurements, as well as age, weight, height and gender, for 507 physically active individuals. The histogram below shows the sample distribution of heights in centimeters.

  1. What is the point estimate for the average height of active individuals? What about the median?
heights <- bdims$hgt
paste("Point estimate for average height of active individuals is ",round(mean(heights),digits=1))
## [1] "Point estimate for average height of active individuals is  171.1"
  1. What is the point estimate for the standard deviation of the heights of active individuals? What about the IQR?
paste("SD is",round(sd(heights),digits=1))
## [1] "SD is 9.4"
paste("IQR will be",round(IQR(heights),digits=1))
## [1] "IQR will be 14"

  1. Is a person who is 1m 80cm (180 cm) tall considered unusually tall? And is a person who is 1m 55cm (155cm) considered unusually short? Explain your reasoning. 180 cm is not very tall. 180cm is 1 PE SD above mean. Also 155cm is 1.5 PE SD below mean. I believe it is unusual for person to be this short.

  2. The researchers take another random sample of physically active individuals. Would you expect the mean and the standard deviation of this new sample to be the ones given above? Explain your reasoning. I think if the random sampling strategy is similar in both case then there is chance we get similar mean and SD, or else the result can be different.

  3. The sample means obtained are point estimates for the mean height of all active individuals, if the sample of individuals is equivalent to a simple random sample. What measure do we use to quantify the variability of such an estimate (Hint: recall that \(SD_x = \frac{\sigma}{\sqrt{n}}\))? Compute this quantity using the data from the original sample under the condition that the data are a simple random sample.

##Answer will be 
9.4/sqrt(507)
## [1] 0.4174687

Thanksgiving spending, Part I. The 2009 holiday retail season, which kicked off on November 27, 2009 (the day after Thanksgiving), had been marked by somewhat lower self-reported consumer spending than was seen during the comparable period in 2008. To get an estimate of consumer spending, 436 randomly sampled American adults were surveyed. Daily consumer spending for the six-day period after Thanksgiving, spanning the Black Friday weekend and Cyber Monday, averaged $84.71. A 95% confidence interval based on this sample is ($80.31, $89.11). Determine whether the following statements are true or false, and explain your reasoning.

library(ggplot2)

ggplot(thanksgiving_spend, aes(x = spending)) +
  geom_histogram(binwidth = 20)

  1. We are 95% confident that the average spending of these 436 American adults is between $80.31 and $89.11.

This is false i believe, this is because confidence interval is for the whole population and not just for the 436 random samples

  1. This confidence interval is not valid since the distribution of spending in the sample is right skewed. False - data is right skewed but not stronlgy skewed.

  2. 95% of random samples have a sample mean between $80.31 and $89.11. I believe this to be false. This is because there is no correlation between confidence interval and sample mean

  3. We are 95% confident that the average spending of all American adults is between $80.31 and $89.11. This is true. This because it is based on the confidence interval

  4. A 90% confidence interval would be narrower than the 95% confidence interval since we don’t need to be as sure about our estimate. This is true. I believe the range gets narrower as the confidence interval becomes smaller.

  5. In order to decrease the margin of error of a 95% confidence interval to a third of what it is now, we would need to use a sample 3 times larger. This is false. I believe to decrease error margin by three times we would need to take sample which is 9 times larger

  6. The margin of error is 4.4.

marginoferror <- (89.11-80.31) / 2
marginoferror
## [1] 4.4

Gifted children, Part I. Researchers investigating characteristics of gifted children col- lected data from schools in a large city on a random sample of thirty-six children who were identified as gifted children soon after they reached the age of four. The following histogram shows the dis- tribution of the ages (in months) at which these children first counted to 10 successfully. Also provided are some sample statistics.

  1. Are conditions for inference satisfied? I believe conditions of inference are in this case satisfied. Also the children are selected and the sampling size is > 30. Also the distribution is not fully skewed.

  2. Suppose you read online that children first count to 10 successfully when they are 32 months old, on average. Perform a hypothesis test to evaluate if these data provide convincing evidence that the average age at which gifted children fist count to 10 successfully is less than the general average of 32 months. Use a significance level of 0.10. ##Had to use DATA606::normalPlot in order for the plot to function

library(qqplotr)
## 
## Attaching package: 'qqplotr'
## The following objects are masked from 'package:ggplot2':
## 
##     stat_qq_line, StatQqLine
n = 36
mn = 30.69
sd = 4.31
se <- sd / sqrt(n)
z = (mn - 32) / se

p=pnorm(z)
DATA606::normalPlot(bounds = c(-Inf, z))

  1. Interpret the p-value in context of the hypothesis test and the data.
p
## [1] 0.0341013

There is significant evidence to infer that the gifted children can count to 10 at an earlier age.

  1. Calculate a 90% confidence interval for the average age at which gifted children first count to 10 successfully.
z90= 1.645
lower = mn-z90 *se
upper = mn+z90 *se
c(lower,upper)
## [1] 29.50834 31.87166
  1. Do your results from the hypothesis test and the confidence interval agree? Explain.

In this case yes. This is because the CI of 90% is under 32 months, this was our hypothesis from the beginning

Gifted children, Part II. Exercise above describes a study on gifted children. In this study, along with variables on the children, the researchers also collected data on the mother’s and father’s IQ of the 36 randomly sampled gifted children. The histogram below shows the distribution of mother’s IQ. Also provided are some sample statistics.

  1. Performahypothesistesttoevaluateifthesedataprovideconvincingevidencethattheaverage IQ of mothers of gifted children is different than the average IQ for the population at large, which is 100. Use a significance level of 0.10.
mn = 118.2
n=36
sd=6.5
se=sd/sqrt(n)

z = (mn-100)/se
pnorm(z)
## [1] 1
  1. Calculate a 90% confidence interval for the average IQ of mothers of gifted children.
lower = mn-z90 *se
upper = mn+z90 *se
c(lower,upper)
## [1] 116.4179 119.9821
  1. Do your results from the hypothesis test and the confidence interval agree? Explain. In this case the results from the hypothesis test and the confidence interval appear to agree. We are 90% confident that the average IQ of mothers of gifted children is between 116.4 and 119.9. This is significantly above population average of 100

CLT. Define the term “sampling distribution” of the mean, and describe how the shape, center, and spread of the sampling distribution of the mean change as sample size increases.

I think a sampling distribution shows the distribution of n samples from a population. Similarly, as the sample size n increases, the shape of the sample distribution approaches the normal distribution. As sample size increases, we can see the shape approaches normal distribution, also the center is more pronounced and the spread becomes lean with more samples near the mean.

CFLBs. A manufacturer of compact fluorescent light bulbs advertises that the distribution of the lifespans of these light bulbs is nearly normal with a mean of 9,000 hours and a standard deviation of 1,000 hours.

  1. What is the probability that a randomly chosen light bulb lasts more than 10,500 hours?
paste("the probability that a randomly chosen light bulb lasts more than 10,500 hours", round(1-pnorm(q=10500, mean=9000, sd=1000), digits = 3)*100 ,"%")
## [1] "the probability that a randomly chosen light bulb lasts more than 10,500 hours 6.7 %"
  1. Describe the distribution of the mean lifespan of 15 light bulbs. The lifespan of the light bulbs are nearly normally distributed.
1000/sqrt(15)
## [1] 258.1989
  1. What is the probability that the mean lifespan of 15 randomly chosen light bulbs is more than 10,500 hours?
s <- (10500 - 9000)/258.2
p <- 1 - pnorm(s)
paste("Probability is ",p,", almost 0")
## [1] "Probability is  3.1339197903435e-09 , almost 0"
  1. Sketch the two distributions (population and sampling) on the same scale.
norm1x <- rnorm(100000, mean = 9000, sd = 1000)

norm2x <- rnorm (100000, mean = 9000, sd = 1000/sqrt(15))

normsx <- data.frame(sample = norm1x, means = norm2x)

ggplot(normsx) + geom_density(aes(x = sample), col="red") + geom_density(aes(x = means))

  1. Could you estimate the probabilities from parts (a) and (c) if the lifespans of light bulbs had a skewed distribution?

No, this is because the estimate requires a dist with little skew.

Same observation, different sample size. Suppose you conduct a hypothesis test based on a sample where the sample size is n = 50, and arrive at a p-value of 0.08. You then refer back to your notes and discover that you made a careless mistake, the sample size should have been n = 500. Will your p-value increase, decrease, or stay the same? Explain.

I believe With the sample size increases, p value will decrease.