Teamwork Report
Team Member | Attendence | Author | Contribution % |
: ———————-| : ——– |: ——– | : ———– :
Lexi Clifford | yes | no | 25% |
Amanda Beach | yes | no | 25% |
Michelle Rodriguez | yes | no | 25% |
Kathleen Vern | yes | yes | 25% |
Total | | | 100% |
Exercise 1:
The distribution of the house area in this sample range from 693-3086 with a mean at 1463 and the median at 1382. The IQR is 600 with the 25th percentile at 1111 and the 75th percentile at 1712. Given that the graph is unimodal with a slight right skew, (learned both in graphing the data and the fact that the median is less than the mean) we are able to suggest that to most accurately analyze the data we should utilize the robust statistics. The “typical” size should thus suggest the median value of area as meaning we believe that the typical area or size of a house is 1382 square feet.
data(ames)
set.seed(999)
n <- 60
samp <- sample_n(ames, n)
samp %>%
summarise(mu = mean(area), pop_med = median(area),
sigma = sd(area), pop_iqr = IQR(area),
pop_min = min(area), pop_max = max(area),
pop_q1 = quantile(area, 0.25), # first quartile, 25th percentile
pop_q3 = quantile(area, 0.75)) # third quartile, 75th percentile
## # A tibble: 1 x 8
## mu pop_med sigma pop_iqr pop_min pop_max pop_q1 pop_q3
## <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 1514 1457 566. 581. 480 2956 1108. 1689.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
A tibble: 1 x 8 mu pop_med sigma pop_iqr pop_min pop_max pop_q1 pop_q3 1 1463. 1382. 469. 600. 693 3086 1111 1712.
https://labs-az-02.oit.duke.edu:30623/graphics/plot.png?width=574&height=299&randomizer=346275109
Exercise 2:
We would not expect another group’s distribution to be identical to ours. While the distributions would probably be similar, point estimates do not give the full range of population parameters and some variables (i.e. the sample means) can vary among groups. However, as the sample size increases, we would expect the statistics of each group to look more similar and mimic the sample mean of the populations more closely
Exercise 3:
For the confidence interval to be valid the sample mean must be normally distributed with a standard error s/sqrt(n). The conditions necessary for this to be met are having random sampling and ensured independence involved in producing 10 or more successes and failures. There must also be the independence condition than the population is less than 10% of the entire population.
Exercise 4:
95% confidence means that we are 95% confident that the mean of the true population (point score) falls within the given range
Exercise 5:
Our confidence interval captures the true average size of houses in Ames as the range is between 1345-1582 and the true average of this sample is 1499.69. Our neighbor’s interval will also capture the true average size of houses in Ames.
## [1] 1.959964
## [1] 1.959964
## # A tibble: 1 x 2
## lower upper
## <dbl> <dbl>
## 1 1371. 1657.
Confidence Interval 95%: A tibble: 1 x 2 lower upper 1 1345. 1582.
Population Average (mu): mu 1 1499.69
Exercise 6:
The proportion of these intervals we would expect to capture the true population mean is 0.95 because this indicates that we would expect to see a confidence interval that contains the true population mean 95% of the time.
Exercise 7:
The proportion of our confidence intervals that include the true population mean is 48/50, or 96%. While this proportion is not exactly equal to the confidence level of 95%, given that the test was only done 50 times there is not possible value that could have given the result of 95% making this value acceptable for our confidence.
set.seed(999)
ci <- ames %>%
rep_sample_n(size = n, reps = 50, replace = TRUE) %>%
summarise(lower = mean(area) - z_star_95 * (sd(area) / sqrt(n)),
upper = mean(area) + z_star_95 * (sd(area) / sqrt(n)))
ci <- ci %>%
mutate(capture_mu = ifelse(lower < params$mu & upper > params$mu, "yes", "no"))
ci_data <- data.frame(ci_id = c(1:50, 1:50),
ci_bounds = c(ci$lower, ci$upper),
capture_mu = c(ci$capture_mu, ci$capture_mu))
qplot(data = ci_data, x = ci_bounds, y = ci_id,
group = ci_id, color = capture_mu) +
geom_point(size = 2) + # add points at the ends, size = 2
geom_line() + # connect with lines
geom_vline(xintercept = params$mu, color = "darkgray") # draw vertical line
https://labs-az-02.oit.duke.edu:30623/graphics/plot.png?width=574&height=299&randomizer=868145293
Exercise 8:
The appropriate critical value for a confidence interval of 99% is 2.575829.
## [1] 2.575829
## [1] 2.575829
Exercise 9:
The proportion of our confidence intervals that include the true population mean is 50/50, or 100%. While this proportion is not exactly equal to the confidence level of 99%, given that the test was only done 50 times there is not possible value that could have given the result of 99% making this value acceptable for our confidence.
set.seed(999)
ci <- ames %>%
rep_sample_n(size = n, reps = 50, replace = TRUE) %>%
summarise(lower = mean(area) - z_star_99 * (sd(area) / sqrt(n)),
upper = mean(area) + z_star_99 * (sd(area) / sqrt(n)))
ci <- ci %>%
mutate(capture_mu = ifelse(lower < params$mu & upper > params$mu, "yes", "no"))
ci_data <- data.frame(ci_id = c(1:50, 1:50),
ci_bounds = c(ci$lower, ci$upper),
capture_mu = c(ci$capture_mu, ci$capture_mu))
qplot(data = ci_data, x = ci_bounds, y = ci_id,
group = ci_id, color = capture_mu) +
geom_point(size = 2) + # add points at the ends, size = 2
geom_line() + # connect with lines
geom_vline(xintercept = params$mu, color = "darkgray") # draw vertical line
https://labs-az-02.oit.duke.edu:30623/graphics/plot.png?width=574&height=299&randomizer=-320888409
---
title: "Lab 5"
author: "Above Average"
date: "Feb 18, 2019"
output: oilabs::lab_report
---

```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
library(tidyverse)
library(oilabs)
library(dplyr)
library(ggplot2)
data(ames)
```

## Teamwork Report

| Team Member | Attendence | Author | Contribution % |
| : ----------------------| : -------- |: -------- | : ----------- :
| Lexi Clifford      | yes | no | 25% |
| Amanda Beach       | yes | no | 25% |
| Michelle Rodriguez | yes | no | 25% |
| Kathleen Vern      | yes | yes | 25% | 
| Total | | | 100% | 


* * *

### Exercise 1: 
The distribution of the house area in this sample range from 693-3086 with a mean at 1463 and the median at 1382. The IQR is 600 with the 25th percentile at 1111 and the 75th percentile at 1712. Given that the graph is unimodal with a slight right skew, (learned both in graphing the data and the fact that the median is less than the mean) we are able to suggest that to most accurately analyze the data we should utilize the robust statistics. The “typical” size should thus suggest the median value of area as meaning we believe that the typical area or size of a house is 1382 square feet.

```{r}
data(ames)
set.seed(999)
n <- 60
samp <- sample_n(ames, n)
samp %>%
     summarise(mu = mean(area), pop_med = median(area), 
              sigma = sd(area), pop_iqr = IQR(area),
              pop_min = min(area), pop_max = max(area),
              pop_q1 = quantile(area, 0.25),  # first quartile, 25th percentile
              pop_q3 = quantile(area, 0.75))  # third quartile, 75th percentile
qplot(data = samp, x = area, geom = "histogram")
```
A tibble: 1 x 8
     mu pop_med sigma pop_iqr pop_min pop_max pop_q1 pop_q3
  <dbl>   <dbl> <dbl>   <dbl>   <dbl>   <dbl>  <dbl>  <dbl>
1 1463.   1382.  469.    600.     693    3086   1111  1712.

https://labs-az-02.oit.duke.edu:30623/graphics/plot.png?width=574&height=299&randomizer=346275109

### Exercise 2:
We would not expect another group’s distribution to be identical to ours. While the distributions would probably be similar, point estimates do not give the full range of population parameters and some variables (i.e. the sample means) can vary among groups. However, as the sample size increases, we would expect the statistics of each group to look more similar and mimic the sample mean of the populations more closely

### Exercise 3:
For the confidence interval to be valid the sample mean must be normally distributed with a standard error s/sqrt(n). The conditions necessary for this to be met are having random sampling and ensured independence involved in producing 10 or more successes and failures. There must also be the independence condition than the population is less than 10% of the entire population.

### Exercise 4:
95% confidence means that we are 95% confident that the mean of the true population (point score) falls within the given range
 
### Exercise 5:
Our confidence interval captures the true average size of houses in Ames as the range is between 1345-1582 and the true average of this sample is 1499.69. Our neighbor’s interval will also capture the true average size of houses in Ames.
```{r}
set.seed(999)
z_star_95 <- qnorm(0.975)
z_star_95
1.959964
samp %>%
     summarise(lower = mean(area) - z_star_95 * (sd(area) / sqrt(n)),
               upper = mean(area) + z_star_95 * (sd(area) / sqrt(n)))
params <- ames %>%
     summarise(mu = mean(area))
```
Confidence Interval 95%:
A tibble: 1 x 2
  lower upper
  <dbl> <dbl>
1 1345. 1582.

Population Average (mu):
mu
1	1499.69

### Exercise 6:
The proportion of these intervals we would expect to capture the true population mean is 0.95 because this indicates that we would expect to see a confidence interval that contains the true population mean 95% of the time.

### Exercise 7:
The proportion of our confidence intervals that include the true population mean is 48/50, or 96%. While this proportion is not exactly equal to the confidence level of 95%, given that the test was only done 50 times there is not possible value that could have given the result of 95% making this value acceptable for our confidence. 
```{r}
set.seed(999)
ci <- ames %>%
     rep_sample_n(size = n, reps = 50, replace = TRUE) %>%
     summarise(lower = mean(area) - z_star_95 * (sd(area) / sqrt(n)),
               upper = mean(area) + z_star_95 * (sd(area) / sqrt(n)))
ci <- ci %>%
     mutate(capture_mu = ifelse(lower < params$mu & upper > params$mu, "yes", "no"))
ci_data <- data.frame(ci_id = c(1:50, 1:50),
                      ci_bounds = c(ci$lower, ci$upper),
                      capture_mu = c(ci$capture_mu, ci$capture_mu))
qplot(data = ci_data, x = ci_bounds, y = ci_id, 
       group = ci_id, color = capture_mu) +
     geom_point(size = 2) +  # add points at the ends, size = 2
     geom_line() +           # connect with lines
     geom_vline(xintercept = params$mu, color = "darkgray") # draw vertical line
```
https://labs-az-02.oit.duke.edu:30623/graphics/plot.png?width=574&height=299&randomizer=868145293 

### Exercise 8:
The appropriate critical value for a confidence interval of 99% is 2.575829.
```{r}
z_star_99 <- qnorm(0.995)
z_star_99
2.575829
```

### Exercise 9:
The proportion of our confidence intervals that include the true population mean is 50/50, or 100%. While this proportion is not exactly equal to the confidence level of 99%, given that the test was only done 50 times there is not possible value that could have given the result of 99% making this value acceptable for our confidence.  
```{r}
set.seed(999)
ci <- ames %>%
    rep_sample_n(size = n, reps = 50, replace = TRUE) %>%
    summarise(lower = mean(area) - z_star_99 * (sd(area) / sqrt(n)),
               upper = mean(area) + z_star_99 * (sd(area) / sqrt(n)))
ci <- ci %>%
     mutate(capture_mu = ifelse(lower < params$mu & upper > params$mu, "yes", "no"))
ci_data <- data.frame(ci_id = c(1:50, 1:50),
                       ci_bounds = c(ci$lower, ci$upper),
                       capture_mu = c(ci$capture_mu, ci$capture_mu))
qplot(data = ci_data, x = ci_bounds, y = ci_id, 
      group = ci_id, color = capture_mu) +
    geom_point(size = 2) +  # add points at the ends, size = 2
     geom_line() +           # connect with lines
     geom_vline(xintercept = params$mu, color = "darkgray") # draw vertical line
```
https://labs-az-02.oit.duke.edu:30623/graphics/plot.png?width=574&height=299&randomizer=-320888409 

