Include your answers in this document in the sections below the rubric.

Rubric

  1. (1 p) Participate in two data collection and entering activities. Describe any potential issues about the data collection process.

  2. (2 p) Interpret correlation for Males, Females, and Everyone combined.

  3. (1 p) How would the correlation change if both hand span and height were measured in inches?

  4. (2 p) Why is there a difference in the strength of the correlation for everyone compared to either gender separately?

  5. (2 p) Describe the relationships between the scores and the guessed score.

  6. (2 p) Identify and explain the most surprising feature of these data.


Height vs Hand Span

Procedure:

  1. Record your height in inches. For example 5’0" is 60 inches.
  2. Use a ruler to measure your hand span in centimeters: the distance from the tip of your thumb to pinky finger with your hand splayed as wide as possible.
  3. Have someone from your table enter your measurements at this link (as well as your gender): https://docs.google.com/spreadsheets/d/1D22hva-Em1ZMsLpmo5yNJpuwzAHScZbphN281wunw9I
  4. Analysis.

Describe any potential issues about the data collection process.

# install.packages("gsheet")

# Height vs Hand Span
library(gsheet)
dat.hand.url <- "docs.google.com/spreadsheets/d/1D22hva-Em1ZMsLpmo5yNJpuwzAHScZbphN281wunw9I"
dat.hand <- gsheet2tbl(dat.hand.url)
## No encoding supplied: defaulting to UTF-8.
dat.hand <- as.data.frame(dat.hand)
dat.hand <- na.omit(dat.hand)
dat.hand$Gender_M_F <- factor(dat.hand$Gender_M_F, levels = c("F", "M"))

str(dat.hand)
## 'data.frame':    84 obs. of  5 variables:
##  $ Table      : int  1 1 1 1 1 1 2 2 2 2 ...
##  $ Person     : int  1 4 5 6 7 9 1 2 3 4 ...
##  $ Gender_M_F : Factor w/ 2 levels "F","M": 1 2 1 2 1 2 1 1 1 1 ...
##  $ Height_in  : num  62 72 65 70 60 72.9 69.5 67 68.5 64.5 ...
##  $ HandSpan_cm: num  17 22.5 19 21 18.5 22.5 19.5 20 20 20 ...
##  - attr(*, "na.action")=Class 'omit'  Named int [1:42] 2 3 8 17 18 27 34 35 36 44 ...
##   .. ..- attr(*, "names")= chr [1:42] "2" "3" "8" "17" ...
# Plot the data using ggplot and ggpairs
library(ggplot2)
library(GGally)
p1 <- ggpairs(dat.hand[,c("Gender_M_F", "Height_in", "HandSpan_cm")]
            , mapping = ggplot2::aes(colour = Gender_M_F)
            , lower = list(continuous = "smooth")
            , diag  = list(continuous = "density")
            #, upper = list(params = list(corSize = 6))
            )
## Warning in check_and_set_ggpairs_defaults("diag", diag, continuous =
## "densityDiag", : Changing diag$continuous from 'density' to 'densityDiag'
print(p1)
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

Interpret correlation for Males, Females, and Everyone combined.

How would the correlation change if both hand span and height were measured in inches?

Why is there a difference in the strength of the correlation for everyone compared to either gender separately?


Word memory scores

15 seconds to memorize 15 words: http://www.randomlists.com/random-words?qty=15

Procedure:

  1. Round 1
    1. Put up a list of words for 15 seconds and view.
    2. Have 60 seconds to write/type as many words as you can remember.
    3. Score yourself (anonymous, so honesty is best – we’re all going to be bad at this).
  2. Given your first performance, make a guess at how many words you’ll remember in round 2.
  3. Round 2 (repeat of round 1)
  4. Have someone from your table enter your scores at this link (as well as your gender and student status): https://docs.google.com/spreadsheets/d/1Y3PJgUFgf0mfHnyhsJ28S005trBkvI7rsXvVdB2DesA
  5. Analysis.
# Memory Scores
library(gsheet)
dat.memory.url <- "docs.google.com/spreadsheets/d/1Y3PJgUFgf0mfHnyhsJ28S005trBkvI7rsXvVdB2DesA"
dat.memory <- gsheet2tbl(dat.memory.url)
## No encoding supplied: defaulting to UTF-8.
dat.memory <- as.data.frame(dat.memory)
dat.memory <- na.omit(dat.memory)
dat.memory$Gender_M_F <- factor(dat.memory$Gender_M_F, levels = c("F", "M"))
dat.memory$UGrad_Grad <- factor(dat.memory$UGrad_Grad)
dat.memory$EnglishNativeLanguage <- factor(dat.memory$EnglishNativeLanguage)
# Plot the data using ggplot and ggpairs
library(ggplot2)
library(GGally)
p2 <- ggpairs(dat.memory[,c("Gender_M_F", "UGrad_Grad", "EnglishNativeLanguage", "Score_1", "Guessed_2", "Score_2")]
            , mapping = ggplot2::aes(colour = EnglishNativeLanguage) #, shape = UGrad_Grad)
            , lower = list(continuous = "smooth")
            , diag  = list(continuous = "density")
            #, upper = list(params = list(corSize = 6))
            )
## Warning in check_and_set_ggpairs_defaults("diag", diag, continuous =
## "densityDiag", : Changing diag$continuous from 'density' to 'densityDiag'
print(p2)
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

library(ggplot2)
p <- ggplot(dat.memory, aes(x = Score_1, y = Guessed_2))
p <- p + geom_abline(intercept = 0, slope = 1, linetype = "dashed", alpha = 0.5)
p <- p + geom_jitter(aes(colour = EnglishNativeLanguage), position = position_jitter(width = 0.1), alpha = 1/2)
p <- p + geom_smooth(method = lm)
p <- p + scale_y_continuous(limits=c(0, 15))
p <- p + scale_x_continuous(limits=c(0, 15))
p <- p + coord_fixed(ratio = 1)
print(p)
## Warning: Removed 1 rows containing missing values (geom_point).

library(ggplot2)
p <- ggplot(dat.memory, aes(x = Guessed_2, y = Score_2))
p <- p + geom_abline(intercept = 0, slope = 1, linetype = "dashed", alpha = 0.5)
p <- p + geom_jitter(aes(colour = EnglishNativeLanguage), position = position_jitter(width = 0.1), alpha = 1/2)
p <- p + geom_smooth(method = lm)
p <- p + scale_y_continuous(limits=c(0, 15))
p <- p + scale_x_continuous(limits=c(0, 15))
p <- p + coord_fixed(ratio = 1)
print(p)
## Warning: Removed 1 rows containing missing values (geom_point).

library(ggplot2)
p <- ggplot(dat.memory, aes(x = Score_1, y = Score_2))
p <- p + geom_abline(intercept = 0, slope = 1, linetype = "dashed", alpha = 0.5)
p <- p + geom_jitter(aes(colour = EnglishNativeLanguage), position = position_jitter(width = 0.1), alpha = 1/2)
p <- p + geom_smooth(method = lm)
p <- p + scale_y_continuous(limits=c(0, 15))
p <- p + scale_x_continuous(limits=c(0, 15))
p <- p + coord_fixed(ratio = 1)
print(p)

Describe the relationships between the scores and the guessed score.

Explain the most surprising feature of these data.