Skipping · ·

overview

the longer the set, the easier to make counting mistakes
the longer the set, the more time for speed variations to even out, resulting in more consistent times
skipping on hard surface let me jump fast
soft carpet : i have to jump harder, et il y a plus de friction entre la moquette et la corde à sauter
whey + coffee + milk + water 15-30min before reduces errors
fatigue increases errors. it is hard to skip slowly
transitioning from low-normal-high speed is high-risk for errors
skipping faster than 2.7 jumps/s makes it hard to count
y = b0 + b1*x1 + b2*x2 + b3*t + u seems reasonable for now
what about a panel? binary for each day? or just do panel data. each day.

in R studio

# IMPORT DATA
path = "\\skipping\\skipping_data.csv"
df = read.csv(path)

# CONDITIONALLY REMOVE DATA
# WHEN I LOSE TRACK OF THE COUNT, I ASSIGN A VALUE OF 0
data = df[df$number_of_jumps != 0, ]

# RENAME VARIABLES
x1 = data$number_of_seconds
x2 = data$number_of_errors
t = data$date # THE DATE AS DAYS FROM JAN 1, 1900
y = data$number_of_jumps

# A LITTLE GRAPH
plot(x1, y)

# OLS
model = lm(y ~ x1 + x2 + t, data=data)
summary(model)

using rafficot

n_total
n_omitted
n_included

xTx
(xTx)_inv
xTy
the coefficients b = (xTx)_inv * xTy b = x^Tx^-1 * x^Ty b = (x^{T}x)^{-1} x^{T}y

  # TRY STHING ELSE
  y2 = y/x1
  plot(x1, y2)
  model2 = lm(y2 ~ x1 + x2)
  summary(model2)
  abline(model2, col="orange")

  # CALCULATE THE BINS. BE CAREFUL. IT ONLY ACCEPTS NUMBER LITERALS. 

  # A USEFUL HISTOGRAM : THE AVG NUMBER OF JUMPERS PER SECOND 
  hist(y2,
       main="average jumps per second",
       breaks = c(0.05*40:60))

  # ANOTHER USEFUL HISTOGRAM : NUMBER OF SECONDS (DURATION OF A SET) 
  x1_max = max(x1)
  bin_width = 10
  bin_upper_limit = ceiling(x1_max/bin_width)
  hist(x1,
       main="number of seconds",
       breaks = c(bin_width*0:bin_upper_limit))
  
  # MAYBE LETS INCLUDE TIME (t REPRESENTS THE DAYS FROM JAN 1, 1900) 


  # MAKES MORE SENSE TO LOOK AT x1*t I THINK 
  x1_sq = x1 * x1
  x1t = x1 * t
  model = lm(y ~ x1 + x1_sq + x2 + x1t)
  summary(model)

  max()
  mean()
  sum()

should i use other terms? x^2? for instance ?
remember. b = (x^{T}x)^{-1} x^{T}y
show in R_MATRIX

total number of sets relevant sets within range
number of bins	-		+

frequency of number of seconds (the duration of a set)

	number of seconds

number of jumps vs skipping duration (number of seconds)

number of seconds

overview

in R studio

using rafficot

histogram