<jupyter_start><jupyter_text>D4P   Kinesia Score: The number of keystrokes in 20 seconds Kinesia Score is analyzed by comparing the mean bidirectional response times (in milliseconds) using the Wilcoxon signed-rank test. The number of times both keys are hit are inversely proportional to the mean bidirectional response time.$$\textrm{keys hit} = k \cdot \mu_{bidirectional}^{-1} \textrm{   where  } k=20\textrm{s} \cdot 1000 \textrm{ms/s}$$Example: In the Dominant Hand with Horizontal Keyboard (Before) analysis, Subject_DL hit 29 keys: $$\frac{20000 \textrm{ms}}{696.25 \textrm{ms}} \approx 29 \textrm{  total hit keys}$$ The data is analyzed using non-parametric tests due to the small sample size of 4. Normality tests (e.g. Kolmogorov-Smirnov or Shapiro-Wilk) are not applied because small samples most often pass (p > 0.05).   Dominant Hand with Horizontal Keyboard<jupyter_code>### -------------------------
### Wilcoxon signed-rank test
### -------------------------

Input = ("
 Subject        Before               After
 subject_DL     696.25               650.9333333333333
 subject_JF     712.5714285714286    724.6666666666666
 subject_LV     369.51851851851853   362.0181818181818
 subject_PEV    547.5833333333334    605.96875
")

Data = read.table(textConnection(Input),header=TRUE)

wilcox.test(Data$Before, 
            Data$After, 
            paired=TRUE)<jupyter_output><empty_output><jupyter_text>Since the p-value is greater than 0.05, we conclude that the means have remained essentially unchanged (we accept the null hypothesis H0).<jupyter_code>plot(Data$Before, Data$After, 
    pch = 4,
    xlab="Before",
    ylab="After")

abline(0,1, col="blue", lwd=2)<jupyter_output><empty_output><jupyter_text>Plot of paired samples from a Wilcoxon signed-rank test. Data points above and to the left of the blue one-to-one line indicate observations with a higher value for After than for Before. The line of no improvement. Anything below the line is a decreased response time (i.e. improvement), and anything above is increased response time.  Dominant Hand with Vertical Keyboard<jupyter_code>### -------------------------
### Wilcoxon signed-rank test
### -------------------------

Input = ("
 Subject        Before               After
 subject_DL     721.3703703703703    743.2692307692307
 subject_JF     685.551724137931     747.5769230769231
 subject_LV     404.2040816326531    312.6507936507937
 subject_PEV    605.46875            629.9032258064516
")

Data = read.table(textConnection(Input),header=TRUE)

wilcox.test(Data$Before, 
            Data$After, 
            paired=TRUE)<jupyter_output><empty_output><jupyter_text>Since the p-value is greater than 0.05, we conclude that the means have remained essentially unchanged (we accept the null hypothesis H0).<jupyter_code>plot(Data$Before, Data$After, 
    pch = 4,
    xlab="Before",
    ylab="After")

abline(0,1, col="red", lwd=2)<jupyter_output><empty_output><jupyter_text>The line of no improvement. Anything below the line is a decreased response time (i.e. improvement), and anything above is increased response time.  Non-Dominant Hand with Horizontal Keyboard<jupyter_code>### -------------------------
### Wilcoxon signed-rank test
### -------------------------

Input = ("
 Subject        Before               After
 subject_DL     714.25               672.551724137931
 subject_JF     679.2758620689655    600.3636363636364
 subject_LV     351.2142857142857    346.8070175438597
 subject_PEV    584.2058823529412    574.4117647058823
")

Data = read.table(textConnection(Input),header=TRUE)

wilcox.test(Data$Before, 
            Data$After, 
            paired=TRUE)<jupyter_output><empty_output><jupyter_text>Since the p-value is greater than 0.05, we conclude that the means have remained essentially unchanged (we accept the null hypothesis H0).<jupyter_code>plot(Data$Before, Data$After, 
    pch = 4,
    xlab="Before",
    ylab="After")

abline(0,1, col="red", lwd=2)<jupyter_output><empty_output><jupyter_text>The line of no improvement. Anything below the line is a decreased response time (i.e. improvement), and anything above is increased response time.  Non-Dominant Hand with Vertical Keyboard<jupyter_code>### -------------------------
### Wilcoxon signed-rank test
### -------------------------

Input = ("
 Subject        Before               After
 subject_DL     681.8620689655172    760.9230769230769
 subject_JF     662.9666666666667    681.8275862068965
 subject_LV     344.08620689655174   368.5925925925926
 subject_PEV    591.7878787878788    591.5757575757576
")

Data = read.table(textConnection(Input),header=TRUE)

wilcox.test(Data$Before, 
            Data$After, 
            paired=TRUE)<jupyter_output><empty_output><jupyter_text>Since the p-value is greater than 0.05, we conclude that the means have remained essentially unchanged (we accept the null hypothesis H0).<jupyter_code>plot(Data$Before, Data$After, 
    pch = 4,
    xlab="Before",
    ylab="After")

abline(0,1, col="red", lwd=2)<jupyter_output><empty_output>
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