Dataset

paged_table(dat2)

Descriptive

describe(dat2)

##           vars  n   mean     sd median trimmed    mad   min   max range skew
## M_FLEX       1 40 221.99  78.39 194.60  219.23  69.90  94.3 377.7 283.4 0.41
## JSTR_FLEX    2 40 202.28  72.05 186.50  198.03  74.87  87.0 351.0 264.0 0.42
## M_EXT        3 40 206.34  70.21 196.60  201.40  61.31  85.4 361.2 275.8 0.53
## JSTR_EXT     4 40 191.18  66.06 188.00  188.81  74.87  73.0 360.0 287.0 0.37
## M_ABD        5 40 189.59  59.16 183.90  187.88  74.13  91.2 316.3 225.1 0.23
## JSTR_ABD     6 40 176.75  53.48 168.50  176.19  67.46  88.0 264.0 176.0 0.10
## M_ADD        7 40 232.03  83.78 211.10  223.23  33.73  89.8 482.7 392.9 1.11
## JSTR_ADD     8 40 198.72  63.06 187.00  195.03  30.39  75.0 342.0 267.0 0.63
## JAM          9 40 427.38 120.78 405.65  424.01 142.48 230.5 665.1 434.6 0.20
## JT_GRIP     10 40 440.22 132.58 428.00  436.34 155.67 234.0 671.0 437.0 0.17
##           kurtosis    se
## M_FLEX       -1.04 12.39
## JSTR_FLEX    -0.97 11.39
## M_EXT        -0.27 11.10
## JSTR_EXT     -0.51 10.45
## M_ABD        -1.15  9.35
## JSTR_ABD     -1.45  8.46
## M_ADD         1.11 13.25
## JSTR_ADD      0.00  9.97
## JAM          -1.29 19.10
## JT_GRIP      -1.25 20.96

M_FLEX vs JSTR_FLEX

Regression Analysis

summary(lm(M_FLEX ~ JSTR_FLEX, data = dat2))

## 
## Call:
## lm(formula = M_FLEX ~ JSTR_FLEX, data = dat2)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -61.217 -23.671   4.278  20.551  51.148 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 16.85320   13.70988   1.229    0.227    
## JSTR_FLEX    1.01412    0.06394  15.861   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 28.77 on 38 degrees of freedom
## Multiple R-squared:  0.8688, Adjusted R-squared:  0.8653 
## F-statistic: 251.6 on 1 and 38 DF,  p-value: < 2.2e-16

Plot

ggplot(dat2, aes(M_FLEX, JSTR_FLEX)) +
  geom_point() +
  geom_smooth(method = lm, col = "black") +
  theme_bw() +
  ggtitle("Isometric Strength - Flexion") +
  ylab("JTECH (N)") +
  xlab("MicroFET2 (N)")

## `geom_smooth()` using formula 'y ~ x'

Reliability

flexion <- dat2 %>%
  select(M_FLEX, JSTR_FLEX)

icc(flexion, "twoway", "agreement")

##  Single Score Intraclass Correlation
## 
##    Model: twoway 
##    Type : agreement 
## 
##    Subjects = 40 
##      Raters = 2 
##    ICC(A,1) = 0.9
## 
##  F-Test, H0: r0 = 0 ; H1: r0 > 0 
##  F(39,8.84) = 27.1 , p = 8.85e-06 
## 
##  95%-Confidence Interval for ICC Population Values:
##   0.694 < ICC < 0.958

M_EXT vs JSTR_EXT

Regression Analysis

summary(lm(M_EXT ~ JSTR_EXT, data = dat2))

## 
## Call:
## lm(formula = M_EXT ~ JSTR_EXT, data = dat2)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -71.407 -13.606  -3.989   7.173  62.973 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 16.79845   12.54566   1.339    0.189    
## JSTR_EXT     0.99143    0.06211  15.963   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 25.62 on 38 degrees of freedom
## Multiple R-squared:  0.8702, Adjusted R-squared:  0.8668 
## F-statistic: 254.8 on 1 and 38 DF,  p-value: < 2.2e-16

Plot

ggplot(dat2, aes(M_EXT, JSTR_EXT)) +
  geom_point() +
  geom_smooth(method = lm, col = "black") +
  theme_bw() +
  ggtitle("Isometric Strength - Extension") +
  ylab("JTECH (N)") +
  xlab("MicroFET2 (N)")

## `geom_smooth()` using formula 'y ~ x'

Reliability

extension <- dat2 %>%
               select(M_EXT, JSTR_EXT)

icc(extension, "twoway", "agreement")

##  Single Score Intraclass Correlation
## 
##    Model: twoway 
##    Type : agreement 
## 
##    Subjects = 40 
##      Raters = 2 
##    ICC(A,1) = 0.91
## 
##  F-Test, H0: r0 = 0 ; H1: r0 > 0 
##  F(39,12.3) = 28 , p = 1.56e-07 
## 
##  95%-Confidence Interval for ICC Population Values:
##   0.767 < ICC < 0.959

M_ABD vs JSTR_ABD

Regression Analysis

summary(lm(M_ABD ~ JSTR_ABD, data = dat2))

## 
## Call:
## lm(formula = M_ABD ~ JSTR_ABD, data = dat2)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -41.117 -13.558  -0.575   8.847  63.205 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  6.33311   11.54185   0.549    0.586    
## JSTR_ABD     1.03681    0.06257  16.571   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 20.9 on 38 degrees of freedom
## Multiple R-squared:  0.8784, Adjusted R-squared:  0.8752 
## F-statistic: 274.6 on 1 and 38 DF,  p-value: < 2.2e-16

Plot

ggplot(dat2, aes(M_ABD, JSTR_ABD)) +
  geom_point() +
  geom_smooth(method = lm, col = "black") +
  theme_bw() +
  ggtitle("Isometric Strength - Abduction") +
  ylab("JTECH (N)") +
  xlab("MicroFET2 (N)")

## `geom_smooth()` using formula 'y ~ x'

Reliability

abduction <- dat2 %>%
                select(M_ABD, JSTR_ABD)

icc(abduction, "twoway", "agreement")

##  Single Score Intraclass Correlation
## 
##    Model: twoway 
##    Type : agreement 
## 
##    Subjects = 40 
##      Raters = 2 
##    ICC(A,1) = 0.91
## 
##  F-Test, H0: r0 = 0 ; H1: r0 > 0 
##  F(39,11.4) = 28.6 , p = 3.82e-07 
## 
##  95%-Confidence Interval for ICC Population Values:
##   0.758 < ICC < 0.96

M_ADD vs JSTR_ADD

Regression Analysis

summary(lm(M_ADD ~ JSTR_ADD, data = dat2))

## 
## Call:
## lm(formula = M_ADD ~ JSTR_ADD, data = dat2)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -72.993 -15.911   3.526  15.357 107.375 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -10.78611   17.62438  -0.612    0.544    
## JSTR_ADD      1.22187    0.08463  14.438   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 33.33 on 38 degrees of freedom
## Multiple R-squared:  0.8458, Adjusted R-squared:  0.8418 
## F-statistic: 208.4 on 1 and 38 DF,  p-value: < 2.2e-16

Plot

ggplot(dat2, aes(M_ADD, JSTR_ADD)) +
  geom_point() +
  geom_smooth(method = lm, col = "black") +
  theme_bw() +
  ggtitle("Isometric Strength - Aduction") +
  ylab("JTECH (N)") +
  xlab("MicroFET2 (N)")

## `geom_smooth()` using formula 'y ~ x'

Reliability

aduction <- dat2 %>%
               select(M_ADD, JSTR_ADD)

icc(aduction, "twoway", "agreement")

##  Single Score Intraclass Correlation
## 
##    Model: twoway 
##    Type : agreement 
## 
##    Subjects = 40 
##      Raters = 2 
##    ICC(A,1) = 0.805
## 
##  F-Test, H0: r0 = 0 ; H1: r0 > 0 
##  F(39,4.92) = 16.2 , p = 0.00295 
## 
##  95%-Confidence Interval for ICC Population Values:
##   0.299 < ICC < 0.926

JAMAR vs JTECH

Regression Analysis

summary(lm(JAM ~ JT_GRIP, data = dat2))

## 
## Call:
## lm(formula = JAM ~ JT_GRIP, data = dat2)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -63.73 -21.91  -3.86  21.00  92.47 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 43.84519   19.83344   2.211   0.0332 *  
## JT_GRIP      0.87121    0.04318  20.174   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 35.76 on 38 degrees of freedom
## Multiple R-squared:  0.9146, Adjusted R-squared:  0.9124 
## F-statistic:   407 on 1 and 38 DF,  p-value: < 2.2e-16

Plot

ggplot(dat2, aes(JAM, JT_GRIP)) +
  geom_point() +
  geom_smooth(method = lm, col = "black") +
  theme_bw() +
  ggtitle("Grip Strength - JAMAR vs JTECH") +
  ylab("JTECH (N)") +
  xlab("JAMAR (N)")

## `geom_smooth()` using formula 'y ~ x'

Reliability

jamar.vs.JT_GRIP <- dat2 %>%
                       select(JAM, JT_GRIP)

icc(jamar.vs.JT_GRIP, "twoway", "agreement")

##  Single Score Intraclass Correlation
## 
##    Model: twoway 
##    Type : agreement 
## 
##    Subjects = 40 
##      Raters = 2 
##    ICC(A,1) = 0.948
## 
##  F-Test, H0: r0 = 0 ; H1: r0 > 0 
##    F(39,33) = 40.8 , p = 3.53e-19 
## 
##  95%-Confidence Interval for ICC Population Values:
##   0.901 < ICC < 0.973

Regression Analysis

Nelson Carvas Jr

6/28/2021

Dataset

Descriptive

M_FLEX vs JSTR_FLEX

Regression Analysis

Plot

Reliability

M_EXT vs JSTR_EXT

Regression Analysis

Plot

Reliability

M_ABD vs JSTR_ABD

Regression Analysis

Plot

Reliability

M_ADD vs JSTR_ADD

Regression Analysis

Plot

Reliability

JAMAR vs JTECH

Regression Analysis

Plot

Reliability