Check the Gap: Facemask Performance and Exhaled Aerosol Distributions Around the Wearer
Emily L. Kolewe,☨ Zachary S. Stillman,☨ Ian R. Woodward,☨ and Catherine A. Fromen, Ph.D.
Department of Chemical and Biomolecular Engineering, University of Delaware, Newark, DE 19716☨Contributed Equally
*Corresponding Author
cfromen@udel.edu
2020-09-01 (Last compiled)
Contour Plots by Aerosol Size
Common Scales
Individual Scales
Individual Models and Plots
## [1] "No Mask 0.3"
## [1] -50000 0 2000 4000 6000 8000 10000 12000 14000 16000
## [11] 18000 20000 22000 24000 26000
##
## Call:
## lm(formula = background_subtracted ~ poly(r, degree = 2) + theta +
## r:theta, data = data_mask)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4236 -2554 -1495 1807 8328
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 473.77 1341.15 0.353 0.72956
## poly(r, degree = 2)1 537.89 5690.03 0.095 0.92613
## poly(r, degree = 2)2 11841.24 4023.46 2.943 0.01142 *
## theta 211.90 40.16 5.276 0.00015 ***
## theta:r -42.10 10.26 -4.105 0.00124 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 4023 on 13 degrees of freedom
## Multiple R-squared: 0.8025, Adjusted R-squared: 0.7417
## F-statistic: 13.2 on 4 and 13 DF, p-value: 0.0001641
## [1] "Surgical 0.3"
## [1] -50000 0 2000 4000 6000 8000 10000 12000 14000 16000
## [11] 18000 20000 22000 24000 26000
##
## Call:
## lm(formula = background_subtracted ~ poly(r, degree = 2) + theta +
## r:theta, data = data_mask)
##
## Residuals:
## Min 1Q Median 3Q Max
## -592.70 -288.92 -2.51 226.52 710.28
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4503.750 152.129 29.605 2.57e-13 ***
## poly(r, degree = 2)1 305.352 645.431 0.473 0.64399
## poly(r, degree = 2)2 -1781.893 456.388 -3.904 0.00181 **
## theta -2.045 4.555 -0.449 0.66091
## theta:r -2.127 1.163 -1.829 0.09049 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 456.4 on 13 degrees of freedom
## Multiple R-squared: 0.7265, Adjusted R-squared: 0.6424
## F-statistic: 8.634 on 4 and 13 DF, p-value: 0.001252
## [1] "SewnPC 0.3"
## [1] -50000 0 2000 4000 6000 8000 10000 12000 14000 16000
## [11] 18000 20000 22000 24000 26000
##
## Call:
## lm(formula = background_subtracted ~ poly(r, degree = 2) + theta +
## r:theta, data = data_mask)
##
## Residuals:
## Min 1Q Median 3Q Max
## -843.81 -433.02 -85.02 392.41 1219.38
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 6.069e+03 2.404e+02 25.243 1.98e-12 ***
## poly(r, degree = 2)1 -1.551e+03 1.020e+03 -1.521 0.15224
## poly(r, degree = 2)2 -2.388e+03 7.213e+02 -3.311 0.00563 **
## theta 4.298e-01 7.199e+00 0.060 0.95330
## theta:r 8.402e-02 1.839e+00 0.046 0.96425
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 721.3 on 13 degrees of freedom
## Multiple R-squared: 0.5428, Adjusted R-squared: 0.4021
## F-statistic: 3.858 on 4 and 13 DF, p-value: 0.02797
## [1] "Sewn 0.3"
## [1] -50000 0 2000 4000 6000 8000 10000 12000 14000 16000
## [11] 18000 20000 22000 24000 26000
##
## Call:
## lm(formula = background_subtracted ~ poly(r, degree = 2) + theta +
## r:theta, data = data_mask)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2010.21 -1051.13 -90.17 1133.26 2356.88
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4301.500 504.506 8.526 1.11e-06 ***
## poly(r, degree = 2)1 8114.951 2140.439 3.791 0.00224 **
## poly(r, degree = 2)2 -5427.785 1513.519 -3.586 0.00332 **
## theta 55.041 15.107 3.643 0.00297 **
## theta:r -10.869 3.858 -2.817 0.01454 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1514 on 13 degrees of freedom
## Multiple R-squared: 0.7168, Adjusted R-squared: 0.6296
## F-statistic: 8.226 on 4 and 13 DF, p-value: 0.001554
## [1] "N95L 0.3"
## [1] -50000 0 2000 4000 6000 8000 10000 12000 14000 16000
## [11] 18000 20000 22000 24000 26000
##
## Call:
## lm(formula = background_subtracted ~ poly(r, degree = 2) + theta +
## r:theta, data = data_mask)
##
## Residuals:
## Min 1Q Median 3Q Max
## -598.29 -333.68 77.38 180.79 713.15
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 7097.3056 143.4463 49.477 3.44e-16 ***
## poly(r, degree = 2)1 -633.7992 608.5911 -1.041 0.3167
## poly(r, degree = 2)2 -947.4380 430.3389 -2.202 0.0464 *
## theta -6.5520 4.2955 -1.525 0.1511
## theta:r -0.5877 1.0970 -0.536 0.6012
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 430.3 on 13 degrees of freedom
## Multiple R-squared: 0.6431, Adjusted R-squared: 0.5333
## F-statistic: 5.857 on 4 and 13 DF, p-value: 0.006394
## [1] "N95T 0.3"
## [1] -50000 0 2000 4000 6000 8000 10000 12000 14000 16000
## [11] 18000 20000 22000 24000 26000
##
## Call:
## lm(formula = background_subtracted ~ poly(r, degree = 2) + theta +
## r:theta, data = data_mask)
##
## Residuals:
## Min 1Q Median 3Q Max
## -245.47 -48.73 11.66 74.07 153.66
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 415.7778 38.3199 10.850 6.93e-08 ***
## poly(r, degree = 2)1 -184.7561 162.5775 -1.136 0.27630
## poly(r, degree = 2)2 -330.6695 114.9596 -2.876 0.01298 *
## theta 3.7657 1.1475 3.282 0.00595 **
## theta:r -0.1812 0.2930 -0.618 0.54705
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 115 on 13 degrees of freedom
## Multiple R-squared: 0.7601, Adjusted R-squared: 0.6862
## F-statistic: 10.29 on 4 and 13 DF, p-value: 0.0005553
## [1] "SewnF 0.3"
## [1] -50000 0 2000 4000 6000 8000 10000 12000 14000 16000
## [11] 18000 20000 22000 24000 26000
##
## Call:
## lm(formula = background_subtracted ~ poly(r, degree = 2) + theta +
## r:theta, data = data_mask)
##
## Residuals:
## Min 1Q Median 3Q Max
## -392.11 -50.76 -39.98 81.40 525.51
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 5267.4167 78.9766 66.696 < 2e-16 ***
## poly(r, degree = 2)1 449.6548 335.0695 1.342 0.203
## poly(r, degree = 2)2 245.8332 236.9299 1.038 0.318
## theta -56.6165 2.3649 -23.940 3.89e-12 ***
## theta:r -0.5731 0.6039 -0.949 0.360
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 236.9 on 13 degrees of freedom
## Multiple R-squared: 0.9942, Adjusted R-squared: 0.9924
## F-statistic: 556.8 on 4 and 13 DF, p-value: 2.172e-14
## [1] "No Mask 0.5"
## [1] -50000 0 8000 16000 24000 32000 40000 48000 56000 64000
## [11] 72000 80000 88000 96000 104000 112000 120000
##
## Call:
## lm(formula = background_subtracted ~ poly(r, degree = 2) + theta +
## r:theta, data = data_mask)
##
## Residuals:
## Min 1Q Median 3Q Max
## -19040 -11356 -7548 12532 38898
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 45.83 6505.63 0.007 0.994486
## poly(r, degree = 2)1 -31.66 27601.06 -0.001 0.999102
## poly(r, degree = 2)2 57075.67 19516.90 2.924 0.011840 *
## theta 1007.95 194.81 5.174 0.000179 ***
## theta:r -196.03 49.75 -3.940 0.001692 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 19520 on 13 degrees of freedom
## Multiple R-squared: 0.7989, Adjusted R-squared: 0.737
## F-statistic: 12.91 on 4 and 13 DF, p-value: 0.0001837
## [1] "Surgical 0.5"
## [1] -50000 0 8000 16000 24000 32000 40000 48000 56000 64000
## [11] 72000 80000 88000 96000 104000 112000 120000
##
## Call:
## lm(formula = background_subtracted ~ poly(r, degree = 2) + theta +
## r:theta, data = data_mask)
##
## Residuals:
## Min 1Q Median 3Q Max
## -308.22 -56.52 -22.63 45.18 666.78
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 301.2778 74.0779 4.067 0.00133 **
## poly(r, degree = 2)1 -490.7967 314.2860 -1.562 0.14238
## poly(r, degree = 2)2 89.3397 222.2338 0.402 0.69421
## theta -3.3468 2.2182 -1.509 0.15528
## theta:r 0.5492 0.5665 0.970 0.34998
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 222.2 on 13 degrees of freedom
## Multiple R-squared: 0.25, Adjusted R-squared: 0.01921
## F-statistic: 1.083 on 4 and 13 DF, p-value: 0.4047
## [1] "SewnPC 0.5"
## [1] -50000 0 8000 16000 24000 32000 40000 48000 56000 64000
## [11] 72000 80000 88000 96000 104000 112000 120000
##
## Call:
## lm(formula = background_subtracted ~ poly(r, degree = 2) + theta +
## r:theta, data = data_mask)
##
## Residuals:
## Min 1Q Median 3Q Max
## -61.289 -32.554 -3.746 28.130 86.101
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 198.83333 15.70348 12.662 1.09e-08 ***
## poly(r, degree = 2)1 -57.81276 66.62422 -0.868 0.4013
## poly(r, degree = 2)2 -126.02925 47.11043 -2.675 0.0191 *
## theta 0.33119 0.47024 0.704 0.4937
## theta:r -0.07788 0.12009 -0.648 0.5280
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 47.11 on 13 degrees of freedom
## Multiple R-squared: 0.4624, Adjusted R-squared: 0.2969
## F-statistic: 2.795 on 4 and 13 DF, p-value: 0.07094
## [1] "Sewn 0.5"
## [1] -50000 0 8000 16000 24000 32000 40000 48000 56000 64000
## [11] 72000 80000 88000 96000 104000 112000 120000
##
## Call:
## lm(formula = background_subtracted ~ poly(r, degree = 2) + theta +
## r:theta, data = data_mask)
##
## Residuals:
## Min 1Q Median 3Q Max
## -858.08 -70.39 -31.46 45.65 1875.08
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 474.019 207.964 2.279 0.0402 *
## poly(r, degree = 2)1 -1190.413 882.316 -1.349 0.2003
## poly(r, degree = 2)2 329.411 623.892 0.528 0.6064
## theta -7.664 6.227 -1.231 0.2402
## theta:r 1.483 1.590 0.933 0.3679
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 623.9 on 13 degrees of freedom
## Multiple R-squared: 0.1768, Adjusted R-squared: -0.07646
## F-statistic: 0.6981 on 4 and 13 DF, p-value: 0.6068
## [1] "N95L 0.5"
## [1] -50000 0 8000 16000 24000 32000 40000 48000 56000 64000
## [11] 72000 80000 88000 96000 104000 112000 120000
##
## Call:
## lm(formula = background_subtracted ~ poly(r, degree = 2) + theta +
## r:theta, data = data_mask)
##
## Residuals:
## Min 1Q Median 3Q Max
## -29.54 -14.81 -11.17 16.16 40.21
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 235.94444 8.31259 28.384 4.42e-13 ***
## poly(r, degree = 2)1 27.45342 35.26733 0.778 0.45025
## poly(r, degree = 2)2 -75.69702 24.93777 -3.035 0.00956 **
## theta 0.02729 0.24892 0.110 0.91437
## theta:r -0.06930 0.06357 -1.090 0.29544
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 24.94 on 13 degrees of freedom
## Multiple R-squared: 0.4968, Adjusted R-squared: 0.342
## F-statistic: 3.209 on 4 and 13 DF, p-value: 0.04871
## [1] "N95T 0.5"
## [1] -50000 0 8000 16000 24000 32000 40000 48000 56000 64000
## [11] 72000 80000 88000 96000 104000 112000 120000
##
## Call:
## lm(formula = background_subtracted ~ poly(r, degree = 2) + theta +
## r:theta, data = data_mask)
##
## Residuals:
## Min 1Q Median 3Q Max
## -19.7164 -7.9890 0.8728 6.2025 16.7632
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 16.148148 3.960248 4.078 0.00131 **
## poly(r, degree = 2)1 -43.690952 16.801907 -2.600 0.02199 *
## poly(r, degree = 2)2 -19.823834 11.880743 -1.669 0.11909
## theta 0.211176 0.118588 1.781 0.09832 .
## theta:r 0.009487 0.030285 0.313 0.75906
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 11.88 on 13 degrees of freedom
## Multiple R-squared: 0.6936, Adjusted R-squared: 0.5993
## F-statistic: 7.356 on 4 and 13 DF, p-value: 0.002525
## [1] "SewnF 0.5"
## [1] -50000 0 8000 16000 24000 32000 40000 48000 56000 64000
## [11] 72000 80000 88000 96000 104000 112000 120000
##
## Call:
## lm(formula = background_subtracted ~ poly(r, degree = 2) + theta +
## r:theta, data = data_mask)
##
## Residuals:
## Min 1Q Median 3Q Max
## -9.1667 -0.7500 -0.2083 1.2500 5.8333
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 191.83333 1.56961 122.217 < 2e-16 ***
## poly(r, degree = 2)1 65.38348 6.65929 9.818 2.22e-07 ***
## poly(r, degree = 2)2 3.77492 4.70883 0.802 0.437
## theta -1.85370 0.04700 -39.439 6.43e-15 ***
## theta:r -0.08333 0.01200 -6.943 1.02e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 4.709 on 13 degrees of freedom
## Multiple R-squared: 0.9983, Adjusted R-squared: 0.9978
## F-statistic: 1891 on 4 and 13 DF, p-value: < 2.2e-16
## [1] "No Mask 1"
## [1] -50000 0 2000 4000 6000 8000 10000 12000 14000 16000
## [11] 18000 20000 22000 24000 26000 28000 30000
##
## Call:
## lm(formula = background_subtracted ~ poly(r, degree = 2) + theta +
## r:theta, data = data_mask)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5693.3 -3414.0 -975.4 3834.4 5689.6
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.074 1628.420 0.001 0.999484
## poly(r, degree = 2)1 -3.358 6908.799 0.000 0.999620
## poly(r, degree = 2)2 17184.593 4885.259 3.518 0.003784 **
## theta 303.486 48.763 6.224 3.1e-05 ***
## theta:r -59.005 12.453 -4.738 0.000388 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 4885 on 13 degrees of freedom
## Multiple R-squared: 0.8518, Adjusted R-squared: 0.8062
## F-statistic: 18.68 on 4 and 13 DF, p-value: 2.669e-05
## [1] "Surgical 1"
## [1] -50000 0 2000 4000 6000 8000 10000 12000 14000 16000
## [11] 18000 20000 22000 24000 26000 28000 30000
##
## Call:
## lm(formula = background_subtracted ~ poly(r, degree = 2) + theta +
## r:theta, data = data_mask)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1098.24 -222.67 -97.40 98.69 2634.17
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 415.139 289.829 1.432 0.176
## poly(r, degree = 2)1 -1995.917 1229.641 -1.623 0.129
## poly(r, degree = 2)2 749.420 869.488 0.862 0.404
## theta -12.913 8.679 -1.488 0.161
## theta:r 2.511 2.216 1.133 0.278
##
## Residual standard error: 869.5 on 13 degrees of freedom
## Multiple R-squared: 0.2517, Adjusted R-squared: 0.02151
## F-statistic: 1.093 on 4 and 13 DF, p-value: 0.4002
## [1] "SewnPC 1"
## [1] -50000 0 2000 4000 6000 8000 10000 12000 14000 16000
## [11] 18000 20000 22000 24000 26000 28000 30000
##
## Call:
## lm(formula = background_subtracted ~ poly(r, degree = 2) + theta +
## r:theta, data = data_mask)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.4686 -1.5033 -0.6681 0.2794 9.9481
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.537037 1.099135 0.489 0.633
## poly(r, degree = 2)1 -2.109350 4.663233 -0.452 0.658
## poly(r, degree = 2)2 -3.178878 3.297404 -0.964 0.353
## theta 0.004435 0.032913 0.135 0.895
## theta:r 0.001478 0.008405 0.176 0.863
##
## Residual standard error: 3.297 on 13 degrees of freedom
## Multiple R-squared: 0.1015, Adjusted R-squared: -0.1749
## F-statistic: 0.3673 on 4 and 13 DF, p-value: 0.8276
## [1] "Sewn 1"
## [1] -50000 0 2000 4000 6000 8000 10000 12000 14000 16000
## [11] 18000 20000 22000 24000 26000 28000 30000
##
## Call:
## lm(formula = background_subtracted ~ poly(r, degree = 2) + theta +
## r:theta, data = data_mask)
##
## Residuals:
## Min 1Q Median 3Q Max
## -589.61 -109.18 -53.78 53.20 1400.80
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 225.565 154.123 1.464 0.167
## poly(r, degree = 2)1 -1071.397 653.890 -1.638 0.125
## poly(r, degree = 2)2 389.004 462.370 0.841 0.415
## theta -7.050 4.615 -1.528 0.151
## theta:r 1.375 1.179 1.166 0.264
##
## Residual standard error: 462.4 on 13 degrees of freedom
## Multiple R-squared: 0.2542, Adjusted R-squared: 0.02473
## F-statistic: 1.108 on 4 and 13 DF, p-value: 0.3941
## [1] "N95L 1"
## [1] -50000 0 2000 4000 6000 8000 10000 12000 14000 16000
## [11] 18000 20000 22000 24000 26000 28000 30000
##
## Call:
## lm(formula = background_subtracted ~ poly(r, degree = 2) + theta +
## r:theta, data = data_mask)
##
## Residuals:
## Min 1Q Median 3Q Max
## -11.102 -3.509 1.236 2.105 13.314
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 7.388889 2.219582 3.329 0.00544 **
## poly(r, degree = 2)1 -5.353034 9.416889 -0.568 0.57942
## poly(r, degree = 2)2 -10.596259 6.658746 -1.591 0.13555
## theta -0.104841 0.066465 -1.577 0.13872
## theta:r 0.006823 0.016974 0.402 0.69425
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 6.659 on 13 degrees of freedom
## Multiple R-squared: 0.3924, Adjusted R-squared: 0.2055
## F-statistic: 2.099 on 4 and 13 DF, p-value: 0.1392
## [1] "N95T 1"
## [1] -50000 0 2000 4000 6000 8000 10000 12000 14000 16000
## [11] 18000 20000 22000 24000 26000 28000 30000
##
## Call:
## lm(formula = background_subtracted ~ poly(r, degree = 2) + theta +
## r:theta, data = data_mask)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.956 -1.621 -1.026 0.932 11.044
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.777778 1.219561 1.458 0.169
## poly(r, degree = 2)1 0.152944 5.174160 0.030 0.977
## poly(r, degree = 2)2 -3.576237 3.658683 -0.977 0.346
## theta -0.027290 0.036519 -0.747 0.468
## theta:r 0.004483 0.009326 0.481 0.639
##
## Residual standard error: 3.659 on 13 degrees of freedom
## Multiple R-squared: 0.126, Adjusted R-squared: -0.1429
## F-statistic: 0.4687 on 4 and 13 DF, p-value: 0.7579
## [1] "SewnF 1"
## [1] -50000 0 2000 4000 6000 8000 10000 12000 14000 16000
## [11] 18000 20000 22000 24000 26000 28000 30000
##
## Call:
## lm(formula = background_subtracted ~ poly(r, degree = 2) + theta +
## r:theta, data = data_mask)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.1751 -1.0508 -0.4722 0.9638 12.2416
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.138889 1.360903 1.572 0.140
## poly(r, degree = 2)1 -8.201612 5.773821 -1.420 0.179
## poly(r, degree = 2)2 4.232985 4.082708 1.037 0.319
## theta -0.052956 0.040752 -1.299 0.216
## theta:r 0.009868 0.010407 0.948 0.360
##
## Residual standard error: 4.083 on 13 degrees of freedom
## Multiple R-squared: 0.2344, Adjusted R-squared: -0.001227
## F-statistic: 0.9948 on 4 and 13 DF, p-value: 0.4448
## [1] "No Mask 3"
## [1] -50000 0 800 1600 2400 3200 4000 4800 5600 6400
## [11] 7200 8000 8800 9600 10400 11200 12000
##
## Call:
## lm(formula = background_subtracted ~ poly(r, degree = 2) + theta +
## r:theta, data = data_mask)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2260.0 -1061.2 -706.5 962.8 4392.0
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.704 657.452 0.003 0.997972
## poly(r, degree = 2)1 1.504 2789.331 0.001 0.999578
## poly(r, degree = 2)2 5334.907 1972.355 2.705 0.018027 *
## theta 94.165 19.687 4.783 0.000358 ***
## theta:r -18.308 5.028 -3.641 0.002986 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1972 on 13 degrees of freedom
## Multiple R-squared: 0.7724, Adjusted R-squared: 0.7024
## F-statistic: 11.03 on 4 and 13 DF, p-value: 0.0003992
## [1] "Surgical 3"
## [1] -50000 0 800 1600 2400 3200 4000 4800 5600 6400
## [11] 7200 8000 8800 9600 10400 11200 12000
##
## Call:
## lm(formula = background_subtracted ~ poly(r, degree = 2) + theta +
## r:theta, data = data_mask)
##
## Residuals:
## Min 1Q Median 3Q Max
## -732.72 -145.00 -65.89 66.67 1752.61
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 277.741 192.827 1.440 0.173
## poly(r, degree = 2)1 -1328.445 818.095 -1.624 0.128
## poly(r, degree = 2)2 500.408 578.480 0.865 0.403
## theta -8.664 5.774 -1.501 0.157
## theta:r 1.688 1.475 1.144 0.273
##
## Residual standard error: 578.5 on 13 degrees of freedom
## Multiple R-squared: 0.2525, Adjusted R-squared: 0.02248
## F-statistic: 1.098 on 4 and 13 DF, p-value: 0.3984
## [1] "SewnPC 3"
## [1] -50000 0 800 1600 2400 3200 4000 4800 5600 6400
## [11] 7200 8000 8800 9600 10400 11200 12000
##
## Call:
## lm(formula = background_subtracted ~ poly(r, degree = 2) + theta +
## r:theta, data = data_mask)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.5760 -1.6151 -0.8582 0.6758 4.8933
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.1481481 0.8985664 2.391 0.0327 *
## poly(r, degree = 2)1 1.5804195 3.8122944 0.415 0.6852
## poly(r, degree = 2)2 -0.4194352 2.6956992 -0.156 0.8787
## theta -0.0059129 0.0269073 -0.220 0.8295
## theta:r -0.0009422 0.0068715 -0.137 0.8930
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2.696 on 13 degrees of freedom
## Multiple R-squared: 0.04803, Adjusted R-squared: -0.2449
## F-statistic: 0.164 on 4 and 13 DF, p-value: 0.9529
## [1] "Sewn 3"
## [1] -50000 0 800 1600 2400 3200 4000 4800 5600 6400
## [11] 7200 8000 8800 9600 10400 11200 12000
##
## Call:
## lm(formula = background_subtracted ~ poly(r, degree = 2) + theta +
## r:theta, data = data_mask)
##
## Residuals:
## Min 1Q Median 3Q Max
## -372.36 -73.18 -34.87 33.98 895.64
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 142.2593 98.5411 1.444 0.173
## poly(r, degree = 2)1 -679.7078 418.0746 -1.626 0.128
## poly(r, degree = 2)2 249.1887 295.6234 0.843 0.415
## theta -4.4507 2.9508 -1.508 0.155
## theta:r 0.8712 0.7536 1.156 0.268
##
## Residual standard error: 295.6 on 13 degrees of freedom
## Multiple R-squared: 0.2508, Adjusted R-squared: 0.02028
## F-statistic: 1.088 on 4 and 13 DF, p-value: 0.4026
## [1] "N95L 3"
## [1] -50000 0 800 1600 2400 3200 4000 4800 5600 6400
## [11] 7200 8000 8800 9600 10400 11200 12000
##
## Call:
## lm(formula = background_subtracted ~ poly(r, degree = 2) + theta +
## r:theta, data = data_mask)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.4430 -1.2116 -0.0015 1.0888 6.8904
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 5.74074 1.11409 5.153 0.000186 ***
## poly(r, degree = 2)1 -7.85112 4.72670 -1.661 0.120623
## poly(r, degree = 2)2 -1.21416 3.34228 -0.363 0.722241
## theta -0.08980 0.03336 -2.692 0.018484 *
## theta:r 0.01101 0.00852 1.293 0.218608
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 3.342 on 13 degrees of freedom
## Multiple R-squared: 0.4824, Adjusted R-squared: 0.3231
## F-statistic: 3.029 on 4 and 13 DF, p-value: 0.05723
## [1] "N95T 3"
## [1] -50000 0 800 1600 2400 3200 4000 4800 5600 6400
## [11] 7200 8000 8800 9600 10400 11200 12000
##
## Call:
## lm(formula = background_subtracted ~ poly(r, degree = 2) + theta +
## r:theta, data = data_mask)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.4196 -0.7233 -0.1111 0.2259 3.2471
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.9259259 0.4016396 2.305 0.0383 *
## poly(r, degree = 2)1 0.6627566 1.7040125 0.389 0.7036
## poly(r, degree = 2)2 -1.5673633 1.2049188 -1.301 0.2159
## theta -0.0080572 0.0120270 -0.670 0.5146
## theta:r 0.0008122 0.0030714 0.264 0.7956
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.205 on 13 degrees of freedom
## Multiple R-squared: 0.1947, Adjusted R-squared: -0.05303
## F-statistic: 0.786 on 4 and 13 DF, p-value: 0.5544
## [1] "SewnF 3"
## [1] -50000 0 800 1600 2400 3200 4000 4800 5600 6400
## [11] 7200 8000 8800 9600 10400 11200 12000
##
## Call:
## lm(formula = background_subtracted ~ poly(r, degree = 2) + theta +
## r:theta, data = data_mask)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.2895 -0.6733 -0.2047 0.1981 2.5292
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.740741 0.370857 4.694 0.00042 ***
## poly(r, degree = 2)1 4.154974 1.573415 2.641 0.02037 *
## poly(r, degree = 2)2 0.883022 1.112572 0.794 0.44163
## theta -0.000130 0.011105 -0.012 0.99084
## theta:r -0.005393 0.002836 -1.902 0.07961 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.113 on 13 degrees of freedom
## Multiple R-squared: 0.5704, Adjusted R-squared: 0.4382
## F-statistic: 4.315 on 4 and 13 DF, p-value: 0.0194
## [1] "No Mask 5"
## [1] -50000 0 600 1200 1800 2400 3000 3600 4200 4800
## [11] 5400 6000 6600 7200 7800 8400 9000 9600 10200 10800
##
## Call:
## lm(formula = background_subtracted ~ poly(r, degree = 2) + theta +
## r:theta, data = data_mask)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2565.2 -907.2 -602.8 798.3 4417.8
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.731 621.142 0.003 0.997818
## poly(r, degree = 2)1 -1.153 2635.283 0.000 0.999657
## poly(r, degree = 2)2 4556.038 1863.426 2.445 0.029490 *
## theta 80.430 18.600 4.324 0.000825 ***
## theta:r -15.641 4.750 -3.293 0.005827 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1863 on 13 degrees of freedom
## Multiple R-squared: 0.7351, Adjusted R-squared: 0.6536
## F-statistic: 9.018 on 4 and 13 DF, p-value: 0.001028
## [1] "Surgical 5"
## [1] -50000 0 600 1200 1800 2400 3000 3600 4200 4800
## [11] 5400 6000 6600 7200 7800 8400 9000 9600 10200 10800
##
## Call:
## lm(formula = background_subtracted ~ poly(r, degree = 2) + theta +
## r:theta, data = data_mask)
##
## Residuals:
## Min 1Q Median 3Q Max
## -510.77 -103.52 -45.76 47.81 1227.32
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 193.824 135.031 1.435 0.175
## poly(r, degree = 2)1 -927.273 572.888 -1.619 0.130
## poly(r, degree = 2)2 346.067 405.093 0.854 0.408
## theta -6.032 4.043 -1.492 0.160
## theta:r 1.172 1.033 1.135 0.277
##
## Residual standard error: 405.1 on 13 degrees of freedom
## Multiple R-squared: 0.2509, Adjusted R-squared: 0.02045
## F-statistic: 1.089 on 4 and 13 DF, p-value: 0.4023
## [1] "SewnPC 5"
## [1] -50000 0 600 1200 1800 2400 3000 3600 4200 4800
## [11] 5400 6000 6600 7200 7800 8400 9000 9600 10200 10800
##
## Call:
## lm(formula = background_subtracted ~ poly(r, degree = 2) + theta +
## r:theta, data = data_mask)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.3439 -1.1343 -0.5154 0.8214 4.8286
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.6111111 0.7229588 2.228 0.0441 *
## poly(r, degree = 2)1 -0.2867697 3.0672544 -0.093 0.9269
## poly(r, degree = 2)2 -2.1799595 2.1688764 -1.005 0.3332
## theta -0.0048246 0.0216488 -0.223 0.8271
## theta:r -0.0005279 0.0055286 -0.095 0.9254
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2.169 on 13 degrees of freedom
## Multiple R-squared: 0.09767, Adjusted R-squared: -0.18
## F-statistic: 0.3518 on 4 and 13 DF, p-value: 0.8382
## [1] "Sewn 5"
## [1] -50000 0 600 1200 1800 2400 3000 3600 4200 4800
## [11] 5400 6000 6600 7200 7800 8400 9000 9600 10200 10800
##
## Call:
## lm(formula = background_subtracted ~ poly(r, degree = 2) + theta +
## r:theta, data = data_mask)
##
## Residuals:
## Min 1Q Median 3Q Max
## -350.11 -71.33 -32.83 32.35 835.89
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 132.7315 91.9660 1.443 0.173
## poly(r, degree = 2)1 -636.9027 390.1788 -1.632 0.127
## poly(r, degree = 2)2 236.5118 275.8981 0.857 0.407
## theta -4.1535 2.7539 -1.508 0.155
## theta:r 0.8101 0.7033 1.152 0.270
##
## Residual standard error: 275.9 on 13 degrees of freedom
## Multiple R-squared: 0.2533, Adjusted R-squared: 0.02358
## F-statistic: 1.103 on 4 and 13 DF, p-value: 0.3963
## [1] "N95L 5"
## [1] -50000 0 600 1200 1800 2400 3000 3600 4200 4800
## [11] 5400 6000 6600 7200 7800 8400 9000 9600 10200 10800
##
## Call:
## lm(formula = background_subtracted ~ poly(r, degree = 2) + theta +
## r:theta, data = data_mask)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.9605 -1.0731 0.1754 0.9605 4.0395
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.185185 0.733031 4.345 0.000794 ***
## poly(r, degree = 2)1 -4.027521 3.109988 -1.295 0.217838
## poly(r, degree = 2)2 -1.103777 2.199094 -0.502 0.624114
## theta -0.034064 0.021950 -1.552 0.144689
## theta:r 0.001608 0.005606 0.287 0.778720
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2.199 on 13 degrees of freedom
## Multiple R-squared: 0.4072, Adjusted R-squared: 0.2248
## F-statistic: 2.232 on 4 and 13 DF, p-value: 0.1218
## [1] "N95T 5"
## [1] -50000 0 600 1200 1800 2400 3000 3600 4200 4800
## [11] 5400 6000 6600 7200 7800 8400 9000 9600 10200 10800
##
## Call:
## lm(formula = background_subtracted ~ poly(r, degree = 2) + theta +
## r:theta, data = data_mask)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.4357 -0.8165 -0.1535 0.5874 3.5643
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.703704 0.556443 3.062 0.00909 **
## poly(r, degree = 2)1 7.086397 2.360789 3.002 0.01021 *
## poly(r, degree = 2)2 1.743968 1.669330 1.045 0.31519
## theta 0.019818 0.016663 1.189 0.25556
## theta:r -0.007180 0.004255 -1.687 0.11537
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.669 on 13 degrees of freedom
## Multiple R-squared: 0.4515, Adjusted R-squared: 0.2828
## F-statistic: 2.675 on 4 and 13 DF, p-value: 0.07934
## [1] "SewnF 5"
## [1] -50000 0 600 1200 1800 2400 3000 3600 4200 4800
## [11] 5400 6000 6600 7200 7800 8400 9000 9600 10200 10800
##
## Call:
## lm(formula = background_subtracted ~ poly(r, degree = 2) + theta +
## r:theta, data = data_mask)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.1652 -0.8911 -0.2102 0.4203 3.8348
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.620370 0.539146 3.005 0.0101 *
## poly(r, degree = 2)1 1.503948 2.287403 0.657 0.5223
## poly(r, degree = 2)2 1.269344 1.617438 0.785 0.4466
## theta -0.011615 0.016145 -0.719 0.4846
## theta:r -0.001917 0.004123 -0.465 0.6497
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.617 on 13 degrees of freedom
## Multiple R-squared: 0.2997, Adjusted R-squared: 0.08427
## F-statistic: 1.391 on 4 and 13 DF, p-value: 0.2909
## [1] "No Mask 10"
## [1] -50000 0 200 400 600 800 1000 1200 1400 1600
## [11] 1800 2000 2200
##
## Call:
## lm(formula = background_subtracted ~ poly(r, degree = 2) + theta +
## r:theta, data = data_mask)
##
## Residuals:
## Min 1Q Median 3Q Max
## -616.3 -167.2 -111.4 112.0 1078.7
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.05556 136.13568 0.000 0.99968
## poly(r, degree = 2)1 -0.26765 577.57477 0.000 0.99964
## poly(r, degree = 2)2 841.11116 408.40703 2.059 0.06006 .
## theta 14.84717 4.07654 3.642 0.00298 **
## theta:r -2.88582 1.04106 -2.772 0.01586 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 408.4 on 13 degrees of freedom
## Multiple R-squared: 0.6631, Adjusted R-squared: 0.5594
## F-statistic: 6.396 on 4 and 13 DF, p-value: 0.004508
## [1] "Surgical 10"
## [1] -50000 0 200 400 600 800 1000 1200 1400 1600
## [11] 1800 2000 2200
##
## Call:
## lm(formula = background_subtracted ~ poly(r, degree = 2) + theta +
## r:theta, data = data_mask)
##
## Residuals:
## Min 1Q Median 3Q Max
## -70.743 -14.677 -7.004 7.504 170.757
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 27.3333 18.7914 1.455 0.170
## poly(r, degree = 2)1 -127.5551 79.7253 -1.600 0.134
## poly(r, degree = 2)2 46.6567 56.3743 0.828 0.423
## theta -0.8432 0.5627 -1.498 0.158
## theta:r 0.1674 0.1437 1.165 0.265
##
## Residual standard error: 56.37 on 13 degrees of freedom
## Multiple R-squared: 0.2433, Adjusted R-squared: 0.01047
## F-statistic: 1.045 on 4 and 13 DF, p-value: 0.4216
## [1] "SewnPC 10"
## [1] -50000 0 200 400 600 800 1000 1200 1400 1600
## [11] 1800 2000 2200
##
## Call:
## lm(formula = background_subtracted ~ poly(r, degree = 2) + theta +
## r:theta, data = data_mask)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.55044 -0.43640 -0.17982 0.05099 1.94956
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.778e-01 2.418e-01 1.149 0.271
## poly(r, degree = 2)1 -7.647e-01 1.026e+00 -0.745 0.469
## poly(r, degree = 2)2 -4.305e-01 7.255e-01 -0.593 0.563
## theta 9.747e-05 7.242e-03 0.013 0.989
## theta:r 3.411e-04 1.849e-03 0.184 0.857
##
## Residual standard error: 0.7255 on 13 degrees of freedom
## Multiple R-squared: 0.08757, Adjusted R-squared: -0.1932
## F-statistic: 0.3119 on 4 and 13 DF, p-value: 0.8649
## [1] "Sewn 10"
## [1] -50000 0 200 400 600 800 1000 1200 1400 1600
## [11] 1800 2000 2200
##
## Call:
## lm(formula = background_subtracted ~ poly(r, degree = 2) + theta +
## r:theta, data = data_mask)
##
## Residuals:
## Min 1Q Median 3Q Max
## -80.996 -15.840 -7.947 8.281 191.504
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 31.5000 21.0724 1.495 0.159
## poly(r, degree = 2)1 -144.0731 89.4028 -1.612 0.131
## poly(r, degree = 2)2 55.0343 63.2173 0.871 0.400
## theta -0.9559 0.6310 -1.515 0.154
## theta:r 0.1838 0.1611 1.141 0.275
##
## Residual standard error: 63.22 on 13 degrees of freedom
## Multiple R-squared: 0.2541, Adjusted R-squared: 0.02462
## F-statistic: 1.107 on 4 and 13 DF, p-value: 0.3943
## [1] "N95L 10"
## [1] -50000 0 200 400 600 800 1000 1200 1400 1600
## [11] 1800 2000 2200
##
## Call:
## lm(formula = background_subtracted ~ poly(r, degree = 2) + theta +
## r:theta, data = data_mask)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.5833 -0.9375 -0.2500 0.7500 2.5000
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.611111 0.456825 3.527 0.00372 **
## poly(r, degree = 2)1 -0.191180 1.938146 -0.099 0.92293
## poly(r, degree = 2)2 -0.397360 1.370476 -0.290 0.77643
## theta -0.016667 0.013680 -1.218 0.24473
## theta:r 0.001852 0.003493 0.530 0.60498
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.37 on 13 degrees of freedom
## Multiple R-squared: 0.1696, Adjusted R-squared: -0.08593
## F-statistic: 0.6637 on 4 and 13 DF, p-value: 0.6282
## [1] "N95T 10"
## [1] -50000 0 200 400 600 800 1000 1200 1400 1600
## [11] 1800 2000 2200
##
## Call:
## lm(formula = background_subtracted ~ poly(r, degree = 2) + theta +
## r:theta, data = data_mask)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.39474 -0.15351 -0.09211 0.08443 2.15351
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.7777778 0.3049671 2.550 0.0242 *
## poly(r, degree = 2)1 2.2176854 1.2938659 1.714 0.1103
## poly(r, degree = 2)2 -0.4635863 0.9149014 -0.507 0.6208
## theta 0.0007797 0.0091322 0.085 0.9333
## theta:r -0.0028265 0.0023321 -1.212 0.2471
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9149 on 13 degrees of freedom
## Multiple R-squared: 0.3315, Adjusted R-squared: 0.1258
## F-statistic: 1.612 on 4 and 13 DF, p-value: 0.2302
## [1] "SewnF 10"
## [1] -50000 0 200 400 600 800 1000 1200 1400 1600
## [11] 1800 2000 2200
##
## Call:
## lm(formula = background_subtracted ~ poly(r, degree = 2) + theta +
## r:theta, data = data_mask)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.3903 -1.0161 -0.1857 0.8637 3.1096
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.222222 0.498406 2.452 0.0291 *
## poly(r, degree = 2)1 -0.152944 2.114559 -0.072 0.9434
## poly(r, degree = 2)2 0.640191 1.495219 0.428 0.6755
## theta -0.017154 0.014925 -1.149 0.2711
## theta:r 0.002924 0.003811 0.767 0.4567
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.495 on 13 degrees of freedom
## Multiple R-squared: 0.1396, Adjusted R-squared: -0.1252
## F-statistic: 0.5271 on 4 and 13 DF, p-value: 0.7179