setwd("~/Dropbox/Research/Johannes")
pep<-read.csv ("Pepper_Field_Study_Online_Replication.csv", header=T, sep=",")
pep$humanness<-(pep$h1_1+pep$h2_1+pep$h3_1+ (8-pep$h4R_1) + (8-pep$h5R_1) + (8-pep$h6R_1))/6
pep$instrumental<-(pep$inst1_1+pep$inst2_1+pep$inst3_1)/3
pep$tech<-(pep$tech1+pep$tech2)/2
pep$social<-(pep$soc1+pep$soc2+pep$soc3+pep$soc4+pep$soc5)/5
summary(lm(humanness ~ condition + age + gender + tech + social + check, pep))
##
## Call:
## lm(formula = humanness ~ condition + age + gender + tech + social +
## check, data = pep)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.1562 -0.5147 0.1283 0.5939 1.5249
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.709924 0.460220 8.061 1.94e-14 ***
## conditionhuman -0.021892 0.121800 -0.180 0.85748
## conditionrobot 0.106388 0.132646 0.802 0.42318
## age 0.013346 0.004432 3.012 0.00283 **
## gender 0.419439 0.094843 4.422 1.38e-05 ***
## tech 0.083309 0.065793 1.266 0.20643
## social 0.298994 0.092769 3.223 0.00141 **
## check -0.137063 0.081659 -1.678 0.09432 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.8379 on 293 degrees of freedom
## Multiple R-squared: 0.1291, Adjusted R-squared: 0.1083
## F-statistic: 6.205 on 7 and 293 DF, p-value: 8.688e-07
hist(pep$humanness)
summary(lm(instrumental ~ condition + age + gender + tech + social + check, pep))
##
## Call:
## lm(formula = instrumental ~ condition + age + gender + tech +
## social + check, data = pep)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.14689 -0.59104 0.00448 0.57247 2.50439
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.903707 0.528872 7.381 1.63e-12 ***
## conditionhuman 0.276768 0.139969 1.977 0.04894 *
## conditionrobot 0.373938 0.152433 2.453 0.01474 *
## age 0.005248 0.005093 1.031 0.30361
## gender -0.039524 0.108991 -0.363 0.71714
## tech -0.042168 0.075607 -0.558 0.57746
## social 0.293212 0.106608 2.750 0.00632 **
## check -0.161451 0.093840 -1.720 0.08640 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9629 on 293 degrees of freedom
## Multiple R-squared: 0.05491, Adjusted R-squared: 0.03234
## F-statistic: 2.432 on 7 and 293 DF, p-value: 0.01952
summary(lm(satisfied_1 ~ condition + age + gender + tech + social + check, pep))
##
## Call:
## lm(formula = satisfied_1 ~ condition + age + gender + tech +
## social + check, data = pep)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.7513 -0.3789 0.2616 0.6645 1.6346
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.433063 0.545518 8.126 1.25e-14 ***
## conditionhuman -0.140427 0.144374 -0.973 0.331525
## conditionrobot -0.402524 0.157230 -2.560 0.010966 *
## age 0.003662 0.005253 0.697 0.486321
## gender 0.402992 0.112422 3.585 0.000395 ***
## tech 0.143347 0.077987 1.838 0.067061 .
## social 0.223595 0.109963 2.033 0.042917 *
## check -0.056471 0.096794 -0.583 0.560060
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9932 on 293 degrees of freedom
## Multiple R-squared: 0.105, Adjusted R-squared: 0.0836
## F-statistic: 4.91 on 7 and 293 DF, p-value: 2.92e-05
summary(lm(recommend_1 ~ condition + age + gender + tech + social + check, pep))
##
## Call:
## lm(formula = recommend_1 ~ condition + age + gender + tech +
## social + check, data = pep)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.8716 -0.7209 0.1587 0.9510 2.2395
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.393106 0.665918 5.095 6.25e-07 ***
## conditionhuman -0.004691 0.176238 -0.027 0.97878
## conditionrobot -0.221904 0.191932 -1.156 0.24856
## age 0.005879 0.006412 0.917 0.36000
## gender 0.431650 0.137234 3.145 0.00183 **
## tech 0.310661 0.095199 3.263 0.00123 **
## social 0.174729 0.134233 1.302 0.19405
## check -0.199495 0.118157 -1.688 0.09240 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.212 on 293 degrees of freedom
## Multiple R-squared: 0.1077, Adjusted R-squared: 0.08642
## F-statistic: 5.054 on 7 and 293 DF, p-value: 1.977e-05
summary(lm(tip ~ condition + age + gender + tech + social + check, pep))
##
## Call:
## lm(formula = tip ~ condition + age + gender + tech + social +
## check, data = pep)
##
## Residuals:
## Min 1Q Median 3Q Max
## -14.671 -5.200 0.701 4.566 66.974
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.160530 4.616046 0.685 0.494
## conditionhuman 1.641558 1.218841 1.347 0.179
## conditionrobot -0.972774 1.327373 -0.733 0.464
## age 0.009177 0.044358 0.207 0.836
## gender 1.445794 0.950810 1.521 0.129
## tech 0.822888 0.660620 1.246 0.214
## social 1.180989 0.928500 1.272 0.204
## check -0.080813 0.821112 -0.098 0.922
##
## Residual standard error: 8.385 on 292 degrees of freedom
## (1 observation deleted due to missingness)
## Multiple R-squared: 0.0396, Adjusted R-squared: 0.01657
## F-statistic: 1.72 on 7 and 292 DF, p-value: 0.104