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newdata_ Rm1.R Environment History Connections Tutorial
8 Run Source Import Dataset 487 MiB List
R Global Environment
9describe(data= AcademicStress_SleepQuality,
Data
10 mean_sleep1 = mean(Sleep1), model List of 12
11 mean_sleep2 = mean(Sleep2), newdata_ 37 obs. of 20 variables
12 mean_sleep3 = mean(Sleep3),
13 mean_sleep4 = mean(Sleep4),
14 mean_sleep5 = mean(Sleep5))
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describe(data= AcademicStress_SleepQuality,
16 mean_sleep6 = mean(Sleep6),
17 mean_sleep7 = mean(Sleep7),
18 mean_sleep8 = mean(Sleep8),
19 mean_sleep9 = mean(Sleep9),
20 mean_sleep10 = mean(Sleep10))
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# paired t test
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scatterplot(AcademicStress_SleepQuality,
24 x= )
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glimpse(AcademicStress_SleepQuality)
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SQ <- select(AcademicStress_SleepQuality, Sleep1, Sleep2,Sleep3,Sleep4,Sleep5,Sleep6,Sleep7,Sleep8,Sleep9,Sleep10)
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View(SQ)
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AS <- select(AcademicStress_SleepQuality, Stress1,Stress2,Stress3,Stress4,Stress5,Stress6,Stress7,Stress8)
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View(AS)
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scatterplot(data= SQ,
38 x= Sleep2,
39 Y= Sleep1,
40 best_fit_line = TRUE)
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# descriptive stats -> mean, SD
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describe(data = newdata_,
48 mean_sleep = mean(SleepQualityAverage),
49 sd_sleep = sd(SleepQualityAverage),
50 mean_stress = mean(AcademicStressAverage),
51 sd_stress = sd(AcademicStressAverage))
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scatterplot(data= newdata_,
54 y= SleepQualityAverage,
55 x= AcademicStressAverage,
56 best_fit_line = TRUE,
57 ylab = "SleepQualityAverage",
58 xlab = "AcademicStressAverage")
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model <- lm(SleepQualityAverage ~AcademicStressAverage, data = newdata_)
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summary(model)
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38:23 (Top Level) R Script
Console
R 4.3.2Terminal Background Jobs
· /cloud/project/
R 4.3.2 · /cloud/project/
> describe(data = newdata_,
+ mean_sleep = mean(SleepQualityAverage),
+ sd_sleep = sd(SleepQualityAverage),
+ mean_stress = mean(AcademicStressAverage),
+ sd_stress = sd(AcademicStressAverage))
# A tibble: 1 × 4
mean_sleep sd_sleep mean_stress sd_stress
<dbl> <dbl> <dbl> <dbl>
1 1.22 0.652 2.69 0.636
> scatterplot(data= newdata_,
+ y= SleepQualityAverage,
+ x= AcademicStressAverage,
+ best_fit_line = TRUE,
+ ylab = "SleepQualityAverage",
+ xlab = "AcademicStressAverage")
> model <- lm(SleepQualityAverage ~AcademicStressAverage, data = n
ewdata_)
> summary(model)
Call:
lm(formula = SleepQualityAverage ~ AcademicStressAverage, data = n
ewdata_)
Residuals:
Min 1Q Median 3Q Max
-1.0201 -0.4002 -0.1427 0.3473 1.5798
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 2.3551 0.4364 5.396 4.83e-06
AcademicStressAverage -0.4199 0.1579 -2.660 0.0117
(Intercept) ***
AcademicStressAverage *
---
Signif. codes:
0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.6028 on 35 degrees of freedom
Multiple R-squared: 0.1682, Adjusted R-squared: 0.1444
F-statistic: 7.077 on 1 and 35 DF, p-value: 0.0117
