Survey data collected from 44 students enrolled in a statistics course in 2007. The dataset contains self-reported information about study habits, work hours, physical characteristics, and lifestyle behaviors. This small classroom dataset is useful for introductory data exploration, simple regression, and discussions about data quality issues (e.g., negative values for alcohol consumption, missing data).
Format
A tibble with 44 rows and 12 columns:
- actual_study
Actual hours spent studying per week. Type: numeric. Range: (0, 70). Mean = 21.5.
- desired_study
Desired hours of study per week. Type: numeric. Range: (0, 100). Mean = 21.5. NA = 1.
- actual_work
Actual hours spent working per week. Type: numeric. Range: (0, 72). Mean = 17.7.
- desired_work
Desired hours of work per week. Type: numeric. Range: (0, 50). Mean = 12.8.
- gender
Gender of student. Type: numeric. Binary indicator (0/1) where 1 = male, 0 = female. 27% male.
- height
Height in inches. Type: numeric. Range: (60, 75). Mean = 66.6 inches.
- desired_weight
Desired weight in pounds. Type: numeric. Range: (99, 200). Mean = 136.6.
- exer
Hours of exercise per week. Type: numeric. Range: (0, 25). Mean = 4.7. NA = 1.
- sleep
Hours of sleep per night. Type: numeric. Range: (6, 9). Mean = 7.0.
- alcohol
Alcoholic drinks consumed per week. Type: numeric. Range: (-1, 10). Mean = 3.4. Note: Contains a negative value (-1) which may represent a data entry error, useful for teaching data cleaning.
- overage
Whether the student is over the age threshold. Type: character. All values are "no" in this cohort.
- age
Age category. Type: numeric. Values: 0, 1, 2. Coded as age group categories rather than exact age.
Details
This dataset is used for introductory regression exercises and data exploration. Key analyses include: simple regression of actual study hours on desired study hours, exploring the relationship between height and desired weight, and identifying data quality issues such as the anomalous negative value for alcohol consumption. The small sample size (n = 44) makes it appropriate for classroom demonstrations.
Examples
data(classdata_07)
head(classdata_07)
#> # A tibble: 6 × 12
#> actual_study desired_study actual_work desired_work gender height
#> <int> <int> <int> <int> <int> <dbl>
#> 1 50 65 0 0 0 64
#> 2 40 25 10 20 0 63
#> 3 16 25 20 20 0 67
#> 4 30 30 0 0 0 67
#> 5 25 15 13 5 0 65
#> 6 20 10 10 10 0 63.5
#> # ℹ 6 more variables: desired_weight <int>, exer <dbl>, sleep <dbl>,
#> # alcohol <int>, overage <chr>, age <int>
# Simple regression: actual vs. desired study hours
lm(actual_study ~ desired_study, data = classdata_07)
#>
#> Call:
#> lm(formula = actual_study ~ desired_study, data = classdata_07)
#>
#> Coefficients:
#> (Intercept) desired_study
#> 9.6559 0.5755
#>
# Relationship between height and desired weight
lm(desired_weight ~ height + gender, data = classdata_07)
#>
#> Call:
#> lm(formula = desired_weight ~ height + gender, data = classdata_07)
#>
#> Coefficients:
#> (Intercept) height gender
#> -75.787 3.073 29.071
#>