READy analysis

This uses HTML as output format. See the Quarto documentation for instructions on how to use other formats.

1 Methods

The dataset was loaded from the processed-data folder using the here package. Linear regression models were fit using the lm() function. The first one is a linear model with height as the outcome and weight as the predictor. The secone one is a multiple linear model with height as the outcome and weight and gender as predictors. The third one is a few linear models with height as the outcome and study time and exercise time as predictors. Model results were cleaned up using the broom package and saved as RDS files in the results/tables folder. Figures generated during analysis were saved to the results/figures folder.

2 Results

library(here)

# Load saved tables
result1 <- readRDS(here("results", "tables", "resulttable1.rds"))
result2 <- readRDS(here("results", "tables", "resulttable2.rds"))
result3 <- readRDS(here("results", "tables", "resulttable3.rds"))

result1

         term    estimate  std.error statistic      p.value
1 (Intercept) 149.6997661 19.7518528 7.5790240 0.0001285084
2      Weight   0.2277371  0.2708841 0.8407177 0.4282859783

result2

         term    estimate  std.error  statistic     p.value
1 (Intercept) 149.2726967 23.3823360  6.3839942 0.001396202
2      Weight   0.2623972  0.3512436  0.7470519 0.488651747
3     GenderM  -2.1244913 15.5488953 -0.1366329 0.896652025
4     GenderO  -4.7644739 19.0114155 -0.2506112 0.812087104

result3

          term      estimate    std.error  statistic      p.value
1  (Intercept) 161.089286648 12.869001653 12.5176211 1.589696e-05
2 Exercisetime  -0.030542742  0.118496421 -0.2577524 8.052106e-01
3    Studytime   0.001656239  0.002921644  0.5668859 5.913515e-01

2.1 Exploratory/Descriptive analysis

Table 1: Data summary table. All caption text goes here.

skim_type	skim_variable	n_missing	complete_rate	factor.ordered	factor.n_unique	factor.top_counts	numeric.mean	numeric.sd	numeric.p0	numeric.p25	numeric.p50	numeric.p75	numeric.p100	numeric.hist
factor	Gender	0	1	FALSE	3	M: 4, F: 3, O: 2	NA	NA	NA	NA	NA	NA	NA	NA
numeric	Height	0	1	NA	NA	NA	165.66667	15.97655	133	156	166	178	183	▂▁▃▃▇
numeric	Weight	0	1	NA	NA	NA	70.11111	21.24526	45	55	70	80	110	▇▂▃▂▂

Figure 1: Height and weight stratified by gender.

2.2 Full analysis

Table 2: Linear model fit table.

term	estimate	std.error	statistic	p.value
(Intercept)	149.2726967	23.3823360	6.3839942	0.0013962
Weight	0.2623972	0.3512436	0.7470519	0.4886517
GenderM	-2.1244913	15.5488953	-0.1366329	0.8966520
GenderO	-4.7644739	19.0114155	-0.2506112	0.8120871