TRUE & FALSE → FALSE
yield > 50 → TRUE/FALSE
Compare crop yields, filter data, and make logical decisions in R.
high_yield <- yield > 60drought <- rain < 10 & temp > 85
sample(1:100, 10)
set.seed(123)
Select representative samples from crop populations for analysis.
sample(1:1000, 20)P(event) = favorable/total
table(data) / length(data)
Calculate germination rates and pest occurrence probabilities.
germinated/total = 85/100 = 0.85rnorm(n, mean, sd)
pnorm(x, μ, σ)
Model crop yields and rainfall patterns with normal curves.
rnorm(100, 60, 8)Simulate seed germination (like coin flips) to see probability in action!
Roll dice to simulate pest counts and see distribution patterns!
See how changing the mean and spread affects the bell curve!
Enter data and click calculate to see results!
Generate a distribution to see statistics!
Use probability to predict germination rates and plan planting strategies.
P(germination) = germinated_seeds / total_seeds
Essential for planning planting density and predicting harvest yields.
Model rainfall and temperature using normal distributions for crop planning.
rainfall ~ N(μ=25, σ=8) inches/month
Critical for irrigation scheduling and crop selection decisions.
Calculate pest occurrence probabilities to optimize treatment timing.
P(pest_outbreak) = historical_outbreaks / years
Helps optimize treatment timing and reduce unnecessary pesticide use.
Use sampling to estimate crop yields and plan harvest logistics.
sample_mean ± margin_of_error
Essential for planning harvest logistics and storage capacity.
Sample products to ensure quality standards and minimize waste.
sample(population, n, replace=FALSE)
Critical for maintaining product standards and customer satisfaction.
Quantify financial risks using probability distributions for better decisions.
expected_value = Σ(outcome × probability)
Helps farmers make informed decisions about crop insurance and investments.