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Biometrics Final

Chapter 1

• 1.2
• Populations- large groups about which inferences must be made.
• Parameter- descriptive measure associated with a random variable when it is considered over the entire population.
• Sample- a subset of the population used to make inferences about the entire population.
• Statistic- a descriptive measure associated with a random variable of a sample.
• 1.3
• Quantitative variables
• Continuous variables or interval data- can assume any value in some (possibly unbounded) interval of real numbers. (length, weight, temperature, volume, and height)
• Discrete variables- assume only isolated values (clutch size, trees per hectare, arms per sea star, or items per quadrant) arise from counting. Things cannot be divided into anything smaller than whole numbers no decimals.
• Ranked (ordinal) variables- not measured but nonetheless have natural ordering. (arrangement from tallest to shortest)
• Categorical Data- are qualitative data. Some examples are: species, gender, genotype, phenotype, healthy/diseased, and marital status. No natural ordering
• POPULATION PARAMETERS ARE USUALLY DENOTED BY GREEK LETTERS.

Chapter 1 (continued)

• 1.4: Measures of Central Tendency: Mean, Median, and Mode Mean
• Population Mean- the sum of the values of the variable under study divided by the total number of objects in the population. This measurement is always constant for a given population.
• DENOTED BY A LOWERCASE "mu" μ
• Sample Mean- the obervations of a subset of size n from a population that have been averaged. This is fundamentally different from population mean because samples can vary from sample to sample within the population.
• DENOTED BY X BAR x̄