Math 11 Chapter 1 Flashcards

Statistics

• The science of conducting studies to collect, organize, summarize, analyze, and draw conclusions from data.
• Distinguishes between inferential statistics (making predictions) and descriptive statistics (summarizing data).

Population

• Represents all subjects under study, such as US citizens or students from Reedley College.

Census

• The collection of data from every member of the population, ensuring complete coverage.

Sample

• A subgroup of the population, exemplified by CA residents or females at Reedley College.

Statistic

• A computed number derived from sample data, denoted with symbols like X for sample mean and S for sample standard deviation.

Parameter

• A number characterizing the population, such as M for population mean and σ for population standard deviation.

Qualitative Data

• Categorical data that identifies group membership or characteristics, like hair color, eye color, or race.

Quantitative Data

• Numerical data suitable for arithmetic operations, representing measurable quantities like height, weight, or age.

Quantitative Discrete

• Results from countable values, exemplified by the number of customers visiting a Starbucks in a day.

Quantitative Continuous

• Data arising from infinitely many possible values, such as weight (e.g., 3.2 lbs, 4.97 kg) or height (e.g., 5.5 ft, 10.21 m).

Nominal Level

• Classifies data into distinct, non-overlapping categories without any intrinsic ordering, such as hair color (brown, blonde, black).

Ordinal Level

• Categorizes data that can be ranked but lacks precise differences between ranks, such as social status (upper, middle, lower).

Interval Level

• Ranks data with meaningful differences between measurements but no true zero, exemplified by temperature or historical timelines.

Ratio Level

• Contains characteristics of interval measurement with a true zero, allowing for true ratios, like weight (0 lbs, 20 lbs).

Random Sampling

• Ensures each population member has an equal chance of being selected for the sample.

Simple Random Sampling

• A sampling method where every possible sample of a specified size has the same chance of selection.

Stratified Sampling

• Involves dividing the population into strata (subgroups) and randomly selecting samples from each stratum to ensure representation (e.g., by major).

Systematic Sampling

• Selects samples based on a fixed interval after a random starting point, such as picking every 10th item on an assembly line.

Cluster Sampling

• The population is divided into clusters, and a random selection of whole clusters is sampled, including all members of those clusters.

Convenience Sampling

• Relies on easy-to-access data or participants for sample collection, like stopping every 5th driver at a checkpoint.