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Introduction to Business Statistics

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Terms in this set (20)

  • What is Business Statistics?

    Business statistics is the mathematical science concerned with the collection, analysis, interpretation, and presentation of data for business decision-making.

  • Define Population and Sample in statistics.

    Population is the entire set of items or individuals of interest. Sample is a subset of the population selected for analysis.

  • What are Variables and Data?

    Variables are characteristics that can take different values. Data are the actual values that variables assume.

  • Difference between Descriptive and Inferential Statistics.

    Descriptive statistics summarize and display data. Inferential statistics make predictions or decisions about a population based on sample data.

  • Example of Descriptive vs. Inferential Statistics.

    Descriptive: "The average exam score was 88." Inferential: "Predicting election results by asking voters about their intentions."

  • Applications of Statistics in Business.

    Used in marketing (estimating average income), operations (analyzing qualitative variables), finance (examining quantitative relationships).

  • List the steps in a Business Statistics research project.

    1. Define the problem
    2. Define population/sample
    3. Define data nature
    4. Use statistical tools
    5. Make decisions

  • What is Simple Random Sampling?

    Every member of the population has an equal chance of being selected.

  • Explain Systematic Sampling.

    Select every \(k\)-th member, where \(k=\frac{N}{n}\), N = population size, n = sample size.

  • What is Stratified Sampling?

    Population divided into strata based on important variables; random samples taken from each stratum.

  • Describe Cluster Sampling.

    Population divided into clusters; entire clusters are randomly selected for the sample.

  • What is Convenience Sampling?

    Samples selected based on ease of access; may lead to biased results.

  • Types of Data in Business Statistics.

    Qualitative (categorical) and Quantitative (numerical) data.

  • Difference between Discrete and Continuous Variables.

    Discrete: countable values. Continuous: measurable values within a range.

  • Levels of Measurement: Nominal and Ordinal.

    Nominal: categories with no ranking. Ordinal: categories with meaningful order but no measurable differences.

  • Levels of Measurement: Interval and Ratio.

    Interval: ordered with measurable differences, no true zero. Ratio: like interval but with a true zero point.

  • Primary vs. Secondary Data.

    Primary data collected directly for a purpose. Secondary data collected by others and reused.

  • Common Ethical Issues in Statistics.

    Biased samples and misleading graphs can distort findings and mislead decision-making.

  • Why is stratified sampling important?

    It helps ensure all important subgroups are represented in the sample, improving accuracy.

  • What does the systematic sampling constant \(k=\frac{N}{n}\) represent?

    It represents the interval at which members are selected from the population.