Business Statistics

Business statistics takes the data analysis tools from elementary statistics and applies them to business
1. Many of the tools used in business statistics are built on ones you’ve probably already come across in basic math: mean, mode and median, bar graphs and the bell curve, and basic probability. Hypothesis testing (where you test out an idea) and regression analysis (fitting data to an equation) builds on this foundation.
  1. Describing Populations and Samples
    1. Is called Descriptive Statistics the process of describing populations and samples
      1. Measure of central tendency
        where the bulk of the data lies. It includes finding the mean, mode and median.
        the formula is
        x̄ = (Σ xi) / n Population mean: μ = (Σ * X) / N. They are solved in the same way: add the items together and then divide by the number of items in the set.
      2. Measures of dispersion
        How much is your data set spread out around the mean? Is there a big difference between your highest and lowest values? Can be answered by finding the interquartile range, variance and standard deviation.
        The interquartile range is especially useful if you are more interested in where the bulk of your data lies and less interested in extreme values.
      3. Measures of Association
        Informs you about trends in data. This might show a high or low connection (“correlation”) between different factors and final cost.
        Example
        If the price of tomatoes goes up, it directly affects the price of ketchup.
  2. Probabilities and Random Variables
    1. Probability is the foundation of business statistics.
      1. the basic formula is
        P(A) = number of outcomes that give A / number of possible outcomes = r / n
      2. There are dozens of ways to figure out probabilities. It largely depends on what you want to know. For example, something happening or not happening, choosing people or items.
        In algebra, “x” or “y” can represent a number, like 3,14,or 22.5. In statistics, a random variable must be linked to a random event or experiment.
  3. Probability Distributions
    1. Discrete Distributions
      1. Includes the binomial distribution. In a binomial experiment, there are only two outcomes (such as yes / no or success / failure).
        The formula is
    2. Continuous probability distributions
      1. Can take on an infinite number of different values.

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