Viktor Smusin

Business Statistics

This is the English-language version of my Business Statistics training course.

  1. Introduction to Business Statistics: why do we need all this?
    Describing and displaying categorical variables: capturing the essence.

    2. Data. Variable types. Time series. Cross-sectional data. Population and its parameters. Summarizing a categorical variable. Displaying a categorical variable. Contingency table

  2. Describing and displaying quantitative variables.

    3. Displaying a quantitative variable. Five-number summary of a quantitative variable. Outliers. Standardizing a quantitative variable.

  3. Correlation and linear regression: relationships in data.

    4. Scatterplot. Correlation. Causation and correlation. The linear regression model. Checking the model.

  4. Randomness and probability: how can we describe what we don’t know?

    5. Random events and probability. Properties of probability. Joint probability. Conditional probability.

  5. Random variables and probability models: taming the beast of uncertainty.

    6. Expected value and standard deviation of a random variable. Discrete probability distributions: the Binomial distribution, the Geometric distribution, the Poisson distribution.

  6. The Normal distribution: the law that describes many different variables in business.

    7. The concept of the Normal distribution. Applying the Normal distribution to data analysis. The Central Limit Theorem.

  7. Statistical sampling and surveys.

    8. Principles of unbiased sampling. Sampling designs. Common mistakes in sampling

  8. Confidence intervals: narrowing the search area.

    9. The tradeoff between the precision of an estimate and the certainty that it is correct. Sampling distributions. Standard error. Confidence intervals for proportions and for means. Choosing the sample size.

  9. Testing statistical hypotheses: is there an effect?
    Comparing two groups: how different are two groups, and are they different at all?

    10. The null hypothesis and the alternative hypothesis. P-value. Significance level. Type I and Type II errors. Comparing two means, A/B testing. The two-sample t-test. Paired and unpaired data.

  10. Multiple regression.

    11. The multiple regression model: how can we understand the contribution of each factor?
    Interpreting multiple regression coefficients. Prerequisites for using the multiple regression model.

  11. Time series analysis: where will it go from here?

    12. Components of a time series. Smoothing methods. Autoregressive models. Forecasting time series.

  12. Decision-making and risk: what should we do under unpredictable conditions?
    Statistics in project management.

    13. Actions, states of nature, outcomes. Payoff table. Minimax, maximin and other criteria of decision-making. Expected value with perfect information. Expected value of perfect information. The PERT distribution. Quantifying project uncertainty. Monte Carlo method.

  13. Experimental studies.
    Quality control: don’t miss an alarm.

    14. Principles of experimental design. Analysis of variance (ANOVA). Run chart. Probabilistic model of quality control. Approaches to quality control.