This is the English-language version of my Business Statistics training course.
- Introduction to Business Statistics: why do we need all this?
Describing and displaying categorical variables: capturing the essence.
- Describing and displaying quantitative variables.
- Correlation and linear regression: relationships in data.
- Randomness and probability: how can we describe what we don’t know?
- Random variables and probability models: taming the beast of uncertainty.
- The Normal distribution: the law that describes many different variables in business.
- Statistical sampling and surveys.
- Confidence intervals: narrowing the search area.
- Testing statistical hypotheses: is there an effect?
Comparing two groups: how different are two groups, and are they different at all?
- Multiple regression.
- Time series analysis: where will it go from here?
- Decision-making and risk: what should we do under unpredictable conditions?
Statistics in project management.
- Experimental studies.
Quality control: don’t miss an alarm.
Data. Variable types. Time series. Cross-sectional data. Population and its parameters. Summarizing a categorical variable. Displaying a categorical variable. Contingency table
Displaying a quantitative variable. Five-number summary of a quantitative variable. Outliers. Standardizing a quantitative variable.
Scatterplot. Correlation. Causation and correlation. The linear regression model. Checking the model.
Random events and probability. Properties of probability. Joint probability. Conditional probability.
Expected value and standard deviation of a random variable. Discrete probability distributions: the Binomial distribution, the Geometric distribution, the Poisson distribution.
The concept of the Normal distribution. Applying the Normal distribution to data analysis. The Central Limit Theorem.
Principles of unbiased sampling. Sampling designs. Common mistakes in sampling
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.
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.
The multiple regression model: how can we understand the contribution of each factor?
Interpreting multiple regression coefficients. Prerequisites for using the multiple regression model.
Components of a time series. Smoothing methods. Autoregressive models. Forecasting time series.
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.
Principles of experimental design. Analysis of variance (ANOVA). Run chart. Probabilistic model of quality control. Approaches to quality control.