Statistics and Mathematical Foundation 101 — On-demand course
Build a practical understanding of how data are described, how uncertainty is represented and how statistical evidence is interpreted. Explore the concepts behind data science, supported by a smart-grid energy consumption case study. Study the recorded material at your own pace.
This course is provided by MCC Economics & Finance and focuses on statistical reasoning and the mathematical foundations of data analysis.
Course provider
Economics, finance and applied analysis
MCC Economics provides analysis and advice across economics, finance and regulation. This course focuses on statistical reasoning and the mathematical foundations of data analysis. Contact the team to confirm the named lecturer for your selected session or recording.
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Statistics and Mathematical Foundation 101 introduces data types, descriptive statistics, probability, distributions and statistical inference, with an energy-use application in smart grids. The recorded package includes a one-hour lecture video, annotated slides, a transcript and a quiz. This is recorded study, with no live meeting. Check the Eventbrite listing for current access and ticket terms.
The course topics support three learning outcomes. They describe the concepts you will work through, rather than promising mastery from a single session.
This outline organises the topics described in the course listing. The summaries explain the subject matter in plain English.
These definitions support the course topic and help you interpret the material. They are explanatory context, not a promise that every term is a separate lesson.
| Term | Meaning |
|---|---|
| Nominal and ordinal data | Nominal categories have no intrinsic order; ordinal categories have an order but not necessarily equal spacing. |
| Interval and ratio data | Interval scales have meaningful differences; ratio scales also have a meaningful zero. |
| Mean | The sum of observed values divided by the number of observations. |
| Variance and standard deviation | Measures of spread; standard deviation is expressed in the same units as the variable. |
| Probability distribution | A description of the possible values of a random variable and their probabilities or probability density. |
| Hypothesis test | A procedure for evaluating evidence against a specified null hypothesis under stated assumptions. |
| Confidence interval | An interval produced by a method with a stated long-run coverage rate. It is not a guarantee for a particular sample. |
| P-value | Under the null model, the probability of a test statistic at least as extreme as the observed one. It is not the probability that the null hypothesis is true. |
Statistics and Mathematical Foundation 101 suits learners interested in statistical reasoning and the mathematical foundations of data analysis. No formal prerequisite is specified. Comfort with basic arithmetic and reading graphs will help.
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MCC Economics brings economic and financial analysis to policy, regulation and investment questions. Statistics and Mathematical Foundation 101 offers a focused introduction to statistical reasoning and the mathematical foundations of data analysis. Explore its data science services, research publications and team profiles to see the wider context of MCC’s work.
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Statistics and Mathematical Foundation 101 introduces data types, descriptive statistics, probability, distributions and statistical inference, with an energy-use application in smart grids.
This listing is for recorded course material, with no live meeting. Follow the access instructions provided with your registration.
The recorded package includes a one-hour lecture video, annotated slides, a transcript and a quiz.
The indexed description specifies a one-hour recorded lecture. Allow additional time to review supporting material and complete the quiz.
The course is provided by MCC Economics & Finance. Contact the team to confirm the lecturer for your selected recording.
No formal prerequisite is specified. Comfort with basic arithmetic and reading graphs will help.
Descriptive statistics summarise the observed data. Inference uses a sample and a statistical model to draw conclusions about a broader population.
The listing describes a smart-grid case study involving prediction and optimisation of energy consumption.
No. A p-value describes how unusual a test statistic would be under the null model; it does not assign a probability to the hypothesis itself.
The retrievable listing does not specify a required software package or a completion certificate. Ask MCC if either matters to your training plans.
Contact MCC Economics through the consultation page to discuss team access or tailored training.
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