Coursera Statistics Making Sense of Data (University of Toronto)

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Coursera - Statistics: Making Sense of Data (University of Toronto)
WEBRip | English | MP4 | 960 x 540 | AVC ~80.8 kbps | 15 fps
AAC | 128 Kbps | 44.1 KHz | 2 channels | ~10 hours | 801 MB
Genre: eLearning Video / Science, Mathematics, Statistics & Probability
We live in a world where data are increasingly available, in ever larger quantities, and are increasingly expected to form the basis for decisions by governments, businesses, and other organizations, as well as by individuals in their daily lives. To cope effectively, every informed citizen must be statistically literate.

We live in a world where data are increasingly available, in ever larger quantities, and are increasingly expected to form the basis for decisions by governments, businesses, and other organizations, as well as by individuals in their daily lives. To cope effectively, every informed citizen must be statistically literate.
This course will provide an intuitive introduction to applied statistical reasoning, introducing fundamental statistical skills and acquainting students with the full process of inquiry and evaluation used in investigations in a wide range of fields. In particular, the course will cover methods of data collection, constructing effective graphical and numerical displays to understand the data, how to estimate and describe the error in estimates of some important quantities, and the key ideas in how statistical tests can be used to separate significant differences from those that are only a reflection of the natural variability in data.
Syllabus
A first look at data
Weeks 1-2: Summary statistics and graphical displays for a single categorical or quantitative variable and for relationships between two variables.
Collecting data
Week 2: Sampling. Observational studies and experiments. The effect of confounding and concluding causation.
Probability
Week 3: Probability models, the normal distribution, the Law of Large Numbers, the Central Limit Theorem, sampling distributions.
Confidence Intervals
Week 4: Confidence intervals and sample size estimation for proportions and means.
Tests of significance
Week 5: Tests of significance, power and sample size estimation for proportions and means
Two samples
Week 6: Tests of significance and confidence intervals for proportions and means in the two sample case.
Simple linear regression
Week 7: Method of least squares, evaluating model fit, the effects of outliers and influential observations.
The process of statistical inquiry
Week 8: Capstone case study.
Taught by Alison Gibbs and Jeffrey Rosenthal












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