No prerequisites.
| Instructors: |
Prof. Michael Paul Prof. William Aspray |
(Office hour: Thursdays 10:30am-11:30am, ENVD 207) (Office hour: Wednesdays 11:00am-noon, ENVD 207) |
| Time/Place: |
MWF 10:00 AM - 10:50 AM CLRE 207 (Campus map, Google Maps) |
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| Contact: | info1301@colorado.edu |
| Textbook: | Diez, Barr, Çetinkaya-Rundel (2015) OpenIntro Statistics, 3rd Edition. |
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Surveys concepts and techniques for characterizing and quantifying data.
Students will learn to use different types of quantitative data,
to summarize data with descriptive statistics, to measure similarity of
different datasets, to interpret probabilities and statistical significance
and to quantify and predict changes in data.
No prerequisites. |
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| Lecture | Materials | Readings |
| Part 1: The Shape of Data | ||
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Monday, August 22, 2016
Introduction Course introduction and overview. |
[slides-1] | |
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Wednesday, August 24, 2016
What is data? Representations of data. |
[slides-2] | |
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Friday, August 26, 2016
What is data? (cont'd) Types of data. |
[slides-2] |
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Monday, August 29, 2016
What is data? (cont'd) Representing data in MiniTab. |
[slides-3] |
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Wednesday, August 31, 2016
What is a data? (cont'd) Data relationships, associations. |
[slides-3] |
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Friday, September 2, 2016
What is a dataset? Representing collections. Sets, vectors, matrices. Set operations and Venn diagrams. |
[slides-4] [notes-1] |
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Monday, September 5, 2016
No class – Labor Day holiday
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Wednesday, September 7, 2016
What is a dataset? (cont'd) Collecting data. Populations and sampling. |
[slides-5] |
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Friday, September 9, 2016
Truth and logic Brief introduction to boolean variables and boolean logic. |
[slides-6] |
Quiz 1 today
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Monday, September 12, 2016
Describing data Mean, median, mode. |
[slides-7] |
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Wednesday, September 14, 2016
Describing data (cont'd) Variance and standard deviation. |
[slides-8] |
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Friday, September 16, 2016
Describing data (cont'd) Percentiles and box plots. Outliers. Euclidean distance, correlation. |
[slides-9] | |
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Monday, September 19, 2016
Practicum 1: Describing data Descriptive statistics in MiniTab. Visualizing data. |
[problems-1] |
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Wednesday, September 21, 2016
Review Day Catch up. More examples. |
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Friday, September 23, 2016
Midterm Exam 1
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| Part 2: Randomness of Data | ||
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Monday, September 26, 2016
Probability basics Random variables, outcomes. Why is probability useful? |
[slides-10] |
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Wednesday, September 28, 2016
Probability basics (cont'd) Probability operations. Revisiting sets, Venn diagrams. |
[slides-11] |
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Friday, September 30, 2016
Probability basics (cont'd) Independence. Conditional probability. |
[slides-12] |
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Monday, October 3, 2016
Quantifying randomness Brief introduction to information theory. Information entropy. |
[slides-13] |
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Wednesday, October 5, 2016
Quantifying randomness (cont'd) Applications of entropy. Measuring predictability, equality, diversity. |
[slides-14] |
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Friday, October 7, 2016
Review Day Catch up. More examples. |
[slides-15] | |
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Monday, October 10, 2016
The normal distribution Distributions as functions. Bell curves and the normal approximation. |
[slides-16] |
Quiz 2 today
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Wednesday, October 12, 2016
The normal distribution (cont'd) Data standardization. Area and percentiles. |
[slides-16] |
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Friday, October 14, 2016
The normal distribution (cont'd) Percentiles continued. |
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Monday, October 17, 2016
Quantifying chance Variability, confidence intervals. |
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Wednesday, October 19, 2016
Quantifying chance (cont'd) Hypothesis testing. Interpreting p-values. |
[slides-17] |
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Friday, October 21, 2016
Quantifying chance (cont'd) Hypothesis testing. Interpreting p-values. |
[slides-17] |
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Monday, October 24, 2016
Review Day Catch up. More examples. |
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Wednesday, October 26, 2016
Midterm Exam 2
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| Part 3: Changes in Data | ||
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Friday, October 28, 2016
Rate of change Slope and intercept. |
[slides-18] |
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Monday, October 31, 2016
Rate of change (cont'd) Interpreting slope. Trends, minima & maxima. |
[slides-19] |
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Wednesday, November 2, 2016
Rate of change (cont'd) Fitting and using lines. |
[slides-19] |
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Friday, November 4, 2016
Rate of change (cont'd) Brief introduction to derivatives. Derivatives in Wolfram Alpha. |
[slides-20] | |
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Monday, November 7, 2016
Rate of change (cont'd) Interpreting derivatives. Revisiting minima & maxima. |
[slides-20] | |
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Wednesday, November 9, 2016
Review Day Catch up. More examples. |
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Friday, November 11, 2016
Review Day Catch up. More examples. |
Quiz 3 today | |
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Monday, November 14, 2016
Mathematics of regression Fit as a function of slope. Root mean squared error. |
[slides-21] |
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| Part 4: Applications and Lenses of Data | ||
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Wednesday, November 16, 2016
Statistics of polling Revisiting the Central Limit Theorem. |
[slides-22] | |
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Friday, November 18, 2016
Descriptive statistics Revisiting data statistics and probability. Practical applications. |
[slides-23] | |
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Monday, November 21, 2016
No class – Fall break
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Wednesday, November 23, 2016
No class – Fall break
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Friday, November 25, 2016
No class – Thanksgiving holiday
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Monday, November 28, 2016
Inferential statistics Revisiting modeling and regression. Practical applications. |
[slides-24] | |
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Wednesday, November 30, 2016
Final Exam Review Review of semester. |
Course evaluations today | |
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Friday, December 2, 2016
Final Exam Review Review of semester. |
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Monday, December 5, 2016
Beyond MiniTab Databases and spreadsheets. |
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Wednesday, December 7, 2016
Statistical programming R programming language. | ||
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Friday, December 9, 2016
Practicum 3: R Practicing R in class. |
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| Final Exam Period | ||
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Sunday, December 11, 2016
Final ExamTime: 7:30pm-10:00pm Location: CLRE 207 (our usual room) |
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