Regression and Time Series Analysis

Setting up linear regression (33:40)

4 Topics | 6 Quizzes

Overview of data science (8:11)

Concept review 1: overview of data science

What is regression? (7:51)

Concept review 2: what is regression?

Building linear regression (10:23)

Concept review 3: building linear regression

Exercise set 1: basic linear regression (5:13)

Assessing your model’s accuracy (7:15)

Concept review 4: assessing your model’s accuracy

Feedback for “Setting up linear regression”

Measuring model errors (38:13)

4 Topics | 7 Quizzes

Calculating variance and standard deviation (8:11)

Concept review 5: calculating variance and standard deviation

Identifying outliers (11:26)

Concept review 6: identifying outliers

Exercise set 2: finding residuals and outliers (9:52)

Calculating covariance and correlation (8:05)

Concept review 7: calculating covariance and correlation

Testing a model’s significance (9:59)

Concept review 8: testing a model’s significance

Exercise set 3: calculating statistical significance (6:47)

Feedback for “Measuring model errors”

Modeling multiple variables (31:21)

4 Topics | 7 Quizzes

Building a multivariate model (8:00)

Concept review 9: building a multivariate model

Exercise set 4: visualizing correlations (5:22)

Plotting multivariate regression (8:56)

Concept review 10: plotting multivariate regression

Identifying multicollinearity (6:33)

Concept review 11: identifying multicollinearity

Exercise set 5: multivariate regression (8:55)

Datafying categorical variables (7:52)

Concept review 12: datafying categorical variables

Feedback for “Modeling multiple variables”

Adjusting your model (38:01)

4 Topics | 8 Quizzes

Validating your model (9:37)

Concept review 13: validating your model

Exercise set 6: categorical variables (7:34)

Testing for heteroscedasticity (7:51)

Concept review 14: testing for heteroscedasticity

Identifying important variables (11:40)

Concept review 15: identifying important variables

Exercise set 7: model selection (9:45)

Building nonlinear regression models (8:53)

Concept review 16: building nonlinear regression models

Exercise set 8: building non-linear regression (7:35)

Feedback for “Adjusting your model”

Adding seasonality to your model (35:40)

4 Topics | 6 Quizzes

Transforming variables (10:03)

Concept review 17: transforming variables

Identifying seasonality (7:31)

Concept review 18: identifying seasonality

Calculating seasonality (9:19)

Concept review 19: calculating seasonality

Exercise set 9: calculating seasonality (10:19)

Minimizing seasonality errors (8:47)

Concept review 20: minimizing seasonality errors

Feedback for “Adjusting seasonality to your model”

Refining your model (29:33)

5 Topics | 7 Quizzes

Calculating seasonality components (6:34)

Concept review 21: calculating seasonality components

Predicting customer demand (7:35)

Concept review 22: predicting customer demand

Exercise set 10: Holt-Winters modeling (5:13)

Using the LOESS method (6:52)

Concept review 23: using the LOESS method

Additional considerations and tips (8:32)

Concept review 24: additional considerations and tips

Exercise set 11: running LOESS models (9:42)

Feedback for “Refining your model”

End of course feedback survey

Concept review 2: what is regression?

Regression and Time Series Analysis Setting up linear regression (33:40) What is regression? (7:51) Concept review 2: what is regression?

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Question 1 of 5

1. Question
1. Fill in the blank below.
- Forecasting means to something after looking at the available information.
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Question 2 of 5

2. Question
2. What can regression do?
- Predict the numerical value of a variable based on the value of another variable(s)
- Predict probabilities
- Confirm causation
- Predict group membership
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Question 3 of 5

3. Question
3. Which of these use cases are examples of how regression is applied? Select all that apply.
Select all that apply
- GlaxoSmithKline predicts the supply of participants for clinical trials of new drugs in order to allocate drug trial resources.
- Amazon uses forecasts of consumer demand to manage inventory levels.
- Ford uses forecasts of seasonally adjusted annual sales rates to manufacture cars.
- New South Wales, Australia, predicts travel time in Sydney based on events in the city and weather forecasts.
- The Heritage Provider Network predicts the number of days a patient will spend in the hospital over the next year.
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Question 4 of 5

4. Question
4. Using the Capital Bikeshare data, what questions can we answer through regression analysis?
Select all that apply
- How does any single given factor (air temperature, humidity, wind speed) affect demand for bikes?
- How do several variables (air temperature, humidity, wind speed, day of the week, holidays, hour of the day) affect demand for bikes?
- How can you factor seasonality and cyclicality when forecasting demand?
- How can you figure out how the color of the bike impacts customer ride satisfaction?
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Question 5 of 5

5. Question
5. What is the first relationship we are going to look at using the Capital Bikeshare data?
- Relationship between the number of bikers and temperature.
- Relationship between the number of bikers and neighborhood.
- Relationship between the length of ride and temperature.
- Relationship between the damage of bikes and neighborhood.
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