regression analysis: simplify complex data relationships coursera weekly challenges 5 answers
Test your knowledge: Foundations of logistic regression
1. When building a logistic regression model, what does CLF stand for?
- Connector
- Classifier
- Claimer
- Codifier
2. Which package do you use to create a plot of your model to visualize its results?
- Results package
- Matrix package
- Dashboard package
- Seaborn package
Test your knowledge: Logistics regression with Python
3. No extreme outliers is one of the four main binomial logistic regression assumptions. What are the other three? Select all that apply.
- Linearity
- No multicollinearity
- Independent observations
- Homoscedasticity
4. Logit is the logarithm of the odds of a given probability.
- True
- False
5. Fill in the blank: The maximum likelihood estimation is a technique used for estimating the beta parameters that _____ the likelihood of a model producing the observed data.
- reduce
- maximize
- balance
- control
Test your knowledge: Interpret logistic regression results
6. The confusion matrix is a graphical representation of how accurate a classifier is at predicting what for a categorical variable?
- Precision
- Errors
- Labels
- Validity
7. Fill in the blank: _____ measures the proportion of positive predictions that were true positives.
- Validity
- Precision
- Accuracy
- Recall
8. Which of the following provide additional information about the likelihood of a result being merely by chance? Select all that apply.
- P-value
- Maximum likelihood estimation
- Confidence intervals
- Logit
Test your knowledge: Compare regression models
9. Which model might a data professional consider first if the outcome variable is binary?
- Single linear regression
- Hypothesis testing
- Multiple linear regression
- Binomial logistic regression
10. A data professional can use recall to evaluate a logistic regression model. What other metrics can be used to meet this goal? Select all that apply.
- Precision
- R squared
- P-value
- Confusion matrices
Weekly challenge 5
11. Fill in the blank: Binomial logistic regression is a technique that models the probability of an observation falling into one of two categories, based on one or more _____ variables.
- continuous
- categorical
- dependent
- independent
12. A data professional calculates a logarithm of the odds of a given probability. What are they calculating?
- Likelihood
- Precision
- Recall
- Logit
13. What technique estimates the beta parameters that increase the likelihood of the model producing observed data?
- Accuracy
- Maximum likelihood estimation
- Recall
- Precision
14. Which regression assumption states that, if multiple X variables are in a model, they should not be highly correlated with one another?
- No extreme outliers
- Independent observations
- Linearity
- No multicollinearity
15. What graphical representation demonstrates a classifier’s accuracy at predicting the labels for a categorical variable?
- Logistic matrix
- Confusion matrix
- Logistic graph
- Likelihood matrix
16. A data professional calculates precision in logistic regression results. They have 89 true positives, 83 true negatives, 3 false positives, and 1 false negative. What is the calculation for precision?
- (83 + 3) / 89
- 89 / (83 + 1)
- 89 / (89 + 3)
- (89 + 1) / 3
17. A data professional calculates accuracy in logistic regression results. They have 99 true positives, 91 true negatives, and 248 total predictions. What is the calculation for accuracy?
- 99 / (248 – 91)
- (248 – 99 ) / 91
- 248 / (99 + 91)
- (99 + 91) / 248
18. A data professional calculates recall in logistic regression results. They have 99 true positives, 80 true negatives, 7 false positives, and 4 false negatives. What is the calculation for recall?
- 80 / (80 + 7)
- (84 + 4) / 80
- (99 – 7) / (80 – 4)
- 99 / (99 + 4)
19. Logit includes which other probability formula?
- Recall
- Precision
- Estimation
- Odds
20. Fill in the blank: A confusion matrix is a graphical representation of how accurate a classifier is at _____ the labels for a categorical variable.
- organizing
- predicting
- limiting
- spacing
21. A data professional calculates recall in logistic regression results. They have 91 true positives, 84 true negatives, 6 false positives, and 5 false negatives. What is the calculation for recall?
- 91 / (91 + 5)
- (91 – 6) / (84 – 5)
- 84 / (84 + 6)
- (84 + 5) / 84
22. What technique models the probability of an observation falling into one of two categories, based on one or more independent variables?
- Binomial logistic regression
- Log-odds function
- Maximum likelihood estimation
- Logistic regression
23. What is the logit formula?
- Logarithm of p divided by 1 minus p
- Logarithm of 1 divided by p minus 1
- Logarithm of p plus 1 divided by p
- Logarithm of 1 plus p divided by p
24. Fill in the blank: Maximum likelihood estimation is a technique for estimating the _____ that maximize the likelihood of the model producing the observed data.
- continuous parameters
- continuous coefficients
- beta parameters
- beta coefficients
25. A data professional calculates precision in logistic regression results. They have 101 true positives, 63 true negatives, 4 false positives, and 2 false negatives. What is the calculation for precision?
- (101 + 2) / 4
- 101 / (101 + 4)
- 101 / (63 + 2)
- (63 + 4) / 101
26. A data professional calculates accuracy in logistic regression results. They have 87 true positives, 94 true negatives, and 222 total predictions. What is the calculation for accuracy?
- 87 / (222 – 94)
- (222 – 87 ) / 94
- (87 + 94) / 222
- 222 / (87 + 94)
27. Following the no extreme outliers assumption, when are outliers detected?
- While the model is being fit
- After the model is fit
- Before the model is fit
- Either before or after the model is fit
28. A data professional calculates accuracy in logistic regression results. They have 82 true positives, 75 true negatives, and 202 total predictions. What is the calculation for accuracy?
- (202 – 82) / 75
- 202 / (82 + 75)
- 82 / (202 – 75)
- (82 + 75) / 202
29. A data professional calculates accuracy in logistic regression results. They have 82 true positives, 75 true negatives, and 202 total predictions. What is the calculation for accuracy?
- (145 + 128) / (4 + 2)
- (128 + 2) / 128
- 145 / (145 + 2)
- (4 – 2) / 145