This set of MCQ(multiple choice questions) will help you with the answers of **Data Analytics with Python NPTEL Week 9 Assignment Solutions**.

### Course layout

** Week 1: Basics of Python Spyder**Week 2 :

**Introduction to probability**

**Week 3 : Sampling and sampling distributions**

**Week 4 : Hypothesis testing**

**Week 5 : Two sample testing and introduction to ANOVA**

**Week 6 : Two way ANOVA and linear regression**

**Week 7 : Linear regression and multiple regression**

**Week 8 :**

**Concepts of MLE and Logistic regression**

**Week 9 : ROC and Regression Analysis Model Building**

**Week 10 : c**

^{2}Test and introduction to cluster analysis**Week 11 : Clustering analysis**

**Week 12 : Classification and Regression Trees (CART)**

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### Data Analytics with Python NPTEL Week 9 Assignment Solutions

**Q1.** State true or false: Statement: there is no difference between, y = β0 + β1x + 𝜖 and E(y) = β0 + β1x , both are regression equations

a) True

b) False

Answer:

**Q2.** Which of the following statements is correct

a) Sensitivity in ROC analysis is called True Positive Rate(tpr)

b) Specificity in ROC analysis is not called True Negative Rate (tnr)

c) Specificity in ROC analysis is called True Positive Rate(tpr)

d) Sensitivity in ROC analysis is called True Negative Rate (tnr)

Answer:

**Q3.** In ROC analysis when the Threshold value is Higher:

a) Specificity decreases

b) Sensitivity decreases

c) Both a. and b.

d) None of these

Answer:

**Q4.** Sensitivity in ROC analysis is defined as:

a) FP / (FP+TN)

b) FN/(TP+FN)

c) TN / (TN+FP)

d) TP / (TP+FN)

Answer:

**Q5.** In ROC analysis, a classifier is called ‘good’ if it has ______

a) Low TPR and Low FPR

b) Low TPR and High FPR

c) High TPR and Low FPR

d) High TPR and High FPR

Answer:

**Q6.** For the given confusion matrix, compute the sensitivity

a) 0.73

b) 0.7

c) 0.78

d) 0.8

Answer:

**Q7.** State true or False: Precision is inversely proportional to sensitivity

a) True

b) False

Answer:

**Q8.** State True or False: Standardization of features is not required before training a Logistic regression model

a) True

b) False

Answer:

**Q9.** Which of the following option is true?

a) Linear Regression errors values have to be normally distributed but in the case of Logistic Regression it is not the case

b) Logistic Regression errors values have to be normally distributed but in the case of Linear Regression it is not the case

c) Both Linear Regression and Logistic Regression error values have to be normally distributed

d) Both Linear Regression and Logistic Regression error values have not to be normally distributed

Answer:

**Q10.** In binary logistic regression,

a) The dependent variable is continuous

b) The dependent variable is divided into two equal subcategories

c) The dependent variable consists of two categories

d) There is no dependent variable

Answer:

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