Embarking on a career in data science or analytics? The interview room can be challenging, particularly regarding probability and statistics questions. Navigating these questions requires more than just theoretical knowledge; it demands a practical understanding of applying statistical concepts. In this article, we delve into key interview questions, unraveling the intricacies of probability and statistics. Whether you’re preparing for a job interview or simply seeking to enhance your, these insights will prove invaluable.

*This article was published as a part of theÂ Data Science Blogathon.*

**1. Which of the following relation is correct for a negative skewed distribution?**

(a) Mean=Mode=Median

(b) Mean>Median>Mode

(c) Mode>Median>Mean

(d) Mean>Mode=Median

**Solution:(c)**

**Explanation:**Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â

**2.Â In the symmetric covariance matrix:**

(a) Diagonal elements must be positive and other elements are always zero.

(b) Diagonal elements can never be negative and other elements are always positive.

(c) Diagonal elements can never be negative and other elements can be negative or positive.

(d) Diagonal elements can be negative and positive and other elements are always negative.

**Solution: (c)**

**Explanation: In a covariance matrix, the diagonal entries represent covariance of the variable with itself which is equal to the variance of that variable and is calculated as the square of standard deviation. Since variance is always positive, therefore diagonal entries are always positive.**

**3. Presence of Outliers in a dataset not affects:**

(a) Standard deviation

(b) Range

(c) Mean

(d) Inter-quartile Range(IQR)

**Solution: (d) **

**Explanation: The IQR is essentially the range of the middle 50% of the data. Since it uses the middle 50%, therefore it is not affected by the outliers.**

**4. If X and Y are independent random variables, then which of the following is TRUE?**

(a) E(XY)=E(X)E(Y) [ E represents Expectation value ]

(b) Cov(X,Y)=0 [ Cov represents covariance between variables ]

(c) Var(X+Y)=Var(X)+Var(Y) [ Var represents variance ]

(d) All of the above

**Explanation:**** If X and Y are independent then Cov(X,Y)=0 and Var(X+Y) = Var(X)+Var(Y) (âˆµ 2Cov(X, Y) = 0)**

** **

**5. For a normal distribution Z, which option is TRUE?**

(a) Coefficient of skewness (E(Z^{3}))=0

(b) E(Z)=0 ; E(Z^{2})=Var(Z)=1

(c) Kurtosis (E(Z^{4}))=3

(d) Its density is symmetric about the mean.

**Solution: (d) **

**Explanation: **

** **

**6. Let X and Y be normal random variables with their respective means 3 and 4 and variances 9 and 16, then 2X-Y will have normal distribution with parameters:**

(a) Mean=2 and Variance=52

(b) Mean=0 and Variance=1

(c) Mean=2 and Variance=1

(d) None of the above

**Solution: (d)**

**Hint: Var(aX + bY) = a ^{2} Var(X) + b^{2} Var(Y) + 2abCov(X,Y)**

**7. Suppose X and Y take values {0,1} and are independent with P(X=1)=1/2 and P(Y=1)=1/3. What is the probability that P(X+Y=1)?**

(a) 5/18

(b) 1/2

(c) 5/6

(d) 1/6

**Solution:(b) **

**Explanation: P(X +Y =1) = P(X=0).P(Y=1) + P(X=1).P(Y=0) = (1/2)(1/3) + (1/2)(2/3) = 1/2.**

**8. Let X and Y are random variables with E(X)=Î¼/2 and E(Y)=Î¼, then which one is TRUE?**

(a) g=X+Y is an unbiased estimator of Î¼

(b) g = X+Y is a biased estimator of Î¼ with bias equals to Î¼

(c) h=X+(Y/2) is an unbiased estimator of Î¼

(d) h= X+(Y/2) is a biased estimator of Î¼ with bias equals to Î¼/2

**Solution: (c)**

**Explanation: E(g)= E(X+Y)= E(X) + E(Y)=3Î¼/2 ; Bias(g)= E(g)-Î¼ = Î¼/2**** E(h)= E(X+(Y/2))= E(X) + 1/2E(Y) = Î¼, Bias(h)= E(h)-Î¼ = 0**

** **

**9. Suppose that X takes values between 0 and 1 and has probability density function(PDF) 2x, then the value of Variance of X ^{2} is :**

(a)1/12

(b) 1/18

(c) 1/6

(d) 5/18

**Solution:(a) **

**Hint: Use Var(X ^{2})= E(X^{4}) -(E(X^{2}))^{2} **

** **

**10. For random variables X and Y, we have Var(X)=1, Var(Y)=4, and Var(2X-3Y)=34, then the correlation between X and Y is:**

(a) 1/2

(b) 1/4

(c) 1/3

(d) None of the above

**Solution:(b)**

**Explanation: Var(2X-3Y) = 34 **

** = 4Var(X)+9Var(Y)-12Cov(X, Y)**

** = 4(1)+9(4)-12Cov(X, Y) = 34**

** **âˆ´** Cov(X, Y)=1/2 **

** **

** **

**11. A fair die is rolled repeatedly until a number larger than 4 is observed. If K is the total number of times that the die is rolled, then P(K=4) is equal to:**

(a) 16/81

(b) 8/81

(c) 8/27

(d) 16/27

**Solution: (b)**

**Explanation: P(K=4) = (P(#less than 4 or equal)) ^{3}.P({4}) = (2/3)^{3}.(1/3) = 8/81.**

** **

**12. Let X and Y be independent uniform (0, 1) random variables. Define A=X+Y and B=X-Y. Then,**

(a) A and B are independent random variables

(b) A and B are uncorrelated random variables

(c) A and B are both uniforms (0,1) random variables.

(d) None of these

**Solution: (b)**

**Explanation: Cov(X+Y, X-Y) = Cov(X, X) – Cov(X, Y) + Cov(Y, X) – Cov(Y ,Y) ****â‡’ Var(X) – Var(Y) = 0**

** **

**13. If g is a point estimator of X, then Mean Square error(MSE) for g is:**

(a) Variance(g) + Bias(g)

(b) Variance(g) + Bias(g^{2})

(c) Variance(g) + (Bias(g))^{2}

(d) Variance(g^{2}) + Bias(g)

**Solution: (c)**

**Explanation: MSE(g) = E[ (g-X) ^{2} ] = Var(g-X) + (E[ g-X ])^{2} = Var(g) + (Bias(g))^{2}**

** **

**14. Let X and Y be two random variables and let a, b, c, d be real numbers, then which one of the following is FALSE?**

(a) Cov(X+b, Y+d) = Cov(X, Y)

(b) Cov(aX, cY) = ac*Cov(X, Y)

(c) Cov(aX+b, cY+d) = ac*Cov(X, Y)

(d) Corr(aX+b, cY+d) = ac*Corr(X, Y) for a,c>0

**Solution: (d)**

**Explanation: Corr(aX+b, cY+d) = Corr(X, Y)**

** **

**15. Let X and Y be jointly(bivariate) normal with Var(X) = Var(Y), then:**

(a) X+Y and X-Y are jointly normal

(b) X+Y and X-Y are uncorrelated

(c) X+Y and X-Y are independent

(d) All of the above

**Solution: (d)**

**Explanation: ****If X and Y be the bivariate normal distribution, then any linear combination of X and Y is also normally distributed.**

** **

**16. Let X _{1}, X_{2}, X_{3}, ——-, X_{n} be a random sample from a distribution with E(X_{i})=**

**g _{1}=X_{1} g_{2}=X’=(X_{1}+X_{2}+X_{3}+————-X_{n})/n**

**Which of these estimator has high mean squared error(MSE)?**

(a) g_{1}

(b) g_{2}

(c) Same for both g_{1 }and g_{2}

(d) None of the above

**Solution: (a)**

**Explanation: MSE(g _{1})=E[(g_{1}–Î¼)^{2}] = E[(X_{1}-E(X_{1}))^{2}] = Var(X_{1}) = Ïƒ^{2}**

** MSE(g _{2})=E[(g_{2}–Î¼)^{2}]= E[(X’-Î¼)^{2}] = Var(X’-Î¼) + (E[X’-Î¼])^{2} = Var(X’) = Ïƒ^{2}/n**

**17. A random sample of n=6 taken from the population has the elements 6, 10, 13, 14 ,18, 20. Then, which option is False?**

(a) Point estimate for population mean is 13.5

(b) Point estimate for population standard deviation is 4.68

(c) Point estimate for population standard deviation is 3.5

(d) Point estimate for standard error of mean is 1.91

**Solution: (c)**

**Explanation: Population mean(X’) = (Î£ X _{i}/n ) = 13.5**

** Population standard deviation(S) = sqrt( (Î£ X _{i}^{2}/n) – (Î£ X_{i}/n)^{2} ) = 4.68**

** Standard error of mean = S/sqrt(n) = 4.68/sqrt(6) = 1.91**

** **

Section-2

** **

**18. True or False: If the Pearson’s correlation between 2 variables is zero, then they are necessarily independent. **

**Explanation: Correlation is a measure of linear dependence between the variables.**

** **

**19. True or False: Let g be an unbiased estimator of X and U be a random variable with zero means, then h=g+U ****is also unbiased for X.**

**Solution: True.**

**Explanation: E(h) =E(g) + E(U) = 0+0 =0( **âˆµ **E(g)=0 due to unbiased estimator)**

** **

**20. True or False: Let X and Y be two independent standard normal random variables and T=XY ^{2}+X+1 and P=X-3, then Cov(T, P)=1**

**Solution: False.**

**Hint: Use properties mentioned in Question-14.**

** **

**21. True or False: Let X has a normal distribution with parameters Î¼ and Ïƒ ^{2}**,

**Solution: False.**

**Explanation: For the given statement to be True, X should be Standard normal distribution(****Î¼=0, ****Ïƒ ^{2}**

** **

**22. True or False: If the characteristic function of a random variable exists, then its expectation and variance will also exist.**

**Solution: False.**

**Hint: Moment Generating Function(MGF)**

** **

**23. True or False: Let X has uniform distribution U(a, b) such that E(X)=2 and Var(X)=3/4, then P(X<1)=1/6.**

**Solution: True.**

**Explanation: E(X) = (a+b)/2 = a+b=4 ; Var(X) = (b-a) ^{2}/12 = (b-a)=3 **

**24. True or False: The correlation coefficient between X+Y and X-Y, where X and Y are independent random variables with variances 36 and 16 respectively is 6/13.**

**Solution: False.**

**Explanation: Corr(X+Y, X-Y) = Cov(X+Y, X-Y)/ Std(X+ Y).Std(X-Y) [Std= Standard Deviation]**

** **

**25. True or False: In interval estimation, ****As the confidence level increases the margin of error decreases.**

**Solution: False.**

**Explanation: ****The Confidence Interval is defined as ****X Â± Z( s/âˆšn)**

** **

In summary, probability and statistics form the backbone of informed decision-making in our data-driven world. From predicting trends to guiding research, these concepts are indispensable. To deepen your mastery, explore our Blackbelt AI/ML program. Equip yourself with essential statistical skills crucial for success in data science. Join today to unlock the power of understanding and navigating the complexities of statistical data.

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Understanding Cost Function
Understanding Gradient Descent
Math Behind Gradient Descent
Assumptions of Linear Regression
Implement Linear Regression from Scratch
Train Linear Regression in Python
Implementing Linear Regression in R
Diagnosing Residual Plots in Linear Regression Models
Generalized Linear Models
Introduction to Logistic Regression
Odds Ratio
Implementing Logistic Regression from Scratch
Introduction to Scikit-learn in Python
Train Logistic Regression in python
Multiclass using Logistic Regression
How to use Multinomial and Ordinal Logistic Regression in R ?
Challenges with Linear Regression
Introduction to Regularisation
Implementing Regularisation
Ridge Regression
Lasso Regression

Introduction to Stacking
Implementing Stacking
Variants of Stacking
Implementing Variants of Stacking
Introduction to Blending
Bootstrap Sampling
Introduction to Random Sampling
Hyper-parameters of Random Forest
Implementing Random Forest
Out-of-Bag (OOB) Score in the Random Forest
IPL Team Win Prediction Project Using Machine Learning
Introduction to Boosting
Gradient Boosting Algorithm
Math behind GBM
Implementing GBM in python
Regularized Greedy Forests
Extreme Gradient Boosting
Implementing XGBM in python
Tuning Hyperparameters of XGBoost in Python
Implement XGBM in R/H2O
Adaptive Boosting
Implementing Adaptive Boosing
LightGBM
Implementing LightGBM in Python
Catboost
Implementing Catboost in Python

Introduction to Clustering
Applications of Clustering
Evaluation Metrics for Clustering
Understanding K-Means
Implementation of K-Means in Python
Implementation of K-Means in R
Choosing Right Value for K
Profiling Market Segments using K-Means Clustering
Hierarchical Clustering
Implementation of Hierarchial Clustering
DBSCAN
Defining Similarity between clusters
Build Better and Accurate Clusters with Gaussian Mixture Models

Introduction to Machine Learning Interpretability
Framework and Interpretable Models
model Agnostic Methods for Interpretability
Implementing Interpretable Model
Understanding SHAP
Out-of-Core ML
Introduction to Interpretable Machine Learning Models
Model Agnostic Methods for Interpretability
Game Theory & Shapley Values

Deploying Machine Learning Model using Streamlit
Deploying ML Models in Docker
Deploy Using Streamlit
Deploy on Heroku
Deploy Using Netlify
Introduction to Amazon Sagemaker
Setting up Amazon SageMaker
Using SageMaker Endpoint to Generate Inference
Deploy on Microsoft Azure Cloud
Introduction to Flask for Model
Deploying ML model using Flask