SEO Corporation Interview Questions and Answers

Q1. State linear regression assumptions.
Ans. 
Linear regression assumptions include linearity, independence, homoscedasticity, and normality.
• Linearity: The relationship between the independent and dependent variables is linear.

• Independence: The residuals are independent of each other.

• Homoscedasticity: The variance of the residuals is constant across all levels of the independent variables.

• Normality: The residuals are normally distributed.

• Example: If we are predict...read more
Answered by AI
Add your answer
Q2. What is multicollinearity? How to tackle this condition?
Ans. 
Multicollinearity is a phenomenon in which two or more predictor variables in a regression model are highly correlated.
• Multicollinearity can lead to unstable estimates of the coefficients and make it difficult to determine the effect of each predictor variable on the outcome.

• One way to tackle multicollinearity is to identify the highly correlated variables and consider removing one of them from the model.

• Another approa...read more
Answered by AI
Add your answer
Q3. What are the components of time series analysis?
Ans. 
Time series analysis components include trend, seasonality, cyclicality, and irregularity.
• Trend: Long-term movement or direction of the data.

• Seasonality: Regular patterns that occur at specific intervals.

• Cyclicality: Repeating patterns that are not necessarily at fixed intervals.

• Irregularity: Random fluctuations or noise in the data.

• Examples: Trend in stock prices, seasonality in retail sales, cyclicality in economic c
Answered by AI
Add your answer
Q4. What are the preventive measures if regression assumptions are not met?
Ans. 
Preventive measures for regression assumptions not met
• Check for multicollinearity among independent variables

• Transform variables if they are not normally distributed

• Consider using non-parametric regression methods

• Use robust regression techniques to handle outliers

• Collect more data to improve model performance
Answered by AI
Add your answer

Q1. How to tackle with missing values ?
Ans. 
Handling missing values is crucial in data analysis. Various techniques like imputation, deletion, or prediction can be used.
• Use imputation techniques like mean, median, mode to fill in missing values.

• Consider using predictive modeling to estimate missing values based on other variables.

• Delete rows or columns with a high percentage of missing values if they cannot be accurately imputed.

• Use advanced techniques like K-ne...read more
Answered by AI
Add your answer
Q2. What is the difference between tuple and list ?
Ans. 
Tuple is immutable and fixed in size, while list is mutable and can change in size.
• Tuple is created using parentheses, while list is created using square brackets.

• Tuple elements can be of different data types, while list elements are usually of the same data type.

• Tuple is faster than list for iteration and accessing elements.

• Example: tuple = (1, 'a', True), list = [1, 2, 3]
Answered by AI
Add your answer
Q3. How to detect outlier ?
Ans. 
Outliers can be detected using statistical methods like Z-score, IQR, or visualization techniques like box plots.
• Calculate Z-score for each data point and identify points with Z-score greater than a certain threshold (usually 3 or -3).

• Use Interquartile Range (IQR) to identify outliers by determining data points that fall below Q1 - 1.5 * IQR or above Q3 + 1.5 * IQR.

• Visualize the data using box plots and identify points...read more
Answered by AI
Add your answer

Q1. Why do you think you are a fit ?
Add your answer

Written test covering economic questions

Q1. Please introduce yourself in mandarin
Add your answer

Q1. Details about Statistics knowledge
Add your answer

Q1. Asked about previous job experience
Add your answer

Q1. Salary discussion and reasons for leaving previous company
Add your answer

Q1. Simple and easy interview. Good interview experience
Add your answer
Q2. All about resume and your skills
Add your answer

Why you , why the company, why this job

Writing test that was allowed to take home

Fit test with head of department

MMM questions. Statistics. Background of education

Q1. Statistics, coding economics
Add your answer

Q1. Questions regarding my projects
Add your answer
Q2. Questions regarding Multiple linear regression, multicollinearity, ridge regression and logistic mainly.
Add your answer
Q3. Technical questions from R, python and Excel (like lookup, pivot table)
Add your answer
Q4. There might be some riddle question also
Add your answer

Marketing Research, Advertisement companies

Q1. Expected Salary, Expectation from the Job role
Add your answer

Q1. What is Regression?What is Mutlicollinearity?
Ans. 
Regression is a statistical technique used to understand the relationship between a dependent variable and one or more independent variables. Multicollinearity occurs when independent variables in a regression model are highly correlated.
• Regression helps in predicting the value of the dependent variable based on the values of independent variables.

• Multicollinearity can lead to issues in interpreting the coefficients of...read more
Answered by AI
Add your answer