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Eli Lilly and Company

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10+ TCS Interview Questions and Answers

Updated 9 Jan 2025

Q1. What is the difference between descriptive and inferential statistics? Give an example to support the same.

Ans.

Descriptive statistics summarize data, while inferential statistics make predictions or inferences about a population based on a sample.

  • Descriptive statistics describe the main features of a dataset, such as mean, median, and standard deviation.

  • Inferential statistics involve making predictions or inferences about a population based on a sample, using techniques like hypothesis testing and confidence intervals.

  • Example: Descriptive statistics would be calculating the average he...read more

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Q2. Which project are you currently working on? What statistical techniques are you planning to use in the project?

Ans.

I am currently working on a project analyzing customer behavior in an e-commerce platform.

  • Using regression analysis to identify factors influencing customer purchase decisions

  • Applying clustering techniques to segment customers based on their behavior

  • Utilizing hypothesis testing to determine the effectiveness of marketing strategies

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Q3. What is degree of freedom, and what does it signify? What is the real interpretation of the degree of freedom?

Ans.

Degree of freedom represents the number of independent values or quantities which can be assigned to a statistical distribution.

  • Degree of freedom is the number of values in the final calculation of a statistic that are free to vary.

  • In simple terms, it signifies the number of independent pieces of information available to estimate a parameter.

  • For example, in a t-test, the degree of freedom is n-1, where n is the sample size.

  • In ANOVA, the degree of freedom for between groups is...read more

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Q4. Follow up to previous - What is t-test? What are some assumptions/criteria the data needs to fulfill for applying the t-test? Some questions about t distribution.

Ans.

T-test is a statistical test used to determine if there is a significant difference between the means of two groups.

  • T-test is used to compare the means of two groups to see if they are significantly different.

  • Assumptions for t-test include normal distribution of data, independence of observations, and homogeneity of variances.

  • There are two types of t-tests: independent samples t-test and paired samples t-test.

  • T-distribution is similar to the normal distribution but with heavi...read more

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Q5. 1 )What is EventLoop? 2) How JavaScript runs? 3) Difference b/w var, let, and const? 4) setTimeout() related questions? 5) Clouser-related questions?

Ans.

EventLoop is a mechanism in JavaScript that allows for asynchronous execution of code.

  • EventLoop is responsible for managing the execution of code in JavaScript.

  • It ensures that code is executed in a non-blocking manner.

  • EventLoop continuously checks for any pending tasks and executes them.

  • It maintains a call stack and a task queue to manage the order of execution.

  • EventLoop is crucial for handling asynchronous operations in JavaScript.

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Q6. What do you know about clinical trials? What are it's phases and in each phase how the sample size is determined?

Ans.

Clinical trials are research studies that test how well new medical treatments work in people.

  • Clinical trials are conducted to evaluate the safety and efficacy of new treatments or interventions.

  • There are typically four phases in clinical trials: Phase 1, Phase 2, Phase 3, and Phase 4.

  • In Phase 1, the sample size is usually small (20-100 participants) to assess safety and dosage.

  • Phase 2 involves a larger sample size (100-300 participants) to evaluate effectiveness and side eff...read more

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Q7. What is p-value? Explain in layman terms and give an example.

Ans.

P-value is a measure that helps determine the strength of evidence against the null hypothesis in statistical hypothesis testing.

  • P-value is the probability of obtaining results as extreme as the observed results, assuming the null hypothesis is true.

  • A low p-value (typically ≤ 0.05) indicates strong evidence against the null hypothesis, leading to its rejection.

  • For example, if a study comparing two drugs shows a p-value of 0.03, it suggests that there is a 3% chance that the r...read more

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Q8. Explain when ANOVA is used. What assumptions need to be fulfilled before testing with ANOVA?

Ans.

ANOVA is used to compare means of three or more groups. Assumptions include normality, homogeneity of variances, and independence.

  • ANOVA is used to compare means of three or more groups to determine if there is a significant difference among them.

  • Assumptions for ANOVA include normality of data within each group, homogeneity of variances between groups, and independence of observations.

  • Example: ANOVA can be used to compare the average test scores of students in different school...read more

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Q9. In descriptive statistics, what are the different techniques? Explain kurtosis and it's three types.

Ans.

Descriptive statistics techniques include measures of central tendency, dispersion, skewness, and kurtosis.

  • Measures of central tendency include mean, median, and mode.

  • Measures of dispersion include range, variance, and standard deviation.

  • Skewness measures the asymmetry of the data distribution.

  • Kurtosis measures the peakedness or flatness of the data distribution.

  • The three types of kurtosis are mesokurtic (normal distribution), leptokurtic (tall and sharp peak), and platykurti...read more

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Q10. What is box plot? Why is it used? How do we assess the skewness of distribution from the box plot?

Ans.

Box plot is a visual representation of the distribution of a dataset, used to identify outliers and assess skewness.

  • Box plot displays the median, quartiles, and potential outliers of a dataset.

  • It is used to visually assess the spread, skewness, and presence of outliers in the data.

  • Skewness can be assessed from the box plot by observing the relative lengths of the whiskers and the position of the median.

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Q11. Stage-2: Behavioral round - explain heteroscedaticity to your granny.

Ans.

Heteroscedasticity is when the variability of a variable is unequal across the range of values of a second variable.

  • Heteroscedasticity refers to the situation where the variance of errors or residuals is not constant across all levels of the independent variable.

  • It violates the assumption of homoscedasticity in regression analysis.

  • For example, in a scatter plot, if the spread of data points widens or narrows as the value of the independent variable increases, it indicates het...read more

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Q12. Stage-3 : Explain the difference between Statistical modelling & Machine Learning.

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