# Linear Regression Interview Questions – Part 1

It is a common practice to test data science aspirants on linear regression as it is the first algorithm that almost everyone studies in Data Science/Machine Learning. Aspirants are expected to possess an in-depth knowledge of these algorithms. We consulted hiring managers and data scientists from various organisations to know about the typical Linear Regression questions which they ask in an interview. Based on their extensive feedback a set of question and answers were prepared to help students in their conversations.

**Q1. What is linear regression?**

In simple terms, linear regression is a method of finding the best straight line fitting to the given data, i.e. finding the best linear relationship between the independent and dependent variables.

In technical terms, linear regression is a machine learning algorithm that finds the best linear-fit relationship on any given data, between independent and dependent variables. It is mostly done by the Sum of Squared Residuals Method.

**Q2. What are assumptions in a linear regression model?**

The assumptions of linear regression are:

- The assumption about the form of the model: It is assumed that there is a linear relationship between the dependent and independent variables. It is known as the ‘linearity assumption’.
- Assumptions about the residuals:
- Normality assumption: It is assumed that the error terms, ε
^{(i)}, are normally distributed. - Zero mean assumption: It is assumed that the residuals have a mean value of zero, i.e., the error terms are normally distributed around zero.
- Constant variance assumption: It is assumed that the residual terms have the same (but unknown) variance, σ
^{2}. This assumption is also known as the assumption of homogeneity or homoscedasticity. - Independent error assumption: It is assumed that the residual terms are independent of each other, i.e. their pair-wise covariance is zero.

- Normality assumption: It is assumed that the error terms, ε
- Assumptions about the estimators:
- The independent variables are measured without error.
- The independent variables are linearly independent of each other, i.e. there is no multicollinearity in the data.

*Explanations:*

- This is self-explanatory.
- If
the residuals are not normally distributed, their randomness is lost,
which implies that the model is not able to explain the relation in the
data.

Also, the mean of the residuals should be zero.

Y^{(i)i}= β_{0}+ β_{1}x^{(i)}+ ε^{(i)}

This is the assumed linear model, where ε is the residual term.

E(Y) = E(β_{0}+ β_{1}x^{(i)}+ ε^{(i)})

= E(β_{0}+ β_{1}x^{(i)}+ ε^{(i)})

If the expectation(mean) of residuals, E(ε^{(i)}), is zero, the expectations of the target variable and the model become the same, which is one of the targets of the model.

The residuals (also known as error terms) should be independent. This means that there is no correlation between the residuals and the predicted values, or among the residuals themselves. If some correlation is present, it implies that there is some relation that the regression model is not able to identify. - If the independent variables are not linearly independent of each other, the uniqueness of the least squares solution (or normal equation solution) is lost.

**Q3. What is heteroscedasticity? What are the consequences, and how can you overcome it?**

A random variable is said to be heteroscedastic when different subpopulations have different variabilities (standard deviation).

The existence of heteroscedasticity gives rise to certain problems in the regression analysis as the assumption says that error terms are uncorrelated and, hence, the variance is constant. The presence of heteroscedasticity can often be seen in the form of a cone-like scatter plot for residual vs fitted values.

One of the basic assumptions of linear regression is that the data should be homoscedastic, i.e., heteroscedasticity is not present in the data. Due to the violation of assumptions, the Ordinary Least Squares (OLS) estimators are not the Best Linear Unbiased Estimators (BLUE). Hence, they do not give the least variance than other Linear Unbiased Estimators (LUEs).

There is no fixed procedure to overcome heteroscedasticity. However, there are some ways that may lead to a reduction of heteroscedasticity. They are —

- Logarithmising the data: A series that is increasing exponentially often results in increased variability. This can be overcome using the log transformation.
- Using weighted linear regression: Here, the OLS method is applied to the weighted values of X and Y. One way is to attach weights directly related to the magnitude of the dependent variable.

**Q4. How do you know that linear regression is suitable for any given data?**

To
see if linear regression is suitable for any given data, a scatter plot
can be used. If the relationship looks linear, we can go for a linear
model. But if it is not the case, we have to apply some transformations
to make the relationship linear. Plotting the scatter plots is easy in
case of simple or univariate linear regression. But in the case of
multivariate linear regression, two-dimensional pairwise scatter plots,
rotating plots, and dynamic graphs can be plotted.

**Q5. How is hypothesis testing used in linear regression?**

Hypothesis testing can be carried out in linear regression for the following purposes:

- To check whether a predictor is significant for the prediction of the target variable. Two common methods for this are —
- By the use of p-values:

If the p-value of a variable is greater than a certain limit (usually 0.05), the variable is insignificant in the prediction of the target variable. - By checking the values of the regression coefficient:

If the value of the regression coefficient corresponding to a predictor is zero, that variable is insignificant in the prediction of the target variable and has no linear relationship with it.

- By the use of p-values:
- To check whether the calculated regression coefficients are good estimators of the actual coefficients.

The Null and Alternate Hypothesis used in the case of linear regression, respectively, are:

β1=0

β1≠0

Thus, if we reject the Null hypothesis, we can say that the coefficient β1 is not equal to zero and hence, is significant for the model. On the other hand, if we fail to reject the Null hypothesis, it is concluded that the coefficient is insignificant and should be dropped from the model.

**Q6. How do you interpret a linear regression model?**

A linear regression model is quite easy to interpret. The model is of the following form:

y=β0+β1X1+β2X2+…+βnXn

The
significance of this model lies in the fact that one can easily
interpret and understand the marginal changes and their consequences.
For example, if the value of *x _{0}* increases by 1 unit, keeping other variables constant, the total increase in the value of

*y*will be

*β*. Mathematically, the intercept term (

_{i}*β*) is the response when all the predictor terms are set to zero or not considered.

_{0}**Q7. What are the shortcomings of linear regression?**

You should never just run a regression without having a good look at your data because simple linear regression has quite a few shortcomings:

- It is sensitive to outliers
- It models the linear relationships only
- A few assumptions are required to make the inference

These phenomena can be best explained by the Anscombe’s Quartet, shown below:

As we can see, all the four linear regression are exactly the same. But there are some peculiarities in the datasets which have fooled the regression line. While the first one seems to be doing a decent job, the second one clearly shows that linear regression can only model linear relationships and is incapable of handling any other kind of data. The third and fourth images showcase the linear regression model’s sensitivity to outliers. Had the outlier not been present, we could have gotten a great line fitted through the data points. So we should never ever run a regression without having a good look at our data.

**Q8. What parameters are used to check the significance of the model and the goodness of fit?**

To check if the overall model fit is significant or not, the primary parameter to be looked at is the **F-statistic**.
While the t-test along with the p-values for betas test if each
coefficient is significant or not individually, the F-statistic is a
measure that can determine whether the overall model fit with all the
coefficients is significant or not.

The basic idea behind the F-test is that it is a relative comparison between the model that you’ve built and the model without any of the coefficients except for β0. If the value of the F-statistic is high, it would mean that the Prob(F) would be low and hence, you can conclude that the model is significant. On the other hand, if the value of F-statistic is low, it might lead to the value of Prob(F) being lower than the significance level (taken 0.05, usually) which in turn would conclude that the overall model fit is insignificant and the intercept-only model can provide a better fit.

Apart from that, to test the goodness or the extent of fit, we look at a parameter called **R-squared** (for simple linear regression models) or **Adjusted R-squared**
(for multiple linear regression models). If your overall model fit is
deemed to be significant by the F-test, you can go ahead and look at the
value of R-squared. This value lies between 0 and 1, with 1 meaning a
perfect fit. A higher value of R-squared is indicative of the model
being good with much of the variance in the data being explained by the
straight line fitted. For example, an R-squared value of 0.75 means that
75% of the variance in the data is being explained by the model. But it
is important to remember than R-squared only tells the extent of the
fit and should not be used to determine whether the model fit is
significant or not.

**Q9. ****If two variables are correlated, is it necessary that they have a linear relationship?**

No, not necessarily. If two variables are correlated, it is very much possible that they have some other sort of relationship and not just a linear one.

But the important point to note here is that there are two correlation coefficients that are widely used in regression. One is the Pearson’s R correlation coefficient which is the correlation coefficient you’ve studied in the linear regression model. This correlation coefficient is designed for linear relationships and it might not be a good measure for if the relationship between the variables is non-linear. The other correlation coefficient is Spearman’s R which is used to determine the correlation if the relationship between the variables is not linear. So even though, Pearson’s R might give a correlation coefficient for non-linear relationships, it might not be reliable. For example, the correlation coefficients as given by both the techniques for the relationship y=X3 for 100 equally separated values between 1 and 100 were found out to be:

Pearson′s R≈0.91

Spearman′s R≈1

And as we keep on increasing the power, the Pearson’s R value consistently drop whereas the Spearman’s R remains robust ar 1. For example, for the relationship y=X10 for the same data points, the coefficients were:

Pearson′s R≈0.66

Spearman′s R≈1

So the takeaway here is that if you have some sense of the relationship being non-linear, you should look at Spearman’s R instead or Pearson’s R. It might happen that even for a non-linear relationship, the Pearson’s R value might be high, but it is simply not reliable.

**Q10. What is the difference between Least Square Error and Mean Square Error?**

Least Square Error is the method used to find the best-fit line through a set of data points. It is. The idea behind the least squared error method is to minimize the square of errors between the actual data points and the line fitted.

Mean Square Error, on the other hand, is used once you have fitted the model and want to evaluate it. So the mean squared error finds out the average of the difference between the actual and predicted values and hence, is a good parameter to compare various models on the same data set.

Thus, LSE is a method used to minimise the sum of squares and is used during model fitting, and MSE is a metric used to evaluate the model after fitting based on the average squared errors.

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