Category: Econometrics
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What is Panel Data – Advantages, Types and Uses
Let’s start with the definition of Panel data. Panel data has both cross-sectional and time-series features. Why Cross-sectional? Simply because panel data is created from various cross-sectional units. And why timeseries? because information on those cross-sectional units is collected across different time periods. It can be years, months or days or any other interval. Types…
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Six Ways to Test For a Normal Distribution
What is normality? Normality is a property of a random variable that follows normal probability distribution. A normal distribution has a zero mean with one standard deviation. In a graphical format, a ‘Normal’ distribution will appear as a bell curve, symmetric about the mean. In the Classical linear Regression model or in certain statistical tests,…
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Heteroscedasticity: Detection, Problem and Remedies
What is Heteroscedasticity? One of the crucial assumption of linear regression models is that the residual or error term needs to have constant variance and this assumption is known as Homoscedasticity. However, when the residual variance is not constant we call it a case of Heteroscedasticity. Heteroscedasticity refers to the situation in which the variability…
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Methods of Sampling from a Population | Sampling Techniques | Statistics
Population and Sample: Population is all elements in a group. For example, college students in a city are a population that includes all of the college students in that city. 25-year-old people in a country is a population that includes all of the people that fits the description. It does not make sense to do…
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Autocorrelation : Detection, Problems and Remedies
What is Autocorrelation? Correlation between the error terms arising in time series data is known as autocorrelation or serial correlation. For such cases, the error term et at time period t is correlated with error terms et+1,et+2,… or et-1,et-2and so on. The first one is an example of positive autocorrelation and the second one is…
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Linear Regression: 20 Most Asked Interview Questions
1.What are the assumptions of Classical Linear Regression model? The model is linear in parameters i.e. the dependent variable (Y) should be expressed as a linear combination of the explanatory variables (Xs) and error term. The number of observations in the linear regression model is not lesser than the number of explanatory variables and any…
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Understanding Hypothesis Testing, Level of Significance and False Positives in Statistics
The primary objective of statistical inference process is to – estimate population parameter and setting up the confidence interval for those estimates testing the statistical significance. Now the terms may sound familiar if you have a background in Statistics. Even if you are a beginner, let me try to explain each of the components in…
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How to test Linearity in Parameters for Linear Regression
One of the crucial assumptions that we test for while building Ordinary Least Squared based models or Linear Regressions is linearity in parameters. Linearity simply implies that our dependent (Y) variable can be expressed as a linear function of the explanatory variables (X) we are choosing to explain the variation in the Y variable. Now…
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How to Test Performance of the Linear Regression Models
We apply linear regression techniques when we try to predict a continuous dependent variable. Hence the predicted output also becomes a continuous variable. Now let’s try to find out what are the model performance metrics that we can test or check to find out if the output looks stable and consistent. Overall Fit : When…
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Outliers | Detection, Impacts and Remedies
Right from the childhood we strive to excel in all aspects of our lives; so that we can stand out from the average population.Sadly, when our lives become mere data points for a data scientist, those outstanding achievements can be treated as ‘outliers’ in a data set. Let’s try to understand why an outlier can…