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Logistic regression is one of the most popular Machine Learning algorithms, which comes under the Supervised Learning technique. It is used for predicting the categorical dependent variable using a given set of independent variables. Logistic regression predicts the output of…
Sentiment analysis is the process of identifying and extracting subjective information from text, such as opinions and emotions. There are several methods that can be used for sentiment analysis, including: Lexicon-based methods: In this method, sentiment scores are assigned to words…
Autocorrelation and autocovariance are one of the most critical metrics in financial time series econometrics. Both functions are based on covariance and correlation metrics. What is Autocorrelation? Autocorrelation measures the degree of similarity between a given time series and the…
An analytical sandbox is a testing environment that is used by data analysts and data scientists to experiment with data and explore various analytical approaches without affecting the production environment. It is a separate, isolated environment that contains a copy…
another answer with equation method
Fit a regression equation to estimate β0 ,β1 and β2 to the following data of a transport company on the weights of 6 shipments, the distances they were moved and the damage of the goods that was incurred. (10) weight…
From the following results, obtain two regression equations and estimate the yield when the rainfall is 29 cm and the rainfall when the yield is 600 kg. (10) Solution :
A residual is the difference between an observed value and a predicted value in regression analysis. Residual = Observed value – Predicted value An observation has a positive residual if its value is greater than the predicted value made by…
Dirty data refers to inaccurate, incomplete, inconsistent, or duplicate data within a dataset. It can arise from various sources such as human error during data entry, system glitches, outdated information, or issues during data migration or integration processes. Dirty data…
Box-Jenkins Methodology PHASE I : Identification and Preparation Data Preparation Differencing the data to achieve stationary series Transform data to stabalize variance Model Selection/identification Shortlist suitable models according to the data Apply ACF and PACF to determine potential models PHASE …
