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2. There must be a linear relationship between the two variables. Create a scatterplot by plotting the two variables
against each other. The scatterplot can then be used to check for linearity. The scatterplot may look something like
one of the following:
Positive Correlation Negative Correlation No Correlation
3. In statistics, outliers are data points that are significantly different from other observations. Outliers may be due to
measurement irregularity or may indicate experimental error; the latter are sometimes excluded from the data set.
Outliers can cause serious problems in statistical analysis. The data should not have any significant outliers. Outliers
are single data points within your dataset that do not follow the usual pattern. The following scatterplots highlight
the potential impact of outliers:
r = 0.39 r = 0.69
Outlier Outlier removed
Outliers can have a great impact on the line of best fit and the Pearson correlation coefficient, leading to very difficult
inferences regarding the data. Therefore, it is best to have no outliers or keep them to a minimum.
4. The variables should be normally distributed (approximately).
Importance of data in Regression Analysis
Data plays a pivotal role in developing any AI model. Care must be taken to use only authentic data for creating an AI
model. Regression analysis is used by businesses to make data-driven decisions since it identifies the variables that have
the most influence on the outcome based on past performance. When forecasting and generating predictions based on
data, businesses can more effectively concentrate on the right things.
How Prediction Changes with Changing Data?
Properly modelling changes over time is necessary for forecasting and essential for any model or process with data that
cover multiple time periods. Prediction can be made about future events using historical data and analytics methods
like statistical modelling and machine learning. This is called predictive analytics. Future insights can be produced with
an impressive level of precision using the science of predictive analytics. Any organization may now use historical and
current data to accurately predict patterns and behaviours milliseconds, days, or years into the future with the aid of
advanced predictive analytics tools and models.
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