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Low Standard Error or the aggregate standard deviation of the residuals (difference between actual Y data points versus the predicted Y values) is the goal and indicates a strong fit of the model to the actual data. With our model, we are showing only about -2%, not a strong indication of the reliability of this model for predicting stock price impact based on earnings surprises. The Adjusted R Square represents the variation that is explained in the model, with high numbers providing a better fit.
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For our model, we have a T-Stat of 0.81, which is NOT a strong indicator of the usefulness of this model. A t Stat of greater than 3 or less than -3 is very good, while less than 2 or greater than -2 would call into question your assumption that Y and X are correlated variables. The t Stat value provides an indication of the significance of the above coefficients. The impact on the price of the company’s stock will be our Y-variable.
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We shall call beating or missing Wall Street’s earnings expectations ‘earnings surprise’ and make this our X-variable. The steps outlined below will guide you in using Excel’s regression tool to see if it is reliable for measuring the impact of JP Morgan’s corporate earnings performance on the company’s closing stock price on the day of earnings Impact of corporate earnings (X) on stock price (Y)įor the purposes of this tutorial, we will cover the basics of regression modeling to explain the important aspects of the tool so that a manager can determine if the correlation that exists in the model is sufficient to rely on in order to use the model to predict future outcomes. Impact of rest from physical exertion (X) on athletic performance (Y)ĭ. Impact of political canvassing (X) on voter turnout (Y)Ĭ. Impact of advertising spend (X) on sales activity (Y)ī. Below are straightforward examples of interesting two-variable regression models:Ī. Linear regression analysis can be used to predict future values based on previously realized results.Ī simple linear regression model will have two variables where one independent variable (X) is used to predict the direction or behavior of a dependent variable (Y) in the forecast (Y = a + bX). In this newsletter, we provide a tutorial that showcases a simple analytical tool a busy manager can readily leverage in Microsoft Excel.
REGRESSION USING ANALYSIS TOOLPAK EXCEL 2016 SOFTWARE
Today, software vendors abound and are offering sophisticated tools for data mining and predictive analysis. Consumer credit card companies are using predictive analytics to understand their customers’ purchasing habits and offer tailored loyalty programs based on those decisions. E-commerce businesses are tracking customer behavior on their websites and using predictive analytics to deliver customized online offerings for their customers. Predictive analytics combined with Big Data tools are allowing business executives to leverage the historical and real-time collection of large data sets to attempt to explain future business trends. An Introduction to Predictive Analytics Using Linear Regression Analysis