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In this case, the independent variable (X) is the amount spent on advertising and the dependent variable (Y) is the total sales amount. We can easily run a regression between the two variables to analyze whether there is a relationship between the two. Suppose we have the past data on the total monthly sales amount of a product and the amount spent on advertising the product. Let’s understand this with the help of an example – " Understand the F-statistic in Linear Regression.We try to regress the value of the dependent variable using the independent variables. JMP Statistical Discovery, Statistics Knowledge Portal. " Simple Linear Regression: Interpreting Regression Output." " STAT 800: Applied Research Methods General Probability Rules." Pennsylvania State University, Eberly College of Science. " STAT 501: Regression Methods 1.5 - The Coefficient of Determination, R-squared." " Use the Analysis ToolPak to Perform Complex Data Analysis." " Simple Linear Regression: Regression Model Assumptions." " Simple Linear Regression, The Chi-Square Test." " Analysis of Application of Fama-French 3-factor Model and Fama-French 5-factor Model in Manufacture Industry and Health Industry." 2020 Management Science Informatization and Economic Innovation Development Conference (MSIEID), December 2020. " Principles of Finance: 15.3 The Capital Asset Pricing Model (CAPM)." Lastly, select "Display R-squared value on chart." The visual result sums up the strength of the relationship, albeit at the expense of not providing as much detail as the table above. In the dialog box, select "Trendline" and then "Linear Trendline." To add the R 2 value, select "More Trendline Options" from the "Trendline" menu. To add a regression line, choose "Add Chart Element" from the "Chart Design" menu. We can chart a regression in Excel by highlighting the data and charting it as a scatter plot. The time period under study may not be representative of other time periods.The data is a time series, so there could also be autocorrelation.There are only 20 observations, which may not be enough to make a good inference.Visa is a component of the S&P 500, so there could be a co-correlation between the variables here.With only one variable in the model, it is unclear whether V affects the S&P 500 prices, if the S&P 500 affects V prices, or if some unobserved third variable affects both prices.However, an analyst at this point may heed a bit of caution for the following reasons: From the R-squared, we can see that the V price alone can explain more than 62% of the observed fluctuations in the S&P 500 index. This indicates that this finding is highly statistically significant, so the odds that this result was caused by chance are exceedingly low.
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