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Is the line of best fit the same as linear regression?
Linear Regression is a method to generate a "Line of Best fit" yes you can use it, but it depends on the data as to accuracy, standard deviation, etc. there are other types of regression like polynomial regression.
What is the difference between simple and multiple linear regression?
I want to develop a regression model for predicting YardsAllowed as a function of Takeaways, and I need to explain the statistical signifance of the model.
Why are your predictions inaccurate using a linear regression model?
There are many possible reasons. Here are some of the more common ones: The underlying relationship is not be linear. The regression has very poor predictive power (coefficient of regression close to zero). The errors are not independent, identical, normally distributed. Outliers distorting regression. Calculation error.
How does a linear regression allow us to better estimate trends costs and other factors in complex situations?
You question is how linear regression improves estimates of trends. Generally trends are used to estimate future costs, but they may also be used to compare one product to another. I think first you must define what linear regression is, and what the alternative forecast methods exists. Linear regression does not necessary lead to improved estimates, but it has advantages over other estimation procesures. Linear regression is a mathematical procedure that calculates a "best fit" line through the data. It is called a best fit line because the parameters of the line will minimizes the sum of the squared errors (SSE). The error is the difference between the calculated dependent variable value (usually y values) and actual their value. One can spot data trends and simply draw a line through them, and consider this a good fit of the data. If you are interested in forecasting, there are many methods available. One can use more complex forecasting methods, including time series analysis (ARIMA methods, weighted linear regression, or multivariant regression or stochastic modeling for forecasting. The advantages to linear regression are that a) it will provide a single slope or trend, b) the fit of the data should be unbiased, c) the fit minimizes error and d) it will be consistent. If in your example, the errors from regression from fitting the cost data can be considered random deviations from the trend, then the fitted line will be unbiased. Linear regression is consistent because anyone who calculates the trend from the same dataset will have the same value. Linear regression will be precise but that does not mean that they will be accurate. I hope this answers your question. If not, perhaps you can ask an additional question with more specifics.
What is the purpose of a residual analysis in simple linear regression?
One of the main reasons for doing so is to check that the assumptions of the errors being independent and identically distributed is true. If that is not the case then the simple linear regression is not an appropriate model.
