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# 统计代写|回归分析代写REGRESSION ANALYSIS代考|STAT346 Common Themes with Regression

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## 统计代写|回归分析代写REGRESSION ANALYSIS代考|Common Themes with Regression

Understanding correlation is a good place to start learning regression. In fact, there are several themes that I touch upon in this section that show up throughout this book.

For instance, analysts naturally want to fit models that explain more and more of the variability in the data. And, they come up with classification schemes for how well the model fits the data. However, there is a natural amount of variability that the model can’t explain just as there was in the height and weight correlation example. Regression models can be forced to go past this natural boundary, but bad things happen. Throughout this book, be aware of the tension between trying to explain as much variability as possible and ensuring that you don’t go too far. This issue pops up multiple times!

Additionally, for regression analysis, you’ll need to use statistical measures in conjunction with graphs just like we did with correlation. This combination provides you the best understanding of your data and the analytical results.

## 统计代写|回归分析代写REGRESSION ANALYSIS代考|Regression Takes Correlation to the Next Level

Wouldn’t it be nice if instead of just describing the strength of the relationship between height and weight, we could define the relationship itself using an equation? Regression analysis does just that by finding the line and corresponding equation that provides the best fit to our dataset. We can use that equation to understand how much weight increases with each additional unit of height and to make predictions for specific heights.

Regression analysis allows us to expand on correlation in other ways. If we have more variables that explain changes in weight, we can include them in the model and potentially improve our predictions. And, if the relationship is curved, we can still fit a regression model to the data.

Additionally, a form of the Pearson correlation coefficient shows up in regression analysis. R-squared is a primary measure of how well a regression model fits the data. This statistic represents the percentage of variation in one variable that other variables explain. For a pair of variables, R-squared is simply the square of the Pearson’s correlation coefficient. For example, squaring the height-weight correlation coefficient of $0.705$ produces an R-squared of $0.497$, or $49.7 \%$. In other words, height explains about half the variability of weight in preteen girls.

But we’re getting ahead of ourselves. I’ll cover R-squared in much more detail in both chapters 2 and $4 .$

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