The question is in an image because I was not able to copy and paste.
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Then answer two classmates’ posts and comment on their description.
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Jenean-
1.Correlation does not imply causality means you cannot apply cause and effect between events. An example of this would be an interesting finding I came across on the site howstuffworks.com, “when ice cream sales are higher, so is the murder rate.” (Gerbis, 2021). I found it humorous because if we applied cause and effect, it would mean that someone who buys ice cream is also a murderer.
3. The methods in section 10-5 review making predictions. I believe you can use a graph to predict eye color. A prediction is not exact and factual though. If you understand recessive and dominate genes, graphing can give you a pretty good idea of the color of the eyes. This graph is called the Punnett square.
Rashan-
1. In your own words, what is meant by the statement that correlation does not imply causality (Section 10-2)?
Simply put, just because something is similar in appearance or activity does not mean that they are related or that one instance triggered another. We like to try to find patterns in things but causation and correlation isn’t the most accurate way to assess that.
2. In your own words, please describe the difference between the regression equation y= b0 b1x and the regression equation y=B0 B1x (Section 10-3).I’ll point out the visible differences first. The Beta’s are lower case versus capital. It’s indicating to us is that It would be estimating for a sample. The capital Betas would be for a population. Y hat is the predicted value. In regression, the capital Beta is used as an estimator of an actual population. Sample is an estimator of population, b
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