1. Produce a residual plot for this model and paste it below. Explain what this

Question

1.Produce a residual plot for this model and paste it below. Explain what this plot tells us about the linear regression model. [Recall that when you compute the regression line using Stat – Regression – Simple Linear in StatCrunch, you can select Residuals vs. X-values under the Graphs drop-down menu to create a residual plot. Make sure your X-values are the log seed weights.]

2.For a seed weight of 100 mg, according to the model in #4, what is the predicted seed count in numbers of seeds?

3.Predict the seed count (again in numbers of seeds) for a seed weight of 10000 mg. How confident are you in this prediction? Explain.

4.Suppose a 20th data value from ``The Giving Tree” [a kids’ book by Shel Silverstein I highly recommend] was added to the data set whose seed weight was 295,894 mg and whose seed count was 8. Using your model from question 4, indicate whether this case is an outlier, whether or not it has high leverage, and whether or not it is influential. [No explanation is necessary].

Outlier?                       High Leverage?                      Influential point?

Explanation / Answer

SOlution-

Date has not been provided, but i will try to make general answers, that will help you in getting to particular answers of above questions-

A. Estimated regression equation must be-

Seed count = constant + slope * seed weight

B. When seed weight = 100mg, then seed count must be calculated as-

Seed count = constant + slope * 100

C. When seed weight = 10000mg, then seed count must be calculated as-

Seed count = constant + slope * 10000

D. If we want to know that a point whose seed weight was 295,894 mg and whose seed count was 8 from the data set is a outlier or not, we will construct a scatter plot of the whole new data and see whether that point is lying far away from rest of the other points or not to be classified as an outlier.

Answers!

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