pollev.com for a set of rapid-fire questions on last night’s notes.pollev.com for a set of quiz-level questions pertaining to Summarizing Numerical Associations and Multiple Linear Regression.Consider the following multiple linear regression model, which will be the subject of the next three review questions.
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Call:
lm(formula = bill_depth_mm ~ bill_length_mm + body_mass_g + species,
data = penguins)
Coefficients:
(Intercept) bill_length_mm body_mass_g speciesChinstrap
10.33083 0.09484 0.00117 -0.90748
speciesGentoo
-5.80117 Which is the correct interpretation of the coefficient in front of bill length? Select all that apply.
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Call:
lm(formula = bill_depth_mm ~ bill_length_mm + body_mass_g + species,
data = penguins)
Coefficients:
(Intercept) bill_length_mm body_mass_g speciesChinstrap
10.33083 0.09484 0.00117 -0.90748
speciesGentoo
-5.80117 Which is the correct interpretation of the coefficient in front of Gentoo?
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Call:
lm(formula = bill_depth_mm ~ bill_length_mm + body_mass_g + species,
data = penguins)
Coefficients:
(Intercept) bill_length_mm body_mass_g speciesChinstrap
10.33083 0.09484 0.00117 -0.90748
speciesGentoo
-5.80117 How would this linear model best be visualized?
Consider the following linear regression output where the variable school is categorical and the variable hours_studied is numerical.
| Coefficients | Estimate |
|---|---|
(Intercept) |
2.5 |
hours_studied |
.2 |
schoolCal |
1 |
schoolStanford |
-1 |
edu dataframe which contains the minimum, median, and IQR for hours_studied among each school. In order to do this, I make use of group_by() followed by summarize(). I save this data frame into an object called GPA_summary.What are the dimensions of GPA_summary?
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