BUA 345 - Lecture 8
Introduction to Regression Modeling in R
2025-02-05
💥 Lecture 8 In-class Exercise - Q1 💥
Many people think that the best movies come at the end of the year, but there are always summer blockbuster movies too.
Based on this scatterplot created from 2024 data, do you think there is a linear correlation between time of year and the daily gross from top 10 movies?
Functions are Mathematical relationships
- Every point is exactly on the line
- No points are above or below the line
- BOTH the points and the line were generated with the same function
\[ y = x^2 \]
Function of a LINE
- While covering functions, a common topic is the function of a line
\[y = mx + b\]
m is the slope of the line
b is the y-intercept
Examples:
Positive slope: \(y = 2x + 3\)
Negative slope: \(y = -3x + 7\)
Notice the Y axis is each plot.
Positive slope: \(y = 2x + 3\)
Negative slope: \(y = -3x + 7\)
Models ARE NOT Functions
Favorite Quote attributed to George Box:
“All models are wrong, but some are useful.”
Common student query:
If all models are wrong, why do we bother modeling?
Models are considered ‘wrong’ because they simplify the ‘messiness’ of the real world to a mathematical relationship.
Models can’t (and shouldn’t) include all the noise of real world data
- BUT models are still useful in understanding how variables are related to each other.
Examples of Models of Noisy Data
No. of Bedrooms helps explain selling price
MANY other factors effect selling price
Location
Size
Age
Mileage helps explain resale price
MANY other factors effect resale price
Model
Maintenance and Climate
One More Example
Years of Education helps explain income
Many other factors do too:
Major
College
Employer
So what do we do about all this noise?
As Box would say, we “worry selectively”
A strong relationship is still useful and informative
In a later lecture will talk about adding more variables to a model.
💥 Lecture 8 In-class Exercises - Q2 💥
The following is an example of a recipe for Russian Tea Cakes
💥 Lecture 8 In-class Exercises - Q2 Cont’d 💥
To make Russian Tea Cake Cookies, you need 6 tablespoons of powdered sugar to make 3 dozen cookies.
Here is the full recipe.
Here is the equation (y-intercept = 0):
\(y = 6x\)
Is this a function or a model?
💥 Lecture 8 In-class Exercises - Q3 💥
Star Wars Character Data Example
💥 Lecture 8 In-class Exercises - Q3 Cont’d 💥
The plot and model show the relationship between height and mass for all Star Wars characters for whom data were available.
Questions 3: Is the relationship shown here a model or a function?
Follow up Question (not on Point Solutions): What is a good way to determine this?
Simple Linear Regression Model
True Population Model
\[y_{i} = \beta_{0} + \beta_{1}x_{i} + e_{i}\]
\(\beta_{0}\) is the y-intercept
\(\beta_{1}\) is the slope
\(e\) is the unexplained variability in Y
Estimated Sample Data Model
\[\hat{y} = b_{0} + b_{1}x\]
\(\hat{y}\) is model estimate of y from x
\(b_{0}\) is model estimate of y-intercept
\(b_{1}\) is model estimate of slope
Each \(e_{i}\) is a residual.
y obs. - reg. estimate of y
\(e_{i} = y_{i} - \hat{y}_{i}\)
Software estimates model with smallest sum of all squared residuals
- minimizes \(\sum_{i=1}^ne_{i}^2\)
Function of a Line vs. Regression Model
Function of a Line
\[y = mx + b\]
Exact precise mathmatical relationship with NO NOISE
Regression Model Equation
\[\hat{y} = b_{0} + b_{1}x\] Estimated line that is simultaneously as close as possible to all observations.
Interpreting a Regression Model
\[\hat{y} = b_{0} + b_{1}x\]
\(\hat{y}\) is regression est. of y
\(b_{0}\) is value of y when X = 0
- NOT always meaningful
\(b_{1}\) is change in y due to 1 unit change in x.
- unit depends on data
NOTE:
Model is only valid for the range of X values used to estimate it.
Using a model to outside of this range is extrapolation.
- Extrapolated estimates are invalid
💥 Lecture 8 In-class Exercises - Q4-Q5 💥
Regression Model:
\[\hat{y} = 33.8641 - 0.022417x\]
Question 4. Based on this model, if Horsepower (x) is increased by 1, what is the change in Highway MPG?
- Round answer to six decimal places
Question 5. Based on this model, if Horsepower (x) is increased by 20 (which is more realistic), what is the change in Highway MPG?
- Round answer to 3 decimal places.
💥 Lecture 8 In-class Exercises - Q6-Q7 💥
Regression Model:
\[\hat{y} = 33.8641 - 0.022417x\]
Question 6. If HP is 600, what is the estimated Highway MPG?
Question 7. What is the residual for the 2016 Aston Martin Vantage
- Follow up Question (not on Point Solutions): Does the intercept have a real-world interpretation in this model.