Exploring Linear Regression and Its Ethical Implications

What is the main focus of the lesson on regression and prediction?

• The history of algebra

• The use of prediction algorithms in business

• Geometry and shapes

• Basic arithmetic operations

What does a credit score represent?

• Your annual income

• The amount of money in your bank account

• How quickly and consistently you pay off debt

• The number of credit cards you own

In the context of loan approvals, what is the explanatory variable?

• Percent of loan paid off

• Credit score

• Loan amount

• Interest rate

Tags

CCSS.HSS.ID.C.9

What is the issue with a model that connects all data points directly?

• It overfits the data

• It ignores the data

• It is too simple

• It underfits the data

Tags

CCSS.HSS.ID.B.6A

What does the line of best fit represent?

• The highest data point

• The lowest data point

• The overall trend in the data

• The exact data points

Tags

CCSS.8.SP.A.2

In the equation y = mx + b, what does 'm' represent?

• The slope

• The constant term

• The x-intercept

• The y-intercept

Tags

CCSS.8.EE.B.6

CCSS.8.F.A.3

What does the y-intercept indicate in a linear model?

• The value of x when y is zero

• The average of all data points

• The slope of the line

• The value of y when x is zero

Tags

CCSS.HSF.LE.B.5

How would you predict the loan repayment percentage for a credit score of 700 using the model y = 0.074x + 26.8?

• By dividing 700 by 0.074 and adding 26.8

• By subtracting 26.8 from 700

• By multiplying 700 by 0.074 and adding 26.8

• By adding 700 to 26.8

Tags

CCSS.8.F.A.3

What is a major ethical consideration when using prediction algorithms?

• The cost of the algorithm

• Whether the algorithm is biased

• The complexity of the algorithm

• The speed of the algorithm

According to data scientist B, why might credit scores reflect biases?

• They are calculated incorrectly

• They are too simple

• They are based on random data

• They are influenced by historical discrimination