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MGT6203 > Week1 - A - Steps in Regression Analysis > Flashcards

Week1 - A - Steps in Regression Analysis Flashcards

1
Q

Statement of the Problem -1

Linear regression attempts to model the _______ between two variables by fitting a ______ _______ to observed data.

A

Statement of the Problem -1

Linear regression attempts to model the relationship between two variables by fitting a linear equation to observed data.

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2
Q

Statement of the Problem/General -2

True-False: Linear Regression is a supervised machine learning algorithm.

A) TRUE
B) FALSE

A

Statement of the Problem/General - 2

Solution: (A)

Yes, Linear regression is a supervised learning algorithm because it uses true labels for training. Supervised learning algorithm should have input variable(s) (x) and an output variable (Y) for each example.

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3
Q

Statement of the Problem/General -3

Which of the following methods do we use to find the best fit line for data in Linear Regression?

A) Least Square Error
B) Maximum Likelihood
C) Logarithmic Loss

D) Both A and B

A

Statement of the Problem/General -3

Solution: (A)

In linear regression, we try to minimize the least square errors of the model to identify the line of best fit.

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4
Q

Statement of the Problem/General - 4

Which of the following is true about Residuals ?

A) Lower is better
B) Higher is better
C) A or B depend on the situation
D) None of these

A

Statement of the Problem/General - 4

Solution: (A)

Residuals refer to the error values of the model. Therefore lower residuals are desired.

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5
Q

Statement of the Problem/General - 5

Name 2 goals of creating a problem statement in a Regression Analysis excercise.

A

Statement of the Problem/General - 5

  • Outline the problem , variables and potential responses
  • If regression is the appropriate method for analysis
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6
Q

Statement of the Problem/General - 6

There are 9 steps outlined to conduct a regression Analysis excercise. Define Step 1:

A

Statement of the Problem/General - 6

  1. Statement of the problem
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7
Q

Statement of the Problem/General - 7

Define the next step in regression analysis exercise

  1. Statement of the problem
A

Statement of the Problem/General - 7

  1. Statement of the problem
  2. Using regression for: Diagnostic, Predictive, or Prescriptive analytics?
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8
Q

Steps in Regression Analysis

Define the next step in a regression analysis exercise.

  1. Statement of the problem
  2. Using regression for: Diagnostic, Predictive, or Prescriptive analytics?
A

Steps in Regression Analysis

  1. Define the next step in regression analysis exercise
  2. Statement of the problem
  3. Using regression for: Diagnostic, Predictive, or Prescriptive analytics?
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9
Q

Steps in Regression Analysis

Define the next step in a regression analysis exercise.

  1. Statement of the problem
  2. Using regression for: Diagnostic, Predictive, or Prescriptive analytics?
    ​
  3. Selection of potentially relevant response and explanatory variables
A

Steps in Regression Analysis

  1. Statement of the problem
  2. Using regression for: Diagnostic, Predictive, or Prescriptive analytics?
    ​
  3. Selection of potentially relevant response and explanatory variables
  4. Data collection (Internal data external data, purchased data, experiments, etc)
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10
Q

Steps in Regression Analysis

Define the next step in a regression analysis exercise.

  1. Statement of the problem
  2. Using regression for: Diagnostic, Predictive, or Prescriptive analytics?
    ​
  3. Selection of potentially relevant response and explanatory variables
  4. Data collection (Internal data external data, purchased data, experiments, etc)
A

Steps in Regression Analysis

  1. Statement of the problem
  2. Using regression for: Diagnostic, Predictive, or Prescriptive analytics?
    ​
  3. Selection of potentially relevant response and explanatory variables
  4. Data collection (Internal data external data, purchased data, experiments, etc)
  5. Choice of fitting method: Ordinary least squares(OLS), Generalized least squares, MLE, etc)
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11
Q

Steps in Regression Analysis

Define the next step in a regression analysis exercise.

  1. Statement of the problem
  2. Using regression for: Diagnostic, Predictive, or Prescriptive analytics?
    ​
  3. Selection of potentially relevant response and explanatory variables
  4. Data collection (Internal data external data, purchased data, experiments, etc)
  5. Choice of fitting method: Ordinary least squares(OLS), Generalized least squares, MLE, etc)
A

Steps in Regression Analysis

  1. Statement of the problem
  2. Using regression for: Diagnostic, Predictive, or Prescriptive analytics?
    ​
  3. Selection of potentially relevant response and explanatory variables
  4. Data collection (Internal data external data, purchased data, experiments, etc)
  5. Choice of fitting method: Ordinary least squares(OLS), Generalized least squares, MLE, etc)
  6. Model fitting
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12
Q

Steps in Regression Analysis

Define the next step in a regression analysis exercise.

  1. Statement of the problem
  2. Using regression for: Diagnostic, Predictive, or Prescriptive analytics?
    ​
  3. Selection of potentially relevant response and explanatory variables
  4. Data collection (Internal data external data, purchased data, experiments, etc)
  5. Choice of fitting method: Ordinary least squares(OLS), Generalized least squares, MLE, etc)
  6. Model fitting
A

Steps in Regression Analysis

  1. Statement of the problem
  2. Using regression for: Diagnostic, Predictive, or Prescriptive analytics?
    ​
  3. Selection of potentially relevant response and explanatory variables
  4. Data collection (Internal data external data, purchased data, experiments, etc)
  5. Choice of fitting method: Ordinary least squares(OLS), Generalized least squares, MLE, etc)
  6. Model fitting
  7. Model validation (diagnostics)
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13
Q

Steps in Regression Analysis

Define the next step in a regression analysis exercise.

  1. Statement of the problem
  2. Using regression for: Diagnostic, Predictive, or Prescriptive analytics?
    ​
  3. Selection of potentially relevant response and explanatory variables
  4. Data collection (Internal data external data, purchased data, experiments, etc)
  5. Choice of fitting method: Ordinary least squares(OLS), Generalized least squares, MLE, etc)
  6. Model fitting
  7. Model Validation (diagnostics)
A

Steps in Regression Analysis

  1. Statement of the problem
  2. Using regression for: Diagnostic, Predictive, or Prescriptive analytics?
    ​
  3. Selection of potentially relevant response and explanatory variables
  4. Data collection (Internal data external data, purchased data, experiments, etc)
  5. Choice of fitting method: Ordinary least squares(OLS), Generalized least squares, MLE, etc)
  6. Model fitting
  7. Model Validation (diagnostics)
  8. Refine the model and iterate from step 3
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14
Q

Steps in Regression Analysis

Define the next step in a regression analysis exercise.

  1. Statement of the problem
  2. Using regression for: Diagnostic, Predictive, or Prescriptive analytics?
    ​
  3. Selection of potentially relevant response and explanatory variables
  4. Data collection (Internal data external data, purchased data, experiments, etc)
  5. Choice of fitting method: Ordinary least squares(OLS), Generalized least squares, MLE, etc)
  6. Model fitting
  7. Model Validation (diagnostics)
  8. Refine the model and iterate from step 3
A

Steps in Regression Analysis

  1. Statement of the problem
  2. Using regression for: Diagnostic, Predictive, or Prescriptive analytics?
    ​
  3. Selection of potentially relevant response and explanatory variables
  4. Data collection (Internal data external data, purchased data, experiments, etc)
  5. Choice of fitting method: Ordinary least squares(OLS), Generalized least squares, MLE, etc)
  6. Model fitting
  7. Model Validation (diagnostics)
  8. Refine the model and iterate from step 3
  9. Use of the model
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15
Q

Business Examples:

Name X-independent Variable(s) and possible Dependent Variable

  • odometer reading
  • age of car
  • Used car price
  • condition
A
  • odometer reading - X-independent Variable
  • age of car - X-independent Variable
  • Used car price - Y - Dependent Variable
  • condition - X-independent Variable
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16
Q

Business Examples:

Name X-independent Variable(s) and possible Y-Dependent Variable

  • Sale price of house
  • square feet
  • # of bedrooms
  • location
A
  • Sale price of house - Y-Dependent Variable
  • square feet - X-independent Variable
  • # of bedrooms - X-independent Variable
  • location - X-independent Variable
17
Q

Business Examples:

Name X-independent Variable(s) and possible Y-Dependent Variable

  • Will customer default on a loan?
  • credit balance
  • income
  • age
A
  • Will customer default on a loan? - Y-Dependent Variable
  • credit balance - X-independent Variable
  • income - X-independent Variable
  • age - X-independent Variable

MGT6203 (3 decks)

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