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Linear Regression - Machine Learning

Angel Intriago 10 questions

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Questions in this quiz (10)

  1. 1Which of the following statements is CORRECT?
    1. AStepwise method selects relevant variables but it does not check correlations between explanatories
    2. BThe statistical test to check individual significance of variables is called p-value
    3. CThe minimum sample size for Linear Regression is at least n (number of observations) >30*k observations (k=number of explanatories)
    4. DNO ANSWER
    5. EReal value for Random perturbation is revealed when we estimate a model: it is the sample error
  2. 2What is the main difference between R squared and Adjusted R squared?
    1. AThey are similar metrics and either of both are valid to measure model global significance
    2. BAdjusted R squared takes into account degrees of freedom
    3. CThe adjusted R squared refers to the model without constant term
    4. DNO ANSWER
    5. EOne measures the model fit and the other the model goodness
  3. 3 In the following linear regression, the LOGARITHM of the Household prices in several areas within a city is explained by:



    - The amount of Household Taxes (Taxes) per person on the area

    - Allocation of the Household to a South Area (1=Yes, 0=No)

    - Residents average income (RAI)

    - Logarithm of Number of schools in the area (Log of Schools)

    Which one is the CORRECT answer, given a 95% confidence level (unless otherwise stated), considering the effects holding the other variables fixed and decimals are represented with a comma (","):    Image for question 3
    1. AOne more unit on Taxes variable produces at most an increase of 30% on the dependent
    2. BIf we increased our confidence level to 99%, none of the explanatories would be significant
    3. CNO ANSWER
    4. DNONE OF THE OTHERS
    5. EOne more unit on Schools variable produces an average of 35% increase in the price of Households
  4. 4In the following linear regression, the LOGARITHM of the Household prices in several areas within a city is explained by:



    - The amount of Household Taxes (Taxes) per person on the area

    - Allocation of the Household to a South Area (1=Yes, 0=No)

    - Residents average income (RAI)

    - Logarithm of Number of schools in the area (Log of Schools)

    Which one of the following is CORRECT, given a 95% confidence level (unless otherwise stated), considering the effects holding the other variables fixed and decimals are represented with a comma (","):    Image for question 4
    1. ANO ANSWER
    2. BThe most relevant variable in explaining the dependent is Taxes
    3. CPenalty of Household prices for living in the south is between 2% and 6%.
    4. DElasticity of Residents Average Income (RAI) with Household price is in the range [0.01 to 0.29]
    5. E1% increase on Schools variable produces at least 5% increase in Household prices
  5. 5Which of the following components is NOT used when building Beta Confidence Intervals?
    1. ABeta Coefficients
    2. BStandardized Beta Coefficients
    3. CNO ANSWER
    4. DThreshold value of t-student test for a given confidence level
    5. EStandard Deviation of Beta Coefficients
  6. 6Regarding statistical assumptions in Linear Regression
    1. AAuto-correlation is having at least a few explanatory variables highly correlated
    2. BMulticollinearity is normally not corrected after standardizing the variables
    3. CNO ANSWER
    4. DHeteroscedasticity occurs when explanatory variables all have unequal variances
    5. EMulticollinearity is having residuals dependent on lagged residuals
  7. 7What sentence is CORRECT regarding explanatory variables in Linear Regression?
    1. ALogarithm transformation of explanatories does not affect the interpretion of its Beta Coefficients
    2. BExplanatory variables should be standardized
    3. CExplanatory variables should follow a normal distribution
    4. DNone of the others
    5. ENO ANSWER
  8. 8Which of the following sentences is FALSE?
    1. At-student's p-value lower than 0.05 means variable is significant with 95% confidence level
    2. BEvery time a new variable is added to a model, beta coefficients will change
    3. CRemoving a variable from a model does not affect the individual significance of the remaining ones
    4. DLinear Regression allows to isolate effect of individual variables
    5. ENo Answer
  9. 9According to the following output from a Linear Regression in which Y variable is in levels, selects the CORRECT answer ceteris paribus and with a 95% CL.Image for question 9
    1. A1% increase on the variable X1 decreases the variable Y by an average of 1.03 units.
    2. BOne more unit on the variable X1 decreases the variable Y by an average of 103.76%.
    3. CNO ANSWER
    4. DOne more unit on the variable X2 increases the variable Y by an average of 30%.
    5. E1% increase on the variable X2 decreases the variable Y by an average of 0.3 units.
  10. 10Regarding Beta parameters, which one is the CORRECT statement?
    1. ANO ANSWER
    2. BIn some circumstances, a non-significant variable might not have the value 0 (zero) within the confidence interval of its Beta coefficient
    3. CThe higher the estimated Beta parameter's dispersion, the wider its confidence interval
    4. DIf Beta estimated parameter for X1 variable is 10 and Beta parameter for X2 is 5, then X1 is twice more important than X2
    5. EBeta Confidence Interval is better calculated with Beta Standardized coefficients

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Informática Quiz on Linear Regression - Machine Learning, created by Angel Intriago on 12/12/2021.
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