Econ 1500 Final Exam with precise detailed
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answers
SSR |(regression |sum |of |squares) |- |✔✔💯Sum |of(yhat |- |yavg)^2
SSE |(residual/error |sum |of |squares) |- |✔✔💯sum |of(y |- |yhat)^2
SS |total |(total |sum |of |squares) |- |✔✔💯sum |of(y |- |yavg)^2
coefficient |of |determination |- |✔✔💯r^2 |= |SSR/SS |Total |= |1 |- |(SSE/SS |Total)
Standard |error |of |estimate |- |✔✔💯-s(yx) |= |sqrt(sum |of(y |- |hyat)^2/(n-2))
-s(yx) |= |sqrt(SSE/(n-2))
-s(yx) |= |sqrt(residual |mean |square)
total |number |of |degrees |of |freedom |(simple |regression) |- |✔✔💯n-1
degrees |of |freedom |for |regression |(simple |regression) |- |✔✔💯1, |when |there |is |1 |independent
degrees |of |freedom |for |residual/error |(simple |regression) |- |✔✔💯n-2
total |number |of |degrees |of |freedom |(multiple |regression) |- |✔✔💯n-1
degrees |of |freedom |for |regression |(multiple |regression) |- |✔✔💯k
, degrees |of |freedom |for |residual/error |(multiple |regression) |- |✔✔💯n |- |(k+1)
MSR |or |mean |squared |regression |(simple/multiple) |- |✔✔💯SSR/k |or |SSR/df
MSE |or |mean |squared |residual/error |(simple/multiple) |- |✔✔💯SSE/(n-(k+1)) |or |SSE/df
F |- |✔✔💯MSR/MSE
global |f |test |- |✔✔💯always |a |right |tailed |test |(?)
global |f |test |null |hypothesis |- |✔✔💯H0:B1 |=B2 |=B3 |=0
global |f |test |alternate |hypothesis |- |✔✔💯H1: |Not |all |the |Bi's |are |0.
if |significance |f |is |small |- |✔✔💯f |is |large
at |least |one |of |the |variables |is |significant
if |significance |f |is |not |significant |- |✔✔💯then |delete |the |variable |with |high |p |value |& |then |run |
regression |again
if |coefficient |estimates |change |a |lot |- |✔✔💯there |is |probably |multicollinearity
clues |to |multicollinearity |- |✔✔💯1. |an |independent |variable |known |to |be |an |important |predictor |
ends |up |having |a |regression |coefficient |that |is |not |significant |
2. |A |regression |coefficient |that |should |have |a |positive |sign |turns |out |to |be |negative |or |vice |versa
3. |When |an |independent |variable |is |added |or |removed, |there |is |a |drastic |change |in |the |values |of |the |
remaining |regression |coefficients
autocorrelation |- |✔✔💯the |run |of |residuals |above |the |mean |of |the |residuals, |followed |by |a |run |
below |the |mean.
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answers
SSR |(regression |sum |of |squares) |- |✔✔💯Sum |of(yhat |- |yavg)^2
SSE |(residual/error |sum |of |squares) |- |✔✔💯sum |of(y |- |yhat)^2
SS |total |(total |sum |of |squares) |- |✔✔💯sum |of(y |- |yavg)^2
coefficient |of |determination |- |✔✔💯r^2 |= |SSR/SS |Total |= |1 |- |(SSE/SS |Total)
Standard |error |of |estimate |- |✔✔💯-s(yx) |= |sqrt(sum |of(y |- |hyat)^2/(n-2))
-s(yx) |= |sqrt(SSE/(n-2))
-s(yx) |= |sqrt(residual |mean |square)
total |number |of |degrees |of |freedom |(simple |regression) |- |✔✔💯n-1
degrees |of |freedom |for |regression |(simple |regression) |- |✔✔💯1, |when |there |is |1 |independent
degrees |of |freedom |for |residual/error |(simple |regression) |- |✔✔💯n-2
total |number |of |degrees |of |freedom |(multiple |regression) |- |✔✔💯n-1
degrees |of |freedom |for |regression |(multiple |regression) |- |✔✔💯k
, degrees |of |freedom |for |residual/error |(multiple |regression) |- |✔✔💯n |- |(k+1)
MSR |or |mean |squared |regression |(simple/multiple) |- |✔✔💯SSR/k |or |SSR/df
MSE |or |mean |squared |residual/error |(simple/multiple) |- |✔✔💯SSE/(n-(k+1)) |or |SSE/df
F |- |✔✔💯MSR/MSE
global |f |test |- |✔✔💯always |a |right |tailed |test |(?)
global |f |test |null |hypothesis |- |✔✔💯H0:B1 |=B2 |=B3 |=0
global |f |test |alternate |hypothesis |- |✔✔💯H1: |Not |all |the |Bi's |are |0.
if |significance |f |is |small |- |✔✔💯f |is |large
at |least |one |of |the |variables |is |significant
if |significance |f |is |not |significant |- |✔✔💯then |delete |the |variable |with |high |p |value |& |then |run |
regression |again
if |coefficient |estimates |change |a |lot |- |✔✔💯there |is |probably |multicollinearity
clues |to |multicollinearity |- |✔✔💯1. |an |independent |variable |known |to |be |an |important |predictor |
ends |up |having |a |regression |coefficient |that |is |not |significant |
2. |A |regression |coefficient |that |should |have |a |positive |sign |turns |out |to |be |negative |or |vice |versa
3. |When |an |independent |variable |is |added |or |removed, |there |is |a |drastic |change |in |the |values |of |the |
remaining |regression |coefficients
autocorrelation |- |✔✔💯the |run |of |residuals |above |the |mean |of |the |residuals, |followed |by |a |run |
below |the |mean.
