WGU C207 QUM2 TASK 1
COMPREHENSIVE QUESTIONS AND
SOLUTIONS UPDATED STUDY RESOURCE
◉ ei (residual values)
Answer: yi - ŷ i
(observed y values minus calculated y values)
◉ var[β̂ 1] (variance of beta 1) A
nswer: MSE / ∑(xi−x̄ )2
(mean of all the residuals squared, divided by the sum of X
differences squared)
◉ MSE (mean standard error)
Answer: ∑(yi−ŷ i)2 / n-2
(sum of all residuals squared [otherwise known as SSE], divided
by your degrees of freedom)
, (in SLR, df will always be the number of values in a column - 2,
because you only have 2 parameters)
◉ SSE (sum of squares error)
Answer: ∑(yi−ŷ i)2
(sum of all squared residual values)
◉ SSR (sum of squares regression)
Answer: ∑(ŷ i-ȳ )2
(sum of all squared [expected y values minus mean y value])
◉ SST (sum of squares total)
Answer: SSE + SSR
◉ t-statistic for SLR
Answer: β̂ 1 / SEβ̂ 1
(calculated beta 1 divided by the standard error of calculated beta
1. note: use df to find p value]
◉ SEβ̂ 1 A
nswer: square root of var[β̂ 1]
COMPREHENSIVE QUESTIONS AND
SOLUTIONS UPDATED STUDY RESOURCE
◉ ei (residual values)
Answer: yi - ŷ i
(observed y values minus calculated y values)
◉ var[β̂ 1] (variance of beta 1) A
nswer: MSE / ∑(xi−x̄ )2
(mean of all the residuals squared, divided by the sum of X
differences squared)
◉ MSE (mean standard error)
Answer: ∑(yi−ŷ i)2 / n-2
(sum of all residuals squared [otherwise known as SSE], divided
by your degrees of freedom)
, (in SLR, df will always be the number of values in a column - 2,
because you only have 2 parameters)
◉ SSE (sum of squares error)
Answer: ∑(yi−ŷ i)2
(sum of all squared residual values)
◉ SSR (sum of squares regression)
Answer: ∑(ŷ i-ȳ )2
(sum of all squared [expected y values minus mean y value])
◉ SST (sum of squares total)
Answer: SSE + SSR
◉ t-statistic for SLR
Answer: β̂ 1 / SEβ̂ 1
(calculated beta 1 divided by the standard error of calculated beta
1. note: use df to find p value]
◉ SEβ̂ 1 A
nswer: square root of var[β̂ 1]
