A summary of the UU course 'statistics', containing information on all important statistics subjects, like hypothesis testing, the z-value, the mean, standard deviation, variance, regressions, and significance tests.
Setup
Voor tutorial woensdag: opdrachten voor de woensdag tutorial maken en nakijken
en door de theorie bladeren en besluiten of ik naar de tutorial zal gaan
Woensdag: tutorial niet verplicht want ik ben repeater, dus ik kan elke groep
joinen die ik wil
Na tutorial woensdag, voor tutorial vrijdag: opdrachten voor de vrijdag tutorial
maken en nakijken en door de theorie bladeren en besluiten of ik naar de tutorial zal
gaan
Vrijdag: tutorial niet verplicht want ik ben repeater, dus ik kan elke groep joinen
die ik wil
Week 8
Nog te maken opdrachten: 11.27 (e-f), 11.28, 11.29, 11.31, 11.33, 11.35(a), 11.37, 11.41,
11.45, 11.83(a-e), 11.93, 11.95(a&b)
, Week 7
The least squares regression line is y-hat = b0 + b1x
b1 being the slope of the line = Cov (x, y) / s2x
b0 being the y-intercept = y-bar – b1x-bar
The least squares procedure estimates b0 and b1 with the lowest sum of the squared
residuals.
b1 = (sum of (xi – x)(yi – y)) / (sum of (xi – x)2) = rxy * sy / sx
The difference between the residual and the error term is that the error term also takes b 0
and b1 into account.
SST = total variability in a regression analysis = SSR + SSE = sum of (y i – y-bar)2
SSE = sum of e2i
SSR = b2i * sum of (xi – x-bar)2
R2 = SSR / SST = 1 – SSE / SST = r2xy. This varies from 0 to 1, the higher the value, the better
the regression.
Estimation of the model error variance = SSE / (n-2)
Say r = sample correlation coefficient
H0 rho = 0, reject if:
For H1 rho > 0 (r * wortel (n-2)) / wortel (1 – r2) > tn-2, a
For H1 rho < 0 (r * wortel (n-2)) / wortel (1 – r2) < -tn-2, a
For H1 rho =not 0 (r * wortel (n-2)) / wortel (1 – r2)
is either smaller than -tn-2, a/2 or greater than tn-2, a/2
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