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Qualified Medicare Beneficiary (QMB) 3200 exam 2 test formulas with All Questions and 100% Correct Answers $8.00   Add to cart

Exam (elaborations)

Qualified Medicare Beneficiary (QMB) 3200 exam 2 test formulas with All Questions and 100% Correct Answers

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  • Qualified Medicare Beneficiary 3200
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  • Qualified Medicare Beneficiary 3200

Qualified Medicare Beneficiary (QMB) 3200 exam 2 test formulas with All Questions and 100% Correct Answers

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  • October 21, 2024
  • 23
  • 2024/2025
  • Exam (elaborations)
  • Questions & answers
  • Qualified Medicare Beneficiary 3200
  • Qualified Medicare Beneficiary 3200
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25 Multiple choice questions

Definition 1 of 25
sigma (yi - mean y)^2

sum of squares due to regression (SSR)

Sum of squares due to error

Total Sum of Squares (SST)

least squares criterion

Definition 2 of 25
s = sqrt(MSE) = sqrt(SSE / n - 2)

Standard error of the estimate

standard deviation of b1

Mean square error

standard error of mean x1 - mean x2

Definition 3 of 25
t = (mean x1 - mean x2) - D_0 / sqrt(s1^2 / n1 + s2^2 / n2)

test statistic for hypothesis tests involving matched samples


Interval estimate of the difference between 2 population means: sigma1 and sigma2
known


test statistic for hypothesis tests about u1 - u2: sigma 1 and sigma2 unknown

interval estimate of the difference between 2 population means: sigma1 and sigma2
unknown

,Definition 4 of 25
SSE = sigma (yi - ^yi)^2

Sum of squares due to error


sum of squares due to regression (SSR)

Sample correlation coefficient


least squares criterion

Definition 5 of 25
t = mean d - u_d / s_d / sqrt(n)

point estimator of the difference between 2 population means

test statistic for hypothesis tests about u1 - u2: sigma 1 and sigma2 unknown

y-intercept estimated regression equation


test statistic for hypothesis tests involving matched samples

Definition 6 of 25
min sigma(yi - mean ^y)^2

sum of squares due to regression (SSR)

Sum of squares due to error


Mean square regression

least squares criterion

Definition 7 of 25
b0 = mean y - b1 * mean x

Sample correlation coefficient


sum of squares due to regression (SSR)

y-intercept estimated regression equation

Sum of squares due to error

, Definition 8 of 25
sigma_b1 = sigma / sqrt(sigma(xi - mean x)^2)

standard error of mean x1 - mean x2


standard deviation of b1

Mean square regression

Total Sum of Squares (SST)

Definition 9 of 25
s^2 = sqrt(MSE) = SSE / n - 2

least squares criterion


Mean square regression

Mean square error

Standard error of the estimate

Definition 10 of 25
mean x1 - mean x2 +- z_signficance / 2 * standard error of mean x1 - mean x2

df: t distribution with 2 independent random samples

interval estimate of the difference between 2 population means: sigma1 and sigma2
unknown

point estimator of the difference between 2 population means

Interval estimate of the difference between 2 population means: sigma1 and sigma2
known

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