T-tests
... Sometimes referred to as a Z-test since t is normally distributed for large samples. In fact for n > 120 it is OK to consult the Z table to obtain the probability The obtained value of t and its significance depend on (1) the size of the mean differences (2) the amount of variability within each sam ...
... Sometimes referred to as a Z-test since t is normally distributed for large samples. In fact for n > 120 it is OK to consult the Z table to obtain the probability The obtained value of t and its significance depend on (1) the size of the mean differences (2) the amount of variability within each sam ...
FinalF2010MA200_A
... a) The null hypothesis is the statement of no difference b) We may fail to reject the null hypothesis if the data is somewhat close to the hypothesized value. c) We may accept the null hypothesis if the sample obtained is close to the hypothesized value. d) The null hypothesis is the value that we a ...
... a) The null hypothesis is the statement of no difference b) We may fail to reject the null hypothesis if the data is somewhat close to the hypothesized value. c) We may accept the null hypothesis if the sample obtained is close to the hypothesized value. d) The null hypothesis is the value that we a ...
Document
... you have a related-measures or matched samples design. You use a related-measures design by matching pairs of different subjects in terms of some uncontrolled variable that appears to have a considerable impact on the dependent variable. ...
... you have a related-measures or matched samples design. You use a related-measures design by matching pairs of different subjects in terms of some uncontrolled variable that appears to have a considerable impact on the dependent variable. ...
QT1 exam answers
... population parameter say θ as defined by the probability density function f(x1, x2, x3,… |θ). But in an estimation situation the x’s are known and θ is unknown. If we take the x’s as parameters but θ as unknown the function f becomes a likelihood function denoted by L((x1, x2, x3,… |θ). ML estimator ...
... population parameter say θ as defined by the probability density function f(x1, x2, x3,… |θ). But in an estimation situation the x’s are known and θ is unknown. If we take the x’s as parameters but θ as unknown the function f becomes a likelihood function denoted by L((x1, x2, x3,… |θ). ML estimator ...
Charita Pearson
... Confidence interval – an interval estimate for which there is a specified degree of certainty that the actual value of the pop. Parameter will fall within the interval. Confidence co-efficient – for a confidence interval, the proportion of such intervals that would include the pop. parameter if the ...
... Confidence interval – an interval estimate for which there is a specified degree of certainty that the actual value of the pop. Parameter will fall within the interval. Confidence co-efficient – for a confidence interval, the proportion of such intervals that would include the pop. parameter if the ...
Hypothesis Testing
... hypothesized value under the null hypothesis. Intuitively, if our sample-based estimate is “far away” from the hypothesized value assuming the null hypothesis is true, we will reject the null hypothesis in favor of the alternative or research hypothesis. Extreme test statistic values occur when our ...
... hypothesized value under the null hypothesis. Intuitively, if our sample-based estimate is “far away” from the hypothesized value assuming the null hypothesis is true, we will reject the null hypothesis in favor of the alternative or research hypothesis. Extreme test statistic values occur when our ...
9.1 Sampling Distributions (new)
... How do we counteract this problem? We take a whole bunch o samples, hence sampling distributions. ...
... How do we counteract this problem? We take a whole bunch o samples, hence sampling distributions. ...
OBJECTIVES
... If scores are similar….they have low variability (homogeneous) If scores are dissimilar…they have high variability (heterogeneous) Two sets of scores may have the exact same mean but one set may have low variability and the other very high….therefore… measures of variability help DESCRIBE these diff ...
... If scores are similar….they have low variability (homogeneous) If scores are dissimilar…they have high variability (heterogeneous) Two sets of scores may have the exact same mean but one set may have low variability and the other very high….therefore… measures of variability help DESCRIBE these diff ...
