Advance Stat Part 2

Front 1

Compares two independent sample

Back 1

T-test independent sample (para)

Mann-Whitney u (nonpara)

Wilcoxon rank sum(non para)

Front 2

Examine a set of differences

Back 2

(para) paired T test

(Non para) Wilcoxon matched pairs signed ranks

Front 3

Assesed the linear association between variables

Back 3

(Para)Pearson correlation coefficient

(non para) spearman rank correlation coefficient

Front 4

Compares three or more groups

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One way analysis of varience (F test) (para)

Kruscal wallis analysis of varience by ranks (non para)

Front 5

Compaired groups classified by two different factors

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(Para) Kruskal wallis analysis of variance by ranks

(nonpara) Friedman two way analysis of varience

Front 6

is a non-parametric test, so it does not assume any assumptions related to the distribution of scores. There are, however, some assumptions that are assumed1. The sample drawn from the population is random.2. Independence within the samples and mutual independence is assumed. That means that an observation is in one group or the other (it cannot be in both).3. Ordinal measurement scale is assumed.

Back 6

Man whitney U

Front 7

requires two repeated measurements on a commensurate scale, that is, that the values of both observations can be compared. If the variable is interval or ratio scale, the differences between both samples need to be ordered and ranked before conducting

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Wilcoxon sign test

Front 8

1. Dependent samples – the two samples need to be dependent observations of the cases. The test assess for differences between a before and after measurement, while accounting for individual differences in the baseline.2. Independence – assumes independence, meaning that the paired observations are randomly and independently drawn.3. Continuous dependent variable – Although the test ranks the differences according to their size and is therefore a non-parametric test, it assumes that the measurements are continuous in theoretical nature. To account for the fact that in most cases the dependent variable is binominal distributed, a continuity correction is applied.4. Ordinal level of measurement – The test needs both dependent measurements to be at least of ordinal scale. This is necessary to ensure that the two values can be compared, and for each pair, it can be said if one value is greater, equal, or less than the other.

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The Wilcoxon Sign test makes four important assumptions:

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Assumption of Independence: you need two independent, categorical groups that represent your independent variable. In the above example of test scores “males” or “females” would be your independent variable.Assumption of normality: the dependent variable should be approximately normally distributed. The dependent variable should also be measured on a continuous scale. In the above example on average test scores, the “test score” would be the dependent variable.Assumption of Homogeneity of Variance: The variances of the dependent variable should be equal.

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Assumptions for the Independent Samples T Test

Front 10

is a statistical procedure used to determine whether the mean difference between two sets of observations is zero.

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Paired sample t test

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The dependent variable must be continuous (interval/ratio).• The observations are independent of one another.• The dependent variable should be approximately normally distributed.• The dependent variable should not contain any outliers.

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The paired sample t-test has four main assumptions:

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Used to determine if there are differences on a dichotomous dependent variable between two RELATED GROUPS Non-Parametric (distribution-free) Test A statistical test used on paired nominal data It is applied using a 2x2 contingency table with the dichotomous variable at time 1 and time 2

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Mcnemar