Hypothesis Testing Help
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Hypothesis Testing is the corner stone of Inferential Statistics, and they provide the corner stone for making statements about population parameters by using sample data. If the sample statistics are too unusual under the assumption that the null hypothesis (the hypothesis of no effect) is true, then the null hypothesis is rejected
In order to conduct inferential statistics procedures, it is necessary that certain assumptions are met. These assumptions are typically the fulfillment of the normality assumption (the samples come from normally distributed populations) and equality of variances. When any of the assumptions is not met, then alternative procedures need to be conducted, namely, Non-Parametric testing.
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We provide you help with the following:
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Parametric Tests
- Z-test and T-test for one population mean
- Z-test for one population proportion
- Z-test and T-test for two population mean
- Z-test for two population proportion
- ANOVA
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Non-Parametric Tests
- Sign Test
- Wilcoxon Signed Ranks
- Wilcoxon Rank Sum
- Mann-Whitney
- Kruskal-Wallis
- Chi-Square tests
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Our team is highly experienced in all kinds of software packages out there. We can work with graphs, and all types of advanced analysis, including syntax, do files, etc. Request your free quote. We a have a satisfaction guarantee policy. If you're not satisfied, we'll refund you. Please see our terms of service for more information about the satisfaction guaranteed policy. See also a sample of our work.
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