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Though, due to its empirical applications and focus, statistics is typically regarded as a distinctive math’s sciences and not only a math’s branch (Chance et al, 2005) Therefore, in certain tasks a statistician use is less mathematical; for example, ensuring that collection of data is carried out in a way that yie...

There are higher education courses in which students will be assigned to write statistics essays

Using the countries of Bahrain and the United States as a comparison point for the following issues which surround crime statistics such as biases, agendas and general influences like education and religion; this essay will be focused around analysing the statistical factors and wider influences which can allow a country to have low or high crime rates....


Ibm Spss Statistics Essay - 1228 Words - StudyMode

Finally the essay moves on to looking at how to identify why official statistics do not reflect in today’s society and may not be totally accurate.

Our tool has significant advantages over other contemporary similar tools. First, with 18 cancer types our tool is the most comprehensive tool to date for survival analysis and can be used by researchers working on a wide array of cancer types. Secondly, our tool does not merge data coming from different studies (having different characteristics) and platforms, which may in certain situations, become erroneous. Third, an indirect advantage of this strategy is that researchers can identify study characteristics where their potential biomarkers may not work at all or may have an inverse effect. Tools like ITTACA are not primarily survival analysis tools and thus, do not have capability of producing survival plots for a lot of different cancer types and studies. ITTACA comprises data for only 7 cancer types and is capable of conducting survival analysis on only a few cancer types using data from only a limited number of studies. PrognoScan although compiles data for 14 cancer types, does not include recent major datasets such as TCGA data, and also cannot be used to study prognostic implications of multiple genes (signatures). KMplot for Breast and Ovarian Cancer suffer from inherent over fitting of data as they normalize gene expression data coming from several different studies and to pool one large patient series, in an attempt to provide meta-analysis. Merging of dataset using currently available algorithms can be performed on datasets profiled only on same platform for optimal results. For this reason, KMplot merges data coming from a single gene expression profiling platform. Although a very robust tool, this strategy in KMplot may lead to misleading results when studying biomarkers identified on other platforms. For instance, Crijns et. al. [] identified an 86 gene signature predictive of overall survival in high risk (high grade and stage) ovarian cancer patients. Gene expression in this study was profiled on custom microarray platform. We tried to plot prognostic plot for this signature using KMPlot and PROGgene separately. For 60 genes from the signature whose decreased expression is associated with higher risk (low overall survival rate), Affymetrix probe IDs (usable in KMplot) were available for only 30 genes. Using these probe Ids KMplot failed to produce statistically significant KM plot for this group of genes (P > 0.1) using high stage (3 + 4) and grade (3) as study parameters. For the same signature we also performed survival analysis using KMplot for the group of genes whose higher expression is associated with high risk. This analysis also failed to produce statistically significant results. For the same group of genes, using PROGgene we were able to produce a significant prognostic plot using datasets which comprise of gene expression profiling of high stage and grade ovarian cancer patients (GSE32062 and TCGA, see Additional file ).