Stat Maker Program
Stat Maker Program - Related Videos
11:19
Merging Absolute and Relative Quantitative PCR Data to Quantify STAT3 Splice Variant Transcripts
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Cited by 2 •
2016
Tandem splicing events occur at sites less than 12 nucleotides apart. Quantifying ratios of such splice variants is feasible using an absolute quantitative PCR approach. This manuscript describes how splice variants of the gene STAT3, in which two splicing events results in Serine-701 inclusion/exclusion and α/β C-termini, can be quantified.
05:56
Polysome Profiling without Gradient Makers or Fractionation Systems
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Cited by 5 •
2021
This protocol describes how to generate a polysome profile without using automated gradient makers or gradient fractionation systems.
07:29
Programmed Electrical Stimulation in Mice
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Cited by 48 •
2010
Programmed electrical stimulation provides the ability to determine conduction properties of the heart, and the possibility to induce and terminate cardiac arrhythmias using various pacing protocols. Using a transvenous catheter, intracardiac electrogram recordings can be obtained in mice following programmed electrical stimulation protocols to identify arrhythmogenic substrates.
01:29
Global Marketing Programs
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2025
Global marketing strategies play a vital role in how businesses operate in international markets, determining the balance between maintaining brand consistency and adapting to local preferences. Two primary approaches are Standardized Global Marketing and Adapted Global Marketing, each with distinct advantages depending on the business goals and market environment. Standardized global marketing involves using a uniform marketing strategy across all international markets. This includes...
08:42
Skeletal Phenotype Analysis of a Conditional Stat3 Deletion Mouse Model
0 Views •
Cited by 4 •
2020
This protocol describes a canonical method to understand the critical genes controlling osteoclast activity in vivo. This method uses a transgenic mouse model and some canonical techniques to analyze skeletal phenotype.
FAQs
The program places observed counts alongside predicted values for each genetic category, making the size and direction of discrepancies visible. It then evaluates those discrepancies statistically rather than treating any numerical difference as meaningful. This comparison helps determine whether an inheritance or segregation pattern remains consistent with the proposed expectation when chance variation is considered.
Chi-square analysis converts differences between observed and expected counts into a statistical measure that can be interpreted in relation to chance. Stat Maker performs the routine calculations, allowing users to focus on whether the experimental pattern provides reasonable support for a genetic hypothesis. The result is more informative than inspecting raw counts alone.
Interpretation depends on the observed counts, the expected genetic pattern, and the magnitude of their differences. A result cannot be understood from a single count in isolation because the program evaluates the full set of categories together. Clear predictions and accurately organized observations therefore help produce a meaningful assessment of inheritance or segregation data.
A visual match between experimental counts and a predicted ratio is only an initial observation. Statistical analysis asks whether the departures from that prediction are plausibly explained by chance. Stat Maker supports this second step by applying chi-square calculations, helping users distinguish an approximately fitting result from evidence that may not agree with the proposed genetic expectation.
Users should first arrange the experimental observations as counts for the relevant genetic categories and identify the corresponding predicted values. Keeping observed and expected data clearly associated prevents category mismatches during analysis. Once the entries are organized, Stat Maker can carry out the statistical calculation and return evidence that supports interpretation of the experimental result.
In genetics, the program can be applied to inheritance, segregation ratios, and trait transmission studies. It helps convert collected counts into statistical evidence that can be used when evaluating a hypothesis. By reducing routine arithmetic work, the tool also makes it easier for students and researchers to communicate whether their results agree with an expected genetic pattern.