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What does it mean for the finding of a statistical analysis of data to be statistically significant?
Data is statistically significant if the p (probability) value is below a certain level (ex: 5% or 1%). The p value describes how often one would receive the results they got if left to chance alone. The lower the p value, the less likely it is that your results were due to chance and is stronger evidence against the null hypothesis. Also important to keep in mind is that just because something is statistically significant does not mean it is practically significant.
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How do you explain the analysis of variance assuming that your audience has not had a statistic class before?
The measurement of any statistical variable will vary from one observation to another. Some of this variation is systematic - due to variations in some other variable that "explains" these variations. There may be several such explanatory variables - acting in isolation or in conjunction with one another. Finally, there will be a residual variation which cannot be explained by any of these "explanatory" variables. The statistical technique called analysis of variance first calculates the total variation in the observations. The next step is to calculate what proportion of that variation can be "explained" by other variables, and finding the residual variation. A comparison of the explained variation with the residual variation is an indicator of whether or not the amount explained is statistically significant. The word "explain" is in quotes because there is not always a causal relationship. The causality may go in the opposite direction. Or the variables may be related to another variable that is not part of the analysis.
What was the most significant contribution of pi to the field of mathematics?
it has helped in finding the perimeters and areas of circle.
When do you run a post-hoc test?
You run a post-hoc test after conducting an analysis of variance (ANOVA) and finding a significant result. A post-hoc test is used to determine which specific groups differ significantly from each other, as ANOVA only tells you that there is a difference somewhere but not which groups are different.
Why is it smart to use rounding on front end estimation after finding an answer?
By using front end estimation, the numbers that you start of with have only one significant digit. It is, therefore, nonsense to have an answer with loads of significant digits.
What steps do you use to find the volume of a pencil?
For finding accurate volume of a pencil use a vernier calliper, which measures diameter up to two significant figures. For finding volume of a pencil you will need radius (r) and length (L) of the pencil. V = 3.14 r²L
What is the difference between statistical significance versus practical significance?
Statistical significance means that you are sure that the statistic is reliable. It is very possible that whatever you conclusion or finding is, it may not be important or it not have any decision-making utility. For example, my diet program has a 1 oz weight loss per month and I can show that is statistically significant. Do you really want a diet like that? It is not practically significant
How do you explain the analysis of variance assuming that your audience has not had a statistic class before?
The measurement of any statistical variable will vary from one observation to another. Some of this variation is systematic - due to variations in some other variable that "explains" these variations. There may be several such explanatory variables - acting in isolation or in conjunction with one another. Finally, there will be a residual variation which cannot be explained by any of these "explanatory" variables. The statistical technique called analysis of variance first calculates the total variation in the observations. The next step is to calculate what proportion of that variation can be "explained" by other variables, and finding the residual variation. A comparison of the explained variation with the residual variation is an indicator of whether or not the amount explained is statistically significant. The word "explain" is in quotes because there is not always a causal relationship. The causality may go in the opposite direction. Or the variables may be related to another variable that is not part of the analysis.
What has the author A Hood Roberts written?
A Hood Roberts has written: 'A statistical linguistic analysis of American English'
Why is the standard normal distribution important in statistical analysis?
Why is normal distribution important in statistical analysis? Why is normal distribution important in statistical analysis? An important statistical effect was named for this manufacturing plant. What is it? In a famous research study conducted in the years 1927-1932 at an electrical equipment manufacturing plant, experimenters measured the influence of a number of variables (brightness of lights, temperature, group pressure, working hours, and managerial leadership) on the productivity of the employees. The major finding of the study was that no matter what experimental treatment was employed, the production of the workers seemed to improve. It seemed as though just knowing that they were being studied had a strong positive influence on the workers. .The Hawthorne effect
What is actual use of integration in life?
Statistically analysis. Finding areas under the curves of normal distributions. Look in the back of any statistics text at the t scores. All done for you by calculus. Statistics are vital in many walks of life, from business to science.
What is the significance of finding a contiguous subarray in the context of algorithmic complexity analysis?
Finding a contiguous subarray is significant in algorithmic complexity analysis because it helps in determining the efficiency of algorithms in terms of time and space. By analyzing the performance of algorithms on subarrays, we can understand how they scale with input size and make informed decisions about their efficiency.
How do you find sxx and sxy in finding the line of regression?
Actually, this would require a graphic illustrations and various complex mathematical formulas to adequately explain the whole process. Any statistical analysis text could do this job quite well.
What circumstances can a small treatment effect be statistically significant?
For a given experiment, and a given sample size, there is a probability that a treatment effect of a given size will yield a statistically significant finding. That is, if the treatment effect is 1 unit, then that probability (the power) might be 50%, and the power for a treatment effect of 2 units might be 75%, etc. Unfortunately, before the experiment, we don't know the treatment effect size, and indeed after the experiment we can only estimate it. So a statistically significant result means that, whatever the treatment effect size happens to be, Mother Nature gave you a "thumbs up" sign. That is more likely to happen with a large effect than with a small one.
What has the author Leonard H Zacks written?
Leonard H. Zacks has written: 'Idle time in a parallel channel queue' -- subject(s): System analysis, Queuing theory 'Queueing theoretic analysis of contractors' sequential bidding problems' -- subject(s): Queuing theory
Attributes in class hierarchy in object oriented analysis and design?
Analysis means finding the exact scenario for the problem and design means finding the main class from the analysis part an d to give operation for that class. and from that we can know the exact process.
Is the average function is a mathematical function?
It is a statistical function specifically, finding a number that is a good representation of a set of numbers.
What is root cause analasyst?
Is the analysis of a problem and finding the root cause or reason for the problem
