General information you should know: 1. experimental design: a group of subjects who receive various levels of the independent variable 2. dependent variable: variable that is measured to find effect of independent variable 3. factor or independent variable: manipulated in experiment 4. levels: different forms or strengths 5. replication: repeated subjects within a control or experiment group 6. controls: ppl that don’t receive manipulated independent variable 7. paired vs. independent replicates: paired is exposed to all, independent not 8. randomization: use of some random method to assign subjecs to a group 9. Why are statistics necessary for analyzing scientific data? Variation to judge whether differences we observe btwn populations are real or due to chance 10. What does sum of squares tell you about a data set? Measure of variation within a group 11. What is meant by “probability” in the context of a statistical test? Due to chance alone 12. Why is replication / sufficient sample size necessary in an experiment? To account for natural variation in population 13. Basic structure of statistical test: null hypothesis: assumption that two groups are the same 14. test statistic ( t (Ttest), F: ANOVA, etc.): measure of magnitude of variation btwn groups 15. probability (p-value): low= not likely due to chance, 16. statistical significance: probability less than 0.05, reject null 17. Why are measures like standard error used when graphing mean values of a data set? Measure of variation within a group 18. Why use an ANOVA rather than multiple t-tests when…
determining whether the results from statistical tests are significant.
Although checking of assumptions underlying statistical methods and data screening is an important component
of any competent statistical analysis, it is not possible for you to do so for this assignment given you are
working with a correlation matrix.
How to report results:
First, you are to focus only on interpreting and reporting results of your analyses. Do not include discussions
of the statistical theory underlying your interpretation…
of New Binomial Tests Over Logistic Regression
Oleg Urminsky (University of Chicago)
Presented at the Marketing Science Conference, 2010
This paper questions the use of logit-based models for statistical testing of interactions in experiments, when the underlying process is assumed to be additive. Using both simulations and exact tests, I show evidence that logistic regression suffers from bias as well as low power in these contexts. Instead, I present four tests that are much better…
application of quality control concepts (Wise & Fair, 2001). Most technical manufacturing programs require some level of quality control training including basic coverage of quality management concepts and statistical data analysis, but offer little insight into implementation issues and the practical problems faced by industry (Balbontin & Taner, 2000). This approach can be quite effective in establishing a basic understanding of quality control theory, but may leave gaps in a student’s ability to successfully…
obtain empirical data which can be used to formulate, expand or evaluate a theory. It is not actually directed in design or purpose towards the solution of practical problems. The main aim is to expand the frontiers of knowledge without the intention of having practical applications. However, the results may be applied eventually to practical problems that have social values. An example is hotel management where all the advances made in this area are dependent upon basic researches in foods and…
Reliability and Validity Paper
University of Phoenix
The profession of human service uses an enormous quantify of information to conduct test in the process of service delivery. The data assembled goes to a panel of assessment when deciding the option that will best fit the interest of the population, or the experiment idea in question. The content of this paper will define, and describe the different types of reliability, and validity. In addition display examples of data collection…
The control chart is mathematically equivalent to a series of statistical hypothesis tests. If a plot point is within control limits, say for the average [pic], the null hypothesis that the mean is some value is not rejected. However, if the plot point is outside the control limits, then the hypothesis that the process mean is at some level is rejected. A control chart shows, graphically, the results of many sequential hypothesis tests.
NOTE TO INSTRUCTOR FROM THE AUTHOR (D.C. Montgomery):
Intelligence: the ability to learn from experience, solve problems, and to use knowledge to adapt to new situations. Many theorists sought to test this intelligence. One intelligence theorist, Thurstone, supported a statistical procedure known as Factor Analysis, which identifies clusters of related items on a test. It is used to determine the different strengths and weaknesses underlying the total score. Guilford, along with Spearman and other theorists, believed in a concept…
This journal article recognizes the pressures to move from the traditional animal testing model to a more politically-more human testing method, in doing so, demonstrates that the current replacement techniques are ineffective to test for human health effects for system toxicity and the high cost of current replacements. This article will help me understand the whole argument, though certainly not humane, the real human costs associated with animal testing of cosmetics and why many…
UB Number: 10037256
Project Title: How can I discover what the highest practical level of efficiency is within Morrisons Flaxby Pack house and what is that level
Company: WM Morrisons Plc.
Submission: November 2013
Scope and Rationale of Project
Front-line managers and supervisors run around…
edu/students/student-resources/university-and-school-policies/ for detailed information regarding the above items.
This course aims to provide you with a clear understanding of the main issues in modern risk management. It provides both theoretical and practical descriptions of the main techniques used by companies and financial institutions to reduce their exposure to various type of risks; such as market risk and credit risk.
In the last decades the globalization of markets has exposed…