Homework Exercise 29
Grand Canyon University
December 23, 2012

1. Were the groups in this study independent or dependent? Provide a rationale for your answer. Answer- The group studies were independent. They were being tested by gender, male and female. They were also not matched or paired with each other. 2. t = −3.15 describes the difference between women and men for what variable in this study? Is this value significant? Provide a rationale for your answer. Answer- The variable that is described by -3.15 is the mental health variable. It is significant as indicated by the p value of 0.002. This is less than the alpha number of 0.05 3. Is t = −1.99 significant? Provide a rationale for your answer. Discuss the meaning of this result in this study. Answer- The variable that -1.99 is for is health functioning. It is significant. The p is 0.049. Since the p value is less than the alpha of 0.05 that was designated for the test. The women have lower health functioning than the men have. 4. Examine the t ratios in table VI. Which t ratio indicates the largest difference between male and females in the post MI study? Is this t ratio significant? Provide a rationale for your answer. Answer- The largest t ratio in this study is -3.15 this has a p value of 0.002. It is for the variable of mental health. It is significant. The means are 62.3 for women and 72.7 for men. There is a big difference between the two genders. 5. Consider t = −2.50 and t = −2.54. Which t ratio has the smaller p value? Provide a rationale for your answer. What does this result mean? Answer- The t ratio of -2.54 has a smaller p value (0.007) than the t ratio of -2.50 which has a p value of (0.01). The smaller the p values the less likely the findings will be in error. This result means that the men and women were different for physical role. The men (12.6 mean) scored higher on the physical role than the women (4.7 mean). 6. What is a Type I error?...

...Counseling Psychology 1983, Vol. 30, No, 3,459-463
Copyright 1983 by the American Psychological Association, Inc.
StatisticalSignificance, Power, and Effect Size: A Response to the Reexamination of Reviewer Bias
Bruce E. Wampold
Department of Educational Psychology University of Utah
Michael J. Furlong and Donald R. Atkinson
Graduate School of Education University of California, Santa Barbara
In responding to our study of the influence thatstatisticalsignificance has on reviewers' recommendations for the acceptance or rejection of a manuscript for publication (Atkinson, Furlong, & Wampold, 1982), Fagley and McKinney (1983) argue that reviewers were justified in rejecting the bogus study when nonsignificant results were reported due to what Fagley and McKinney indicate is the low power of the bogus study. The concept of power is discussed in the present article to show that the bogus study actually had adequate power to detect a large experimental effect and that attempts to design studies sensitive to small experimental effects are typically impractical for counseling research when complex designs are used. In addition, it is argued that the power of the bogus study compares favorably to that of research published in the Journal of Counseling Psychology at the time our study was completed. Finally, the importance of considering statisticalsignificance, power, and effect...

...Testing statisticalsignificance is an excellent way to identify probably relevance between a total data set mean/sigma and a smaller sample data set mean/sigma, otherwise known as a population mean/sigma and sample data set mean/sigma. This classification of testing is also very useful in proving probable relevance between data samples. Although testing statisticalsignificance is not a 100% fool proof, if testing to the 95% probability on two data sets the statistical probability is .25% chance that the results of the two samplings was due to chance. When testing at this level of probability and with a data set size that is big enough, a level of certainty can be created to help determine if further investigation is warranted. The following is a problem is used to illustrate how testing statisticalsignificance paints a more descriptive picture of data set relationships.
Sam Sleep researcher hypothesizes that people who are allowed to sleep for only four hours will score significantly lower than people who are allowed to sleep for eight hours on a management ability test. He brings sixteen participants into his sleep lab and randomly assigns them to one of two groups. In one group he has participants sleep for eight hours and in the other group he has them sleep for four. The next morning he administers the SMAT (Sam's Management Ability Test) to all...

...A Study on Gugo and Okra as Homemade Shampoo
A Research Done by:
Francine Faye A. Jumaquio
Majaline Faye A. Tolentino
Romer T. Nepumoceno
Talavera National High School
Talavera Nueva Ecija
A Study on Gugo and Okra as a Homemade Shampoo
Claudine M. Lajara
I-Rosal
Introduction
This study was conducted to determine the effectiveness of a homemade shampoo out of the native Gugo, scientific name Entada phaseuoliodes and Okra, scientific name Abelomoschus Esculentus L. in making different type of hair stronger.
Four phases were done: Phase 1, the control treatment; Phase 2, homemade shampoo compared to control treatment; Phase 3, homemade shampoo compared to varied concentration of gugo and okra; and Phase 4, where the acceptability of the homemade shampoo was determine in terms of smoothness, softness, and manageability.
Statement of the Problem:
Specifically, the researchers aimed to answer the following questions:
1. Can gugo and okra be used as raw material in making shampoo?
2. How effective are gugo and okra on the tensile strength of the hair?
3. Which treatment is more effective – treatments with greater concentration of okra than gugo or more gugo than okra?
Procedure
A. Preparation of Materials
About 10,000 hair strands were gathered from four respondents having different types of hair, (normal, and dry, ethnic, curly). In each type of hair, 2020 strands were used: 240 strands for water, okra, 10...

...BIOL 231 – Marine Environment
Title
An integrated study of the Mpenjati Estuary-Beach System. (PhysicalComponent)
Kutlo Thathe
University Road
Westville
Private Bag X 5600
Durban
4000
210551705@stu.ukzn.ac.za
Abstract
This study was conducted at the Mpenjati Estaury which is located in Port Shepstone and lies along the south coast of Kwa-Zulu Natal. An estuary is a partially enclosed coastal body of water which is either permanently or periodically open to the sea and within which there is measurable variation salinity due to the mixture of fresh water derived from land, however the fresh water inflow may not be perennial, the connection to the sea may be closed for part of the year and tidal influence may be negligible. The beach slope and the flow rate relatively depend on several factors but mainly the wave action and the sediment transportation, as these two factors controls the activities at the mouth of an estuary The focus of the research was to measure the topology of the beach using the Emery Board method and examine the relationship between the beach profile and sediment. Furthermore, the research was to analyze the statistical structure of the natural stream flow into the estuary using flow-duration analysis in relation to monthly dynamics. The changes in elevation were observed, recorded and analyzed. While simultaneously recording the sediment size found at each elevation point, which the results and...

...
Abstract
Power Analysis, StatisticalSignificance, & Effect Size
“If you plan to use inferential statistics (e.g., t-tests, ANOVA, etc.) to analyze your evaluation results, you should first conduct a power analysis to control what size sample you will need. Statistical tests look for evidence that you can reject the null hypothesis and conclude that your program had an effect. With any statistical test, however, there is always the possibility that you will find a difference between groups when one does not actually exist. This is called a Type I error. Likewise, it is possible that when a difference does exist, the test will not be able to identify it. This type of mistake is called a Type II error”. (merra.snr.umich.edu)
Statistical Tests
The two variables measure assorted arrangements of the students that had anxiety marks and study hours it is more appropriate to conduct the correlation examination than other investigation. Null hypothesis be made up of there is no correlation between the study of anxiety scores and the study hours. (Or r is not equal to 0) If the alpha was set at 0.05, this is will be a two tailed t test. The degree of freedom 10 – 2 = 8, the critical t causes are +/- 2.306. The correlation coefficient r = 0.5654. Test value (effect size) =r square ((n-2) / (1-r^2)) = 0.5654* square root ((10-2) / (1-0.5654^2)) =1.939. Therefore...

...absent per employee increases by 10.26 units because 2.6366 is multiplied by (0), holding all other things constant.
All the coefficients are statistically significant or they are different from each other as shown it the table at the ANOVA Excel output in the appendix. The p values in summary shows this below.
p = (0.000)* (0.000)* (0.000)* (0.000)* (0.002)* (0.000)*
*p value below or statistically significant at the 5% level
**p value greater than or statistically insignificant at the 5% level, not applicable in this report
As indicated above that a single stared p value (*p) means that the coefficients are statistically significant and those which are double starred (**p) are statistically insignificant. To measure significance in this report we have used ∞ = 0.05 or 5%. We are not interested in other percentages except that of ∞ = 0.05 0r 5%. None of the coefficients are statistically insignificant as can be shown with the key above and the ANOVA Excel output in the appendix.
The following are the actual values of p from ANOVA Excel output uncut.
p = (8.11681E-14) (1.43035E-07) (0.000471097) (5.38289E-06) (0.002496639) (5.9905E-07)
These are very small values even the one for D5 is still less than ∞ = 0.05 or 5% as ≈ 0.24%
At ∞ = 0.05 or 5% F stat is also significant as its F = 21.40086746 and Significant F is 3.08395E-14 of which is a very small number 0.00000000000003. This value is provided
The R2 = 0.5323484, that is the...

...it. The Drosophila egg is about half a millimeter long. One day after fertilization the embryo develops and hatches worm like larvae. The larva continuously eats and grows, and moults four times. The fourth time it moults it forms an immobile pupa and turns into the winged form. It hatches in about 4 days and is fertile in 12 hours.
Statistical analysis can be used to determine if there is a significant difference between two of group data sets. One way to do this is to use a Chi square. The Chi square test produces a number which you compare to a statistical Chi square number. Each of these statistical numbers has a significance level. Significance levels show you how likely a result is due to chance. The most common level, which is also used in this lab, is .95 which makes something good enough to be believed. This means that 95% of the time the findings will be true, and 5% of the time they will not. If the Chi square produces a number which is less then the statistical value, you accept your null hypothesis, meaning that there is no significant difference between the data. If the Chi square test produces a number higher than the statistical value then you must refute your null hypothesis, meaning that there is a significant difference in your data. The null hypothesis used is that the pattern for inheritance is autosomal. The expected phenotype ratios for mutant to wild...

...“Statistical Treatment”
Communication Reasearch 1
S.Y. 2013- 2014
T.F 7:00am – 8:30am MCS
June 21, 2013
ILARIA L. PANDOLFI
PROFESSOR ROSALIE CERVANTES
I. Objectives:
The learners are expected to:
a. Determine what statistical treatment is all about.
b. Choose their own right statistics in analysing their data.
c. Follow the steps involving statistical treatment.
d. Interpret the data involving tabulation.
e. Provide answers to the drills.
II. Outline of Content:
A. Statistical treatment of data.
B. Treatment of Data and Distribution
C. Measures of Central Tendencies
a. Mean,
b. Median,
c. Mode.
D. Frequency and percentage.
E. Ranking Method.
F. Chi-Square Techniques.
G. T-Z Test
H. Drills.
III. References:
A. Research (Simplified Guide to Thesis Writing)
* “Developing the Research Design” pgs. 168- 178
B. Website: http://explorable.com/statistical-treatment-of-data
C. Website: http://explorable.com/measures-of-central-tendency
D. Website: https://en.wikipedia.org/wiki/Student's_t-test
E. Website: https://en.wikipedia.org/wiki/Analysis_of_variance
A. STATISTICAL TREATMENT OF DATA:
Is essential in order to make use of the data in the right form. Raw data collection is only one aspect of any experiment; the organization of data is equally important so that...