Qmet 252

Qmet 252
A manufacturer wants to increase the shelf life of a line of cake mixes. Past records indicate that the average shelf life of the mix is 216 days. After a
revised mix has been developed, a sample of nine boxes of cake mix gave these shelf lives (in days): 215, 217, 218, 219, 216, 217, 217, 218 and 218. At
the 0.025 level, has the shelf life of the cake mix increased?
Choose one answer.
|[pic]|a. No, because computed t lies in the region of acceptance. [pic] | |
|[pic]|b. Yes, because computed t is less than the critical value. [pic] | |
|[pic]|c. Yes, because computed t is greater than the critical value. [pic] | |
|[pic]|d. No, ... Show more content on Helpwriting.net ...
Question 5
Marks: 1
A machine is set to fill the small size packages of M&M candies with 56 candies per bag. A sample revealed: 3 bags of 56, 2 bags of 57, 1 bag of 55,
and 2 bags of 58. How many degrees of freedom are there?
Choose one answer.
|[pic]|a. 7 [pic]| |
|[pic]|b. 9 [pic]| |
|[pic]|c. 6 [pic]| |
|[pic]|d. 8 [pic]| |
|[pic]|e. 1 [pic]| |
Total number of bags = 3+2+1+2=8
df = 8–1=7
Incorrect
Marks for this submission: 0/1.
Question 6
Marks: 1
What value does the null hypothesis make a claim about?
Choose one answer.
|[pic]|a. Population parameter [pic] | |
|[pic]|b. Sample mean [pic] | |
|[pic]|c. Sample statistic [pic] | |
|[pic]|d. Type II error [pic] | |
Correct
Question 7
Marks: 1
If α = 0.05, what is the probability of making a Type I error?
Choose one answer.
|[pic]|a. 20/20 [pic] | |
|[pic]|b. 19/20 [pic] | |
|[pic]|c. 1/20 [pic] | |
|[pic]|d. 0 [pic] | |
Correct
Question 8
Marks: 1
What is the level of significance?
Choose one answer.
|[pic]|a. Beta error [pic] | |
|[pic]|b. Probability of a Type II error [pic] | |
|[pic]|c. Probability of a Type I error [pic] | |
|[pic]|d. z–value of 1.96 [pic] | |
Correct
Marks for this
... Get more on HelpWriting.net ...

Statistical Analysis Examples
Statistical Analyses The following physiological measures were assessed for statistical significance: RMSSD, HF power, SBP, DBP and HR. A natural
log transformation was applied to HRV measures prior to the analysis. Each measure was analyzed using a one–way repeated measures ANOVA across
each experimental condition: baseline, stressor, recovery. The application of repeated measures ANOVA calls for the assumption that the dependent
variables follow a normal distribution. In the context of this study, normal distribution cannot be demonstrated given the small sample size. However,
the present study serves as a pilot study and thus normality will be overlooked in favor of obtaining a sense of the quality of the physiological data. As
such, the presence of a normal distribution will be assumed for subsequent statistical analyses. Additionally, the assumption of Mauchly's Test of
Sphericity was assumed to be violated in each case, and the Greehouse–Geisser correction was used in the resulting analyses. Post hoc tests with
Bonferroni's correction were conducted to assess statistical significance between each experimental condition with one another. An α level of .05 was
used, therefore probability values < .05 were determined to be significant.
Results
Table 1 summarizes the means and standard deviations obtained from descriptive statistics. A repeated measures ANOVA with a Greenhouse–Geisser
correction determined that RMSSD values differed significantly across experimental

Student 's Performance Gap Between Students And Students
Introduction Academic institutions are always trying to improve the grades of their students. They are researching if a smaller classroom setting will
improve the grades of their students overall. This is important because these institutions want their students to graduate in the typical four years. In
another study, a small classroom setting is important during adolescence because a child develops social behaviors which later affect his or her level of
learning (Cappella, 2012). Another researcher presented how students who were struggling in school benefitted from a smaller classroom and this
closed the achievement gap between students (Bosworth, 2014). Also, one study reported that class size had no effect when it came to a student's
performance when they were in middle school, but there is an effect when the students are in elementary school (Vaag, 2013). Students in a smaller
class will have a higher grade compared to a students in a larger class. Method Participants Participants were nationally sampled from nine introductory
psychology courses including each grade level, freshman through seniors. While both male and female students are included in the sample the female
population is 68% of the sample size which is made up of 488 students from smaller classes and 879 students from larger classes across the country.
The students participating in the study were required to take the course for credit; however they had to give their consent to allow their data to be used

Essay on Btm 8106 Complete Course Btm8106 Complete Course
BTM 8106 Complete Course BTM8106 Complete Course
Click below link for Answer http://workbank247.com/q/btm–8106–complete–course–btm–8106–complete–course/15548
http://workbank247.com/q/btm–8106–complete–course–btm–8106–complete–course/15548
Week 1
Answer the following questions:
1. Jackson (2012) even–numbered Chapter Exercises (p. 244).
2. What is the purpose of conducting an experiment? How does an experimental design accomplish its purpose?
3. What are the advantages and disadvantages of an experimental design in an educational study?
4. What is more important in an experimental study, designing the study in order to make strong internal validity claims or strong external validity
claims? Why?
5. In an experiment, ... Show more content on Helpwriting.net ...
Why does it matter?
5. Compare and contrast parametric and nonparametric statistics. Why and in what types of cases would you use one over the other?
6. Why is it important to pay attention to the assumptions of the statistical test? What are your options if your dependent variable scores are not
normally distributed?
Part II
Part II introduces you to a debate in the field of education between those who support Null Hypothesis Significance Testing (NHST) and those who
argue that NHST is poorly suited to most of the questions educators are interested in. Jackson (2012) and Trochim and Donnelly (2006) pretty much
follow this model. Northcentral follows it. But, as the authors of the readings for Part II argue, using statistical analyses based on this model may yield
very misleading results. You may or may not propose a study that uses alternative models of data analysis and presentation of findings (e.g., confidence
intervals and effect sizes) or supplements NHST with another model. In any case, by learning about alternatives to NHST, you will better understand it
and the culture of the field of education.
Answer the following questions:
1. What does p = .05 mean? What are some misconceptions about the meaning of p =.05? Why are they wrong? Should all research adhere to the p =
.05 standard for significance? Why or why not?
2. Compare and contrast the concepts of effect size and statistical significance.
3. What is the difference between a

Biology And Reading Comprehension Exams
At the start and finish of a school year, a group of Italian third– and fifth–grade students with diverse sociocultural levels underwent two studies in
which their concurrent and predictive validities of the Naglieri Nonverbal Ability Test (NNAT) and Raven's Colored Progressive Matrices (CPM) were
investigated. The focus of the studies was on their math and reading comprehension exams. The NNAT study is a "nonverbal measure of general
ability... intended to assess cognitive ability independently of linguistic and cultural background." (1 Pearson) The CPM study is also a nonverbal test
but, conversely, it measures the subjects' reasoning abilities. These nonverbal studies were helpful in presenting meaningful data for forecasting the
academic performance of students with diverse sociocultural levels. The studied group consisted of 253 participants. The participants' characteristics
were the following: (1) their average age was 9.4 years (with a standard deviation of 1.1 years of age), (2) 130 were female and 123 were male, and (3)
126 were in the third–grade and 127 were in the fifth–grade. Their Family Cultural Status (FCS) characteristics were the following: (A) Low: consisting
of 86 participants (34% of the total pool of subjects)– (1) their average age was 9.5 years (with a standard deviation of 1.0 years of age), (2) 38 were
female and 48 were male, and (3) 39 were in the third–grade and 47 were in the fifth–grade; (B) Moderate: consisting of 97 participants (38% of the

Statistical Significance
Journal of Counseling Psychology 1983, Vol. 30, No, 3,459–463
Copyright 1983 by the American Psychological Association, Inc.
Statistical Significance, 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 that statistical significance 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 ... Show more content on Helpwriting.net ...
To detect a small experimental effect in the bogus study, for example, we would have had to increase the sample size from 81 to 1,206, or 134 subjects
459
460
COMMENTS argument is that because the average effect size for published research was equivalent to that of a medium effect, the reviewer 's decision
to reject the bogus manuscript under the nonsignificant condition was "reasonable." Further examination of the Haase et al. (1982) article and our own
analysis of published research, however, demonstrates that the power of the bogus study was great enough to detect effect sizes that are typical of
research published in JCP, which was our intention when we designed the bogus study. First, although the median effect size (if) for all univariate
statistical tests, significant and nonsignificant, reported by Haase et al. (1982) was .083, this index was steadily increasing at a rate of approximately
.5% per year, so that the projected median if– in 1981 (the year our study was completed) would be .13. Importantly, an r)2 of .13 corresponds to an
effect size (/) of .39, which Cohen (1977) designates as a large effect. A further examination of the Haase et al. (1982) data also lends support to our
argument. Their analysis examined the strength of association for 11,044 univariate statistical tests derived from only 701 manuscripts; thus, each
manuscript reported an average of more than 15 statistical tests. Since statistically significant and

Basic Terminology For A State Of Affairs
Basic Terminology: Unit Three
Sheila Elyse Brooks
Stanbridge College
April 27, 2015
Introduction As we continue our journey through the "trials and errors" of understanding basic statistical terminology. Let 's focus our attention on the
following: understanding what is the difference between a hypothesis, statistical hypothesis, and an experimental hypothesis. In addition, I want to
explore how researchers determine the appropriate sample size. Now, some of you might be asking the question "what do we do once we have our
hypothesis and sample population?" Well, now is a wonderful time to start experimenting with statistical methods like the chi–square test. I will explain
this later on.
Hypothesis
A ... Show more content on Helpwriting.net ...
However, most clinical research associates do not hold medical degree of any type. This makes it rather to master the skills I need when I see my
patients. Often, we are not allowed to open any lab kits (because they are in sequential order) which makes nurses prone to more mistakes. When I first
came to Mount
Carmel, I notice we had simulators. So, I began to think how these simulators could reduce the clinical errors in research. My hypothesis (state of
affairs) could be: Clinical research nurses who practice on simulators are more likely to have fewer medical errors then those who do not. My
Experimental hypothesis: Clinical research nurses are 25% more likely to make mistakes when performing clinical skills (such as lab draws and
administering study medication) then those nurses who practice on patient simulators. My experiment: Compare the performance of nurses who use
patient simulators to practice specific study related skills versus those who do not. After you figure out your hypothesis, you must consider what makes
a "good estimate".
Making Good Estimates We obtain a sample in order to obtain a statistical measurement such as a mean form our observation. One can say that
different sample sizes would produce different values or variations. The variation between these individual estimates is due to sampling error (Fowler et
el, 2002). It is important to note that sampling

T-Test Paper
I have chosen to report to 4 significant figures for this assignment as going to two decimal places would have disrupted values such as the p–value for
the t–test (0.0002720).
TWO SAMPLE (UNPAIRED) T–TEST WITH UNEQUAL VARIANCES ANALYSIS
The null hypothesis for this experiment was; H0: μ1=μ2. Therefore the alternate hypothesis was; H1: μ1≠ μ2.
The test used to determine if we can fail to reject the null hypothesis is a two sample (unpaired) t–test with unequal variances. A two sample test was
chosen as it compares two independent samples. In this experiment we are using two samples from two different products, one is a genuine Viagra and
one is an intercepted product of undetermined origin.
There are two types of t–tests; one and two tailed. One tailed tests are used for experiments where the question being asked is, is one value less than or
greater than another. A two tailed test is suited to analyse the null hypothesis as it is stating that the genuine product dissolves equally as much as the
intercepted Viagra.
Genuine ... Show more content on Helpwriting.net ...
Since the P value from the two tail test is 0.0002720 this means the data is statistically significant and we are not incorrectly rejecting a true null
hypothesis.
The critical t–value is the minimum value needed to have a p f critical value then p < 0.05 then the results are statistically significant.
A B C D
89.14 82.44 95.58 95.50
87.63 57.41 87.80 92.80
89.40 91.48 168.46 103.0
Anova: Single Factor SUMMARY
Groups Count Sum Average Variance
A 3 266.2 88.72 0.9134
B 3 231.3 77.11

The Test Of Multicollinearity Test And Then Generating The...
For the test of Multicollinearity, I have used the ADF test and then generating the first difference variables and running regression to correct the
Multicollinearity. First using the Dicky–Fuller test, the non–stationary variables were separated from the stationary variables. After which, the non–
stationary variables were made stationary by using the first difference for these variables and getting them close to the mean. Now, the last step to
correct the Multicollinearity was to run regression on the first difference variables. The results for this OLS regression using the first difference
variables, we have only 4 significant variables as compared to 8 significant variable in the original regression. From the initial OLS regression, ... Show
more content on Helpwriting.net ...
Another variable that is significant is the S&P 500 stock index. It's significant at 1% significance level. The sign for the estimates is negative, which is
similar to the intuitively one would think of. Since, we know that the investors will be investing in the gold whenever they are not confident about the
market outlook, so they will be pulling out their investment from the stock market and putting it in the gold market. Which makes sense as the sign of
the estimates is negative. Now, to interpret the estimates for S&P 500 variable, we can say that a unit decrease in the stock market will increase the gold
price by close to $1. Which means that there is equal increase in the gold prices as the decrease in the stock market. Treasury inflation indexed
securities is another variable that is significant at 1% significance level. Now, since the TIPS are indexed to the inflation, the TIPS will increase
whenever there is inflation and decrease when there is deflation. The sign for the variable is negative meaning that a unit increase in the TIPS level will
increase the gold prices by $0.95. Since, the TIPS level is increasing there is inflation and because of that it makes sense that the investors will be
investing in the gold market when there is increase in the inflation. Oil prices variable is also significant at 5% significance level. These means that the
with a unit increase in the prices of oil future contracts will increase the price of gold by $.80 cents,

Path Analysis Paper
Main analyses involved running each of three models through AMOS SEM software separately, using path analysis techniques to assess direct and
indirect effects, among the present observed variables (Arbuckle, 2013). Path analysis, which is based on multiple regressions, examines the
relationship between exogenous (i.e., variable not causes by another variable, but effects one or more variables in model) and endogenous variables
(i.e., a variable that is caused or effected by one or more variables in a model; Iacobucci, 2010). Path models examine the total effects, as well as the
direct and indirect of effects of variables in a single model, simultaneously (Peterson et al., 2014). Structural equation modeling path analysis
techniques are superior to standard regression analyses in that they: 1) provide more accurate estimates of the effects of hypothesized variables; 2)
estimate all effects simultaneously; 3) allow for greater accuracy of parameter estimates when examining competing models; and 4) allow the
researcher to compare effects of multiple mediators (Zhao, Lynch, & Chen, 2010).
Mediation Testing. Data were fit to the path model using AMOS SEM software. For Model 1(see Figure 3) and Model 2 (see Figure 4), ethnic identity
was examined as a mediating variable between community participation– neighborhood sense of community and psychological empowerment (Model
1) and 30–day substance use (Model 2). For Model 3 (see Figure 5), psychological empowerment was examined

One- and Two-Sample Tests of Hypothesis, Variance, and...
Chapter 10
31. A new weight–watching company, Weight Reducers International, advertises that those who join will lose, on the average, 10 pounds the first two
weeks with a standard deviation of 2.8 pounds. A random sample of 50 people who joined the new weight reduction program revealed the mean loss to
be 9 pounds. At the .05 level of significance, can we conclude that those joining Weight Reducers on average will lose less than 10 pounds? Determine
the p–value.
Answer:
H0: = 10 pounds
H1: < 10 pounds
Reject the null hypothesis if Z < –1.65
9.0 – 10.00 Z= –––––––––––––––– = –2.53 2.8/sqrt(50)
The null hypothesis is rejected, the average weight loss is less than 10 pounds.
p–value = .5000 – ... Show more content on Helpwriting.net ...
There is no difference in the mean for waiting time on Little River vs. Murrells Inlet location.
52. The president of the American Insurance Institute wants to compare the yearly costs of auto insurance offered by two leading companies. He selects
a sample of 15 families, some with only a single insured driver, others with several teenage drivers, and pays each family a stipend to contact the two
companies and ask for a price quote. To make the data comparable, certain features, such as the deductible amount and limits of liability, are
standardized. The sample information is reported below. At the .10 significance level, can we conclude that there is a difference in the amounts quoted?
Answer:
H0 = There is no difference in price quoted for car insurance between Progressive and GEICO.
H1 = There is a difference in price quoted for car insurance between Progressive and GEICO.
With 14 degree of freedom and .10 significance level, the critical t values are –1.761 and 1.761.
Paired T–Test and CI: Progressive, GEICO
Paired T for Progressive – GEICO
N Mean StDev SE Mean
Progressive 15 1391 438 113
GEICO 15 1637

The Ongoing Tyranny Of Statistical Significance Testing
Article by Stang, Poole and Kuss (2010) titled "The ongoing tyranny of statistical significance testing in biomedical research" describe common
misuses and interpretation of statistical significance testing (SST). The authors point out fallacy understanding in interpretive the p–value and how it
often mixed in measuring effect size and its precision. This misconception then they assert may impede scientific progress and furthermore become
unintended harmful treatment. They also proposed an important way out of the significance fallacies in this article. Therefore, in this article review, all
the finding that made by the authors will be summarized and review of it will be drawn based on other references.
1. Statistical Significance Test (SST) and P–value
Stang, Poole and Kuss explain, in SST, P–value is an important part to decide the null hypothesis. The SST itself, they explain is analytical approach
that developed based on two prominent statisticians, Fisher and the Neyman–Pearson. However, in present practical, SST is incompatible amalgamation
of those two theories. In Fisher theory, P value represents the strength of evidence against the null hypothesis: the lower the P–value, the stronger the
evidence. In this theory, they criticize lake of alternative hypothesis and concept of statistical power. In contrast Neyman and Person theory included the
alternative for the null hypothesis, type I and II error and theoretical of effect size. This hybrid method leads to

The Fundamental Concepts Of Statistics
Question 5
This paper will provide a sample of the fundamental concepts in statistics. During research there is usually variance between individuals within a group
or between different groups. During hypothesis testing the researcher wants to know if the sample of data collected is truly representative of the entire
population.
Null hypothesis testing is concerned with the correlation or differences in means (continuous data) between groups. The null hypothesis works on the
premise that there is no difference between two groups such as males and females who respond to a set of items (independent sample T–test). The null
hypothesis can also be described as the hypothesized mean being equal to the population mean. So the null hypothesis is ... Show more content on
Helpwriting.net ...
Whereas, if I fail to reject the null, because I believe the groups are the same when, in fact, they are different this is a type 2 error. In its simplest terms,
Type I error is the incorrect rejection of the null hypothesis, whereas Type II is the incorrect acceptance of the null hypothesis.
Related to hypothesis testing is the Significance level or cut off point (alpha). The P or the probability value is one of the indicators that can be used to
test the null hypothesis. Usually a significance level = .05 (95% confidence interval) or lower is the accepted rule for rejecting the null hypothesis. This
means that we will accept up to 5% of type 1 error. Also, P < 0.01 sometimes is referred to as statistically highly significant. If the p value is < .05, then
we would reject the null hypothesis. Visually, on a bell curve (two–tailed test) this can be presented as the critical region or how far our sample statistic
is from the null hypothesis. In relation to P values there is the confidence interval (or bound). A 0.05 level of significance would be centered on a 95%
confidence interval. If our given value sits outside the bound we would reject the null. However, if our value sits within the interval we would not reject
the null hypothesis.
Another concern during research is the issue of sampling error due to only a portion of the population being used during a study. Even when bias is
reduced by random sampling the

The Efficacy Of Methotrexate ( Mtx ) Versus Placebo
The Efficacy of Methotrexate (MTX) versus Placebo in Early Diffuse Scleroderma(1). Introduction The aim of this essay is to explore the objectives,
the hypotheses, the study design, the research methods, statistical data analysis, the results and the ensuing discussion in the research paper mentioned
above and try attempt analyze why, contrary to expectations, this study did not find a significant efficacy of MTX in dcSSc despite having documented
efficacy in skin scores. Systemic sclerosis (SSc) is a chronic, rare, complex, multisystem, autoimmune disease of the connective tissues with diverse
variants. Progressively the skin thickens and scars, with excessive accumulation of fibers, cells and collagen in the skin and visceral organs, ... Show
more content on Helpwriting.net ...
Further ahead (8) guidelines on metrics in disease activity indices such as STPR as a predictor of mortality and visceral involvement (9). These RCTs
has probable findings on the efficacy of MTX (10)(11).Interventions which aim at pathogenesis hold a promising future(e.g. Imatinib).Therapeutic
agents targeting the fibrotic and vascular pathways(4) , conversely, improving survival rates (6). Nonconformity with the CONSORT 2010 checklist
Randomization curbs bias. In this study, much detail remains unclear in the description of the trial design, methods, results and the conclusion. It is
problematic in the non– adherence to the CONSORT statement, notwithstanding, the full trial protocol, availability and accessibility and its entry into
the trial registry. Accordingly, registration precludes post hoc changes in the primary outcome whilst lessening the chances of outcome reporting bias.
Surprisingly, the role of the sponsor or the funder has little attention paid given that it plays a role in generating a conflict of interest. Unsatisfactorily, a
CONSORT flow diagram as a requisite in an RCT has been overlooked. This study has claims of stratified randomization, nevertheless, questions abide
on the generation of the blocks, who generated the random allocation sequence, were there an allocation concealment mechanism, who enrolled the

Exploring Inferential Statistics and Their Discontents
What are degrees of freedom? The degrees of freedom (df) of an estimate is the number or function of sample size of information on which the estimate
is based and are free to vary relating to the sample size (Jackson, 2012; Trochim & Donnelly, 2008).
How are the calculated? The degrees of freedom for an estimate equals the number of values minus the number of factors expected en route to the
approximation in question. Therefore, the degrees of freedom of an estimate of variance is equal to N – 1, where N is the number of observations
(Jackson, 2012). Given a single set of six numbers (N) the df = 6 – 1 = 5.
What do inferential statistics allow you to infer? Inferential statistics establish the methods for the analyses used for ... Show more content on
Helpwriting.net ...
What are your options if your dependent variable scores are not normally distributed? Transformation of data using a logarithm, square root, reciprocal,
or some other function assists in normalizing the data and correcting for heteroscedasiticy, nonlinearity, and outliers when one or more variables are not
normally distributed (Abrams, 1999; Bland & Altman, 1996). The extent of the deviations from normality determines the specific transformation used.
A moderate difference in normality uses a square root transformation, more substantially non–normal uses a log transformation, and severely non–
normal would use an inverse transformation (Abrams, 1999). The aim of data transformation allows for changing the non–normal distributed
population data into more useful variable and is not uncommon as the basic statistical summaries such as the sample mean, variance, z–scores,
histograms, etc., are all transformed data and require the data follow a particular distribution (Bland & Altman, 1996; Trochim & Donnelly, 2008).
Part II
What does p = .05 mean?
P = .05, or p–value, is a probability measurement that the confidence of the research questions or null hypothesis is correct and has a less than 5 percent
observed outcome on a normal distribution curve thus having statistically significant. The p–value is the prospect that null hypothesis is actually
correct; however, criticisms of various scholars believe in science that nearly everything is impossible to

Essay On Dynamic Membranes
3. Inclusion/Exclusion criteria: Cells' data bank will be screened to determine the dynamic membrane properties for each cell group and to ensure they
meting all inclusion and exclusion criteria. Cells will be excluded from the study if they fall under one of the following: (1) Unhealthy neural cells (2)
neural cells that have different diameter or geometric shape (3) cells that don't contain all the required parameters values, (4) Cancer cells (5)
Uncomplete data set. 4. Sample size calculation: To determine the sample size needed for all groups, an a priori power analysis, conducted using
G*Power 3.0.10 software, calculated the required sample size n = 30 per group bases on an ANOVA test that has 80% power to detect a significant ...
Show more content on Helpwriting.net ...
Alternatively, it is possible that the statistical test find significant difference between trials, which is an indication of the in consistency of the algorithm.
In this case we would revise out algorithm and experimental design, we also may increase the number of trials to re–examine the consistency of the
algorithm. Also the statistical test may reveal no significant difference between groups, in this case we will re construct the groups based on
manipulation of three parameters, and we will revisit the range of variation for each parameter. 2. Exp2: We predict a significant difference between
groups, waveforms and the interaction between both of them, and the post hoc test will reveal that groups with variation in Gnamax, Gleak and α will
be more selective than groups with them constant. This is an indication that Gnamax, Gleak and α can be used to improve stimulation selectivity.
However, the test may show no significance between groups, in this case we will revise the parameters combinations. Also, We may run the statistical
test relatively to the stimulus strength (K) instead of NOC. In this case we should revise the parameters variation range and confirm that all cells is
stable when running the test. 3. EXp3: Results from experiment 2 will guide us

Classification And Discussion Of Plant Authentication
CHAPTER–3 RESULTS AND DISCUSSION 3.1 PLANT AUTHENTICATION The Cajanus cajan was collected from Village– Chakneelkanth Dist–
Kushinagar, Uttar Pradesh, India, during the month of January 2014. The authentication of the plant was done by Dr. Tariq Husain, Senior Principal
Scientist, Plant diversity, Systematic and Herbarium Division, CSIR–National Botanical & Research Institute, Lucknow, Uttar Pradesh and plant
specimen has been identified as Cajanus cajan (L.) Huth of the family Fabaceae and a specimen containing voucher number (LWG–037) was submitted
for the future reference. 3.2 PREPARATION OF EXTRACT Fresh and healthy Cajanus cajan roots were collected, shade dried, powdered and
macerated in 70% v/v ethanol for 72 hrs. The liquid extract was collected and evaporated under reduced pressure by using rotary evaporator (Buchi R–
200) at 400C and then freeze–dried in lyophilizer (Labconco, USA) to obtain solid residue. 3.3 GENERAL BEHAVIOR AND ACUTE TOXICITY
According to OECD–423 guidelines (Acute toxic class method) and Douli and Sengupta, 2012, none of the doses tested produced any gross apparent
effect on general motor activity, muscular weakness, feacal output, feeding behavior etc. during the 14 days of observation. Therefore, ethanolic extract
of CC was found safe up to dose of 2000 mg/kg. 3.4 PRELIMINARY PHYTOCHEMICAL SCREENING Preliminary qualitative phytochemical
screening of Cajanus cajan root extract in 70% ethanol showed presence of flavonoids, tannins,

The Effect Of Child Interaction On Brain Development
The authors of this article, Martha Ann Bell, Annie Bernier, and Susan D. Calkins (2016) set out to find whether or not early care–giving experiences
have an effect on brain development. The authors hypothesized that good childcare, or nurturing, would have a positive effect on synapse formation in
the brain. The authors noted that there has not been a lot of research on the effects of mother–infant interactions on child development thus far. The only
cases done at large are those of the severe situations of child neglect and abuse. They were inspired by "Greenough, Black, and Wallace's (1987)
influential propositions pertaining to the experience–dependent nature of brain development" (Bell et. al, 2016). There is remarkable plasticity present
... Show more content on Helpwriting.net ...
The sample size was not big enough, toward the end they had under 200 participants in the study. For a study's results to have much more of a
significant impact the sample size has to be quite large. This was hardly a big study. As for the methods section, they only filmed the mother for two
minutes playing with her infant. Infants' brain activity cannot be properly examined with just a two–minute interval of playing with their mother. This
short time of interaction can hardly detect a significant effect on an infant's brain activity. With that being said, this longitudinal study should have gone
up until the age of at least 2 or 3, this study does not really delve into the complete effect of infant–mother interaction if it doesn't show how it effects
these children in the future. The authors also found small sample size as a limitation, stating that the covariates were modest in magnitude due to small
sample size and therefore resulted in a lower statistical power. They also a agreed that the two minute evaluation wasn't long enough and suggested a
few hours as sufficient to detect some sort of positive correlation. They also believed the controls implemented, "previous power and key
sociodemographics held constant in the regression models", may have led to the small magnitude between infant EEG power and maternal behaviors.
These were the only limitations they discussed which they believe

The Effects Of Supervision And The Types Of Changes That...
Understanding the effects of supervision, and the types of changes that may take place under that supervision, can contribute to developing programs to
assist in a wide variety of areas. In the article, "Changes in the effects of process–oriented group supervision as reported by female and male nursing
students: a prospective longitudinal study", authors Arvidsson, Baigi, and Skärsäter (2008) research the reports of male and female nursing students on
changes in the effects of process–oriented group supervision (PGS). The study took place over the course of a 3–year study period (2002–2005) at a
university in south Sweden, and included a study group of nursing students (n = 183) followed over course of their studies. A questionnaire was used to
assess changes brought on by PGS, and contained the three subscales of supportive, educational, and developmental (Arvidsson et al., 2008). In
addition to these subscales, there were also items for age, gender, and previous experience in healthcare; which is used to give a breakdown of the
overall effects of PGS (Arvidsson et al., 2008). A t–test was used to make a comparison of the first and third year of studies across the educational,
supportive, and developmental subscales (Arvidsson et al., 2008). Although specific research hypotheses are not given in the article, based on the
information presented it is possible to construct the research hypotheses and corresponding null hypotheses with a fairly high degree of accuracy. The

Nursing Interventions For The Management Of Patient Fatigue
Michaela P. Capulong
NU310
Unit 3 Assignment Worksheet
August 17, 2015
Directions
1. List the source in APA style and format
Reference:
Patterson, E., Wan, Yi, Wai, T., Sidani, S. (2013). Nonpharmacological nursing interventions for the management of patient fatigue: a literature review.
Journal of Clinical Nursing, 22, 2668–2678. doi: 10.1111/jocn.12211
2. Is the review thorough–does it include all of the major studies on the topic? Does it include recent research? Are studies from other related
disciplines included, if appropriate? (25 points)
In my opinion the review was fair, but the research study is weak due to several limitations.
The reviewers clearly identified the limitations of the study such as, the sample size and the evaluation of the interventions. Although, the eight types of
interventions were reviewed, the researchers did not include the effect and impact of nonpharmacological interventions of patients with fatigue. The
reviewers included the recent research studies for comparison and credible references were used to support the review. A table with descriptions and
interventions should also be clearly labeled and in detailed. Based on the study, the interventions were delivered by nurses and nurse researchers. It
would more effective if other disciples such as the occupational therapist, physical therapist, Kinesiotherapist, and physicians were included in the
studies. The OT, PT, KT plays a significant role in improving activity tolerance and

Solving How Statistics Could Be Done Incorrectly
After hours of deep research, one would be able to solve how statistics could be done incorrectly. My research was not extreme enough to find the true
difference between replication problem and replication crisis, nevertheless, a replication crisis is when a scientist finds results of many scientific
experiments that are usually impossible to replicate on subsequent investigation. While a replication problem in social sciences would be that the
scientists are not interested in experiments that have already been replicated (Visser). Replications were not highly respected and were extremely
unattractive. Also, another problem was the replication studies that have been carried out are usually demonstrated that they are not robust, which ...
The only problem is that they randomly sampled two hundred students from the school, which can lead to misleading conclusions since they could
possibly choose a group of students of love coffee, but the rest of the students that were not surveyed could dislike coffee. This would lead the new
coffee shop into closing down from the lack of sales. When researchers do not receive the conclusions they were expecting, they switch to p–hacking.
P–hacking is when a surveyor drops specific data points so that the p–value will be under five percent. P–hackers monitor data while it is being
collected. This would be the same as significance searching since the researchers will produce false results so that they can reach their desired
significance. Researcher degrees of freedom also falls in a similar column that these other terms are in. A researcher degrees of freedom influences the
probability of a false positive result. Misunderstanding the p–values is extremely common, nevertheless, there are six ways researchers misinterpret p–
values often. The most common misinterpretations of p–values usually include: the p–value does not indicate the size or importance of the experiment,
the significance level is not determined by the p–value, the p–value is not the probability that replicating the experiment would produce to the same
conclusion, and the p–value is not the probability of falsely rejecting the null hypothesis.

A Research Study On T Test
Good Morning LV Team Members!
We made it! This is our last session and as per your request we will go over the t–test for the last time. T–test can be used to analyze two data sets that
are independent or dependent of each other. There are 3 types of t–test:
– The t–test dependent, when the same group of subjects is tested at different time interval, different conditions or more than once. It is also known as
the t–test for paired samples or t–test for correlated sample (Salkind, 2004, p.184).
– The t–test independent: A two–sample equal variance test is performed
– The t–test independent: A two– sample unequal variance test is performed
I will be using the data from the UC Davis Olive Center reports of 2011 to compute the t–test value. ... Show more content on Helpwriting.net ...
If one has more than two groups to compare then use the ANOVA. There are two way of doing it: the simple or one way analysis of variance due to the
presence of one grouping dimension and the other one is called factorial design. The factorial design is complex, it is similar to the simple ANOVA with
the addition of more level to the group. For example: number of girls in group1, group2, group 3 would be simple, now when we add to each group
another level like number of boys that would make it to factorial because each group would have girls and boys.
4– Once you have selected the right statistical test, in our case the t–test then we will calculate the effect size to find out whether the result is
meaningful or not. The t–test helps us to understand whether there is statistics significance difference or not.
In our example, we want to find the t–test of independency mean. To calculate the t–test we need to calculate the mean, variance (used to calculate the
effect size), SD of each lab results.
The t–test could be easily calculated using the excel program using four ranges.
The first and the second range would be each group data, then select the number of tail (one tail has a t–test of rejection on one side and two tail would
have on each side bell curve) and finally select the type of t–test. One for paired, two for independent with equal variance and three for

Statistical Methods Of Psychology Journals : Guidelines...
Wilkinson, L. (1999). Statistical Methods In Psychology Journals: Guidelines And Explanations. American Psychologist, 54(8), 594–604. Retrieved
September 10, 2015.
In the mid 90's, the Board of Scientific Affairs (BSA) of the American Psychological Association (APA) convened a Task Force on Statistical Affairs
whose goal was to "elucidate some of the controversial issues surrounding applications of statistics including significance testing and its alternatives;
alternative underlying models and data transformation; and newer methods made possible by powerful computers" (BSA, personal communication with
the author, February 28, 1996). This task force consisted of statisticians, teachers of statistics, authors of statistics, journal ... Show more content on
Helpwriting.net ...
Properly defining the population is crucial. When the word population is used, many think of humans or animals, but population can also consist of
observations on research articles, adjectives, as well as living things. The population is crucial because it will affect almost every conclusion in an
article. Sampling procedure, as well as inclusion and exclusion criteria should be emphasized as well as the sample size for any subgroups. It also is
important to include if you are using a convenience sample or subjects that are selected randomly.
Assignment
Random assignment will allow for the strongest possible causal inference that is free of extraneous assumptions. Wilkinson (1999) suggests the
researcher provides enough information to show that the process in making the assignments is in fact random. It is recommended to use a
pseudorandom sequence from a computer generator or published tables of randomized numbers. This also allows other researchers to check the
methods used later. Confounds of covariates are commonly encountered when using nonrandom assignment, and can affect the outcome. It is best to
attempt to determine the covariates, measure them adequately, and then adjust for any effects. If the researcher adjusts this by analysis, any assumptions
made must be explicitly stated, tested, and justified. Sources of bias should also be taken into consideration.
Measurement
Most studies have variables that must

Correlation Between Alcohol And Alcohol
Results The amount of hours a person works a week is negatively correlated to an individual's dependency to alcohol. The less hours a person works
per week the more they will be dependent on alcohol. On average, the individuals in the sample worked a low amount of hours a week (M = 11.31, SD
= 14.9) and had a moderately low dependency on alcohol (M = 31.11, SD = 14.26). In addition, the confidence intervals looking at the amount of hours
worked per week indicated that the participants continued to work low hours (95% CI = [8.96, 13.83]). Along with, both the upper and lower bounds of
the alcohol decency score showing reaming consistent with a modestly low dependency on alcohol (95% CI = [29.02, 33.58]).
When a Spearman's correlation analysis was used a negative correlation was found indicating that a the less amount of hours a person worked a week
the higher their dependency on alcohol is. This correlation supported the hypotheses. A Spearman's correlation was used because thee out of the four
test for a normally distributed population were violated and the sample size was over 100. However, the correlation was not significant rs = –.114, n =
152, p = .08 (one – tailed).
The amount of hours worked in a week had an extremely small effect on a person's dependency to alcohol r2 = .01. This incredibly small effect size is
consistent with the results of the significance test. With the correlation having no significance then it is accurate to say that a person's work hours had

Movie Analysis : ' Welcome Back ' Essay
Slide 1: BONNIE Welcome back! This power point is not as complex or as long as the previous power point. However, we'll review very interesting
concepts that you have heard before, such as estimation, hypothesis testing, and statistical significance. These are foundational concepts that will be
used when we conduct inferential statistical techniques. I hope that you find the powerpoints helpful. Please read your textbook prior to viewing this
powerpoint to enhance your understanding of the discussed concepts and formulas. As with other powerpoints, I have embedded quiz question to assist
you in evaluating your understanding of the concepts. While I provide the introduction to each powerpoint, Eric Regner, our highly regarded Production
Team Manager will narrate the powerpoint slides. Eric, thank you for your valuable contribution to the education of our social work students! Slide 2:
ERIC, START HERE. You have heard the term 'hypothesis'. For example, "You may hypothesize that this is one of the most difficult courses that I have
taken in my academic career". A hypothesis describes in concrete terms what you expect will happen in your study. We use a hypothesis when we have
prior knowledge from the findings in the literature, which assists you in making a tentative statement about the relationship between variables. Studies
that are exploratory in nature and that use a quantitative approach may use a research question instead of a hypothesis. Slide 3: There are two

Intelligence Survey Method
METHODOLOGY
Data collection
Survey Method is employed to collect the data from the respondents through structured inventory designed on the basis of objectives of the study and a
Social Intelligence test. Secondary Data have been collected through various Journals, books & internet which are restricted to the conceptual
framework of the paper only.
Sampling design The population has comprised of Higher Secondary School Students in Palakkad District. A convenient sampling size of 360 students
of respondents has been selected using stratified random sampling.
Tools
SNS usage inventory developed and standardised by the investigator was used for getting the SNS usage level of the sample. It consists of two parts.
First part containing 12 ... Show more content on Helpwriting.net ...
DEPENDENT VARIABLES GENDER N MEAN S.D. 't' VALUE
SNS USAGE MALE 180 149.05 38.34 0.994** FEMALE 180 153.34 43.48
SOCIAL INTELLIGENCE MALE 180 143.39 48.86 1.558** FEMALE 180 150.98 43.36
** – not significant at 0.05 level df = 358
H0 – There is no significance of difference between the male and female students with reference to their SNS usage and Social Intelligence.
Table 1 shows that the 't' value for the SNS usage (0.994) and Social Intelligence (1.558) are lower than the table value 1.96. Hence, it is concluded that
there is no significant difference between the mean scores of male and female students when consider their SNS usage and Social Intelligence. Thus the
framed null hypothesis is accepted.
Table 2. Significance of difference between Mean Scores of SNS usage and Social Intelligence of HSS students with respect to their Locality of School.
DEPENDENT VARIABLES LOCALITY N MEAN S.D. 't' VALUE
SNS USAGE RURAL 180 148.89 42.30 1.071** URBAN 180 153.51 38.40
SOCIAL INTELLIGENCE RURAL 180 149.14 45.78 0.803** URBAN 180 145.22 46.83
** – not significant at 0.05 level df = 358
H0 – There is no significance of difference between the rural and urban school students with reference to their SNS usage and Social Intelligence.
Table 2 shows that the 't' value for the SNS usage (1.071) and Academic Achievement (0.803)

Research Study For An Efficient Study Design
A research study should not only start with an efficient study design to address the hypothesis of the study but also the determination of the appropriate
number of participants in the study. The sample size is dependent on the study design, on statistical analysis used to answer the study questions, and on
the anticipated association between the outcome and the risk factor. The size a sample should not be too large because it wastes money and time both to
the investigators and participants involved in the study. The small size sample may lead to inaccurate results and causes to waste time and resources. In
addition to the other reasons, participates in a large sample size may be exposed to unnecessary risk. Therefore, the researchers should take careful
attention while determining the appropriate sample size for their studies to avoid the question of ethical practices.
The role of researchers is define the main objective of the study or the research question and the study design before selecting a representative sample
size. Then they consider the proportion of participants, and the value of the numbers to put in the formula before determining the sample size.
Kamangar & Islami (2013) states that the non–statistical consideration in determining the sample size are the availability of the fund, ethical issues,
number of participants available, the novelty of the study, and any similar studies being conducted etc. The investigators give the rationale why their
study is

Essay on Juvenile Incarceration
Running head: FINAL PROJECT: JUVENILE INCARCERATION
Final Project: Juvenile Incarceration
Roshon Green, Jessica Mays, Karen McCord
University of Phoenix
Final Project: Juvenile Incarceration
Statement of Problem
The purpose of the juvenile incarceration project is to gain insights into whether or not parental incarceration is related to juvenile incarceration. The
research problem is the loss is the cost of incarceration to the state or society. Incarceration is expensive with costs to society for the crimes committed
and the resulting confinement of the convicted offenders. This research hopes to diminish this problem by determining a correlation between juvenile
offenders and whether or not their parents were previously or ... Show more content on Helpwriting.net ...
The survey included two questions:
Are one or both of your parents incarcerated? Yes  No
What is your gender? Check one:  Male  Female
 Primary research
Data Collection
The data for this project was collected by administering an anonymous survey to incarcerated juveniles at (name of facility), the (name) receiving
center and at the NAACP office in Sacramento, California. The survey asked for gender and parental status (incarcerated versus not incarcerated).
Participants were given a paper survey and a pencil to complete the survey. See Appendix for a copy of the survey.
Limitations This study was limited to juveniles who are protected under the law. This research team was required to sign a waiver that the participants
would never be identified. Another limitation to the study is the fact that the juveniles might not be aware of the previous incarceration status of their
parents.
Statistical Methods
Chi square(1 page explaining percentages)
Based on the ##, the implication is ...
Define the assumptions
Define the methods
Describe what the test does
Results
There were a total of 41 surveys completed. Thirty one were completed at the juvenile jail (name of facility) and 9 more completed either at the (name)
receiving center or at the NAACP office. The initial 32 participants incarcerated either had a pending court date or were already convicted. The other 9
participants had been

Intimate Partner Violence Essay
CITATION:
Arroyo, K., Lundahl, B., Butters, Rob. Vanderloo, M., & Wood, D.S. (2017). Short–term interventions for survivors of intimate partner violence: A
systematic review and meta–analysis. Trauma, Violence, & Abuse, 18(2) 155–171.
SYNOPSIS
The above listed five authors, employed at the College of Social Work, University at Utah, Salt Lake City, UT. USA was the persons that produced the
systematic review (SR). Those same persons were all involved in conducting this review. This review focused on short–term psychotherapeutic
modalities used when working with survivors of intimate partner violence (IPV) in both community settings and shelters. Accordingly, potential
reports, to be considered, were only studies that could be identified ... Show more content on Helpwriting.net ...
The authors included this information via a detailed Table number three. Accordingly, this detailed table identifies and organizes the nine different
categories with examples of outcome measures that were included in each category. Effect size, number of measures, confidence level, z–scores, and p
values. Specific statistical findings are explained in tables 4 & 5. Identified in Table five was five of the target areas, which had large effect sizes,
including PTSD, self–esteem, depression, general distress, and life functioning. Four targeted areas had effects in moderate range; substance use/abuse,
emotional well being, safety, and recurrence of interpersonal violence. All nine targeted outcomes reached a level of statistical significance.
Credibility
The topic in this systematic review is clearly defined in the abstract & the introduction. Yes, the search for studies and other evidence was
comprehensive and unbiased as it was able to be. Strict criteria were followed as described in Figure 1.
Yes, the screening of citations for inclusion in this review was based on explicit criteria, as they wanted to promote confidence in the outcomes
appropriate guidelines were followed.
Yes, the included studies were evaluated for quality by these authors. Results of the review were reported at the effect level and study level. This was
done through systematic review with qualitative summary of identified studies.
Inclusivity of data and

Essay On Fdi
RESEARCH METHODOLOGY
In order to meet the objectives of the study to analyses the Impact of Foreign Direct Investment on Indian Economy, annual data have been collected
from 2007–2016. However to make analysis between financial performance of FDI based Companies and Non FDI based Companies listed at BSE for
10 years has been considered. This study is based on secondary data. The required data have been collected from CMIE Prowess IQ data base.The tools
used in the study are panel data Fixed Effect Model, Random Effect Model, Hausman test and Chow test. The sample size is selected on the basis of
FDI definition given by IMF i.e. if foreign shareholding is 10% or more than 10% in the company that company will be considered as FDI based ...
Profitability = f (Firm Quality Variables, Financial Variables) ––––––––––––––––––––––––––––––––––––––––––––––––––––(1)
FDI Based Companies
ROAFDI= α+ β1 Age + β2Size + β3CR + β4QR + β5DTER + β6GSales + β7GPAT + β8GAssets + e
–––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––
(2)
Non FDI Based Companies
ROANONFDI= α+ β1 Age + β2Size + β3CR + β4QR + β5DTER + β6GSales + β7GPAT + β8GAssets + e
–––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––
(3)
ANALYSIS AND INTERPRETATION
Food and Agriculture Sector
In the study Hausman test is done to know among fixed effect and random effect model which model is more suitable for 23 FDI based companies and
20 Non FDI Companies in Food & Agriculture sector, the results shows that for FDI based companies Random effect model is suitable (H =16.97,
P=0.07) where as for Non FDI Companies fixed effect model is suitable (H=13.18 P=0.21). To know is there is any difference in the financial
performance of FDI based companies and Non FDI Companies model is estimated separately. In table 2 the result of FDI based companies and Non
FDI Companies in Food & Agriculture Sector from 2007 to 2016. Then Chow test is done to examine whether the coefficients obtained from the two
samples are statistically different. The Chow test found to be F=0.21, thus F>F0.01

Word Frequency and the Generation Effect Essay
WORD FREQUENCY AND THE GENERATION EFFECT
ABSTRACT
This report aimed to investigate the generation effect occurs for low frequency words. The experiment used a sample of 117 second year Research
Method students from Birkbeck Univerity in within and between subject design. There were two independent variables, read and generate items and
two dependant variables, low and high frequency. This data was analyzed with related sample t test to examine whether the generation effect occurs for
low frequency words and independent sample t test to investigate whether there is a difference between generation effect with low and high frequency
words. The results show that there is significant difference between generate and read condition ... Show more content on Helpwriting.net ...
They rejected the lexical activation hypothesis stating that the generation effect may be dependent upon familiarity and associations of the to be
remembered items with other words and concepts. Therefore, would the generation effect occur for low frequency words if they were more familiar for
the subject?
Furthermore, the aim of this report is to carry out the experiment on the word frequency in generation effect and to find out whether there is generation
effect for low frequency words and whether the difference between word frequency level has an influence on the occurrence of the generation effect.
Hypothesis 1 : the generation effect will be present with low frequency words.
Hypothesis 2: the generation effect is smaller for low frequency words than for high frequency words.
Null hypothesis 1: there will be no generation effect for low frequency words.
Null hypothesis 2: there will be no difference between generation effect in low and high frequency words.
METHOD
Participants
A hundred and seventeen students at Birkbeck Univerity volunteered in the experiment held during Research Method lecture.
Materials
The materials used in this experiment were fifty six concrete nouns taken from the Kucera and Francis (1967) word count. There were four sets of the
stimuli. Two sets were formed of 28 nouns having counts of 67 or more (high frequency) and other two were 28 nouns having counts of 10 or fewer
(low frequency).

The Key Variables Of The Study Of Student Competency...
Procedure The key variables of the study, student competency proficiency, were based on ten core competencies of EPAS (CSWE, 2012). Total 19
competencies were included in the survey, using a six–point Likert scale. Students were required to rate their competency proficiency on the scale from
1 (very little know) to 6 (substantial amount know) on Survey Monkey. Three assessments used a uniform survey for three different times. Prior to the
course, each student was required to complete the first assessment. At the end of the course, students were invited to complete two assessments, the
post–test and the retrospect–test, which asked students to look back to the beginning of the course. Students were explained that their participation in
the assessments were voluntary, which focuses on helping them assess their competencies from different time perspectives, as well as helping
instructors improve the course. The leading instructor and the teaching assistant, who conducted the study, complied the rules of human subject
protection approved by the University of South Carolina Institutional Review Board. All data were transformed from Survey Monkey platform to SPSS
files. The teaching assistant cleaned the data sets, matched the data of pre–test, post–test, and retrospect–test using student IDs, and merged eligible
data from six data sets of six classes into one data set. Totally 48 matched observations were used to analyze using paired t–test. Paired t–test were used
to examine

The Effects Of Restorative Justice On Juvenile Recidivism
Literature Review
Current research available on the effects of restorative justice on juvenile recidivism range from meta–analyses of multiple studies to individual
program studies. According to Bradshaw and Roseborough (2005), "The use of meta–analytic methods provides a useful means for summarizing
diverse research findings across restorative justice studies and synthesizing these findings in an objective manner." (p. 19). Four meta–analytic studies
reveal an overall reduction in juvenile recidivism (Bradshaw and Roseborough 2005; Bradshaw, Roseborough, & Umbreit, 2006; Latimer, Dowden, &
Muise, 2005; Wong, Bouchard, Gravel, Bouchard, & Morselli, 2016).
Bradshaw and Roseborough's (2005) meta–analysis is comprised of data from 19 ... Show more content on Helpwriting.net ...
The results of this meta–analysis were similar to the others. Wong et al. (2016) reported a positive result in the overall effectiveness of restorative
justice on juvenile recidivism. Data analysis revealed 12 out of the 21 studies had a statistically significant effect size of lowered recidivism of
restorative justice participants compared to juveniles in the traditional justice system (Wong et al., 2016).
Latimer, Dowden, and Muise (2005) showed comparable results of the effectiveness of restorative justice programs by conducting a meta–analysis. 22
studies were collected through a comprehensive literature search and experts were consulted to reveal any unpublished research pertitnant to the effects
of the restorative justice program, VOM on juvenile recidivism (Latimer et al., 2005). The outcome measures for this study focused on recidivism,
along with restitution compliance and victim and offender satisfaction (Latimer et al., 2005). Juveniles that participated in each study were assigned to
either VOM groups or traditional justice comparison groups. The overall results of the meta–analysis showed a positive effect size of .07 on juvenile
recidivism (Latimer et al., 2005).
Another meta–analysis that focused on victim–offender mediation was by performed by Bradshaw and Roseborough, along with Umbreit (2006). This
meta–analysis consisted of 15 studies with 9,172 juvenile offenders, from 21

What Are The Different Weighting Methods Used For...
Results
This section describes some of the different weighting methods for combining effect sizes across studies when conducting meta–analysis. This section
will focus on methods used for binary outcome data and effect measures such as relative risks and odds ratios. The section will first introduce fixed
effect and random effects analyses. It will then describe weighting methods that can be used for these analytic approaches.
Fixed effect versus random effects analyses
Fixed effect and random effects models are the two most common approaches used when conducting a meta–analysis (Borenstein, Hedges, Higgins, &
Rothstein, 2010). The distinction between these approaches is both conceptual and statistical in nature. The fixed–effect model ... Show more content
on Helpwriting.net ...
Although both fixed–effect and random–effects models will generally give more weight to larger studies with lower variance estimates, the weights will
be relatively more balanced with a random–effects model because it assumes that each of the effect sizes in the analysis are estimating a different 'true'
effect (Borenstein, Hedges, & Rothstein, 2007). Thus, random–effects models will give more weight to smaller studies with higher variance estimates
compared to fixed–effect models.
In contrast, the weights for a fixed–effect model will be more extreme than those of a random–effects model because it is assumed that the 'best'
estimate of the effect size will be from the largest study with the lowest variance (Borenstein et al., 2007). Consequently, compared to a random–effects
model, the pooled or summary effect for a fixed–effect model will be closer to the effect obtained from the largest study with the lowest variance. With
respect to the variance estimates of the pooled or summary effect, random–effects model will result in higher variances than fixed–effect models
because of the added between–study variability assumed to be present.
The assumptions in a fixed–effect analysis may only be appropriate in a limited number of situations. For example, if studies included in a meta–
analysis were conducted using a similar methodology by recruiting participants in a similar manner and

The Major Problem With Nhst Essay
The Major Problem With NHST
Kirk (1996) had major criticisms of NHST. According to Kirk, the procedure does not tell researchers what they want to know: In scientific inference,
what we want to know is the probability that the null hypothesis (H0) is true given that we have obtained a set of data (D); that is, p(H0|D). What null
hypothesis significance testing tells us is the probability of obtaining these data or more extreme data if the null hypothesis is true, p(D|H0). (p. 747)
Kirk (1996) went on to explain that NHST was a trivial exercise because the null hypothesis is always false, and rejecting it is merely a matter of
having enough power. In this study, we investigated how textbooks treated this major problem of NHST. Current best practice in this area is open to
debate (e.g., see Harlow, Mulaik, & Steiger, 1997). A number of prominent researchers advocate the use of confidence intervals in place of NHST on
grounds that, for the most part, confidence intervals provide more information than a significance test and still include information necessary to
determine statistical significance (Cohen, Gliner, Leech, & Morgan 85 1994; Kirk, 1996). For those who advocate the use of NHST, the null hypothesis
of no difference (nil hypothesis) should be replaced by a null hypothesis specifying some nonzero value based on previous research (Cohen, 1994;
Mulaik, Raju, & Harshman, 1997). Thus, there would be less chance that a trivial difference between intervention and control

Bilingual Cooperative Integrated Reading And Composition
–– Describe the topic. What is the intended target of the intervention and the goal?
Bilingual Cooperative Integrated Reading and Composition (BCIRC) is the intervention program designed to help Spanish–speaking English Language
Learners(ELL) students successfully read in Spanish and make a successful transition to English reading. The target of the intervention was the
Spanish–dominant ELL students who attended seven elementary schools which had the highest rates of poverty and the lowest levels of student
achievement in El Paso, Texas. The 222 Spanish–speaking ELL students in the second (n = 120) and third (n = 102) grades initially participated in the
study and were assigned to the mixed–ability (high, medium, and low achieving) ... Show more content on Helpwriting.net ...
Although the study authors found some statistically significant differences in the mean reading achievement test scores between the BCIRC students
and the comparison group students in the subgroup findings, the WWC did not confirm the statistically significant positive effects of this finding
because only one study was reviewed.
English language development: The study authors did not find any statistically significant differences in the mean language development test scores
between the BCIRC students and the comparison group students both in the study findings and the subgroup findings. However, the WWC reported that
the intervention had potentially positive effects on English language development reading achievement because the study showed the substantively
important positive effect with the large effect sizes (.29 in the study finding /.38, .22, .53 in the subgroup findings), and it also did not show a
statistically significant or substantively important negative effect. However, the WWC did confirm the statistically significant positive effects of this
finding because only one study was reviewed.
The WWC also provided the Improvement index for reading achievement (+23 percentile points) and English language development (+11 percentile
points) based on the effect size. Each improvement index represents the positive difference between the percentile rank of the

Mediating Variables Essay
1) Discuss the concepts of mediating and moderating variables. Be sure to clarify how these concepts are different and how they are similar.
The reason why one variable effects another does not always occur in isolation, there are both mediating and moderating variables that may influence a
causal relationship.
Mediating Variables – They are a necessary third variable between two items. That is, without mediating variable, "b", variable "a" will not produce a
specific effect on variable, "c." (Crano & Brewer, 2002)
Moderating variables – A third variable that can either enhance or suppress a relationship. Its existence does not cause a specific relationship to occur.
(Crano & Brewer, 2002)
These concepts are similar in the sense that they both have an effect on the relationship but are different in the sense that mediating variables are
necessary in order for the relationship to exist and moderating variables merely suppress or enhance the quality of an existing relationship; that is, the
relationship exists on its own but is either enhanced or suppressed by the moderating variable. (Crano & Brewer, 2002)
2) Review the different scales of measurement (nominal, ordinal, interval, ratio).
Nominal – The lowest, simplest, level of measurement. This scale of measurement has researchers attach labels to attributes that are not rank ordered in
any way. That is, "colors" would be a good example of a nominal scale of measurement. "Green" "Blue" "Orange" would be

Automobile Market Analysis
Table 1. Automobile Sector
From the table 1 of analysis, it can be observed that there were negative returns of automobile stocks before the implementation of GST as co–efficient
of variable C is –0.003229. However, dummy variables suggest that there were significant positive returns 0.005923 in the automobile stocks p value
0.0031whuch is less than 0.05, so in that case, H1 has been accepted.
Table 2. Banking Sector
From the table 2 of analysis, it can be observed that there were negative returns as the coefficient of C is –0.001459 which was not significant. After
implementation of GST, Banking Sector Stocks started giving positive returns as it can be observed in the table that dummy variable coefficient is ...
However, after implementation returns turned into positive returns as dummy variable shows the coefficient of 0.005148 but this return is not
significant as p value of dummy variable is 0.1210 which is greater than the significance level of 0.05, and in that case, null hypothesis H0 has been
accepted.
Table 6. Manufacturing Sector
From the table 6 of analysis, it can be observed that there were negative returns on manufacturing as the coefficient of variable C is –0.0006 but this
return is not significant as p value is 0.6893. However, there were positive returns on these stocks as dummy variable coefficient shows positive value
of 0.003311 and is not significant as p value of dummy variable is 0.1204 which is greater than the significance level of 0.05.So, in that case, null
hypothesis H0 has been accepted.
Table 7. Real Estate
From the table 7, it can be observed that there were positive returns on real estate stocks before implementation of GST as the coefficient of variable C
is 0.0015 but it is not significant as p value is 0.6159. After implementation of GST also there were positive returns on stocks but that return was more
than pre implementation, but that increase in returns after implementation is also not significant as p value is 0.5936 which is higher than the
significance level of 0.05. So, in this case, null

Forecasting Power Of Statistical Data Analytics Essay
I. PROJECT OVERVIEW
Throughout the semester, we have learned different aspects of Big Data analytics and their practicalities. Forecasting and prediction are another
important parts of data analytics. Advanced forecasting analytics is playing a vital role in the age of Big Data, such as predicting crime activities,
weather changes, electric power generations, or personalizing marketing campaigns.
The purpose of this report is to demonstrate the forecasting power of statistical data analytics. We will use a time series dataset to conduct the
forecasting, since this type of datasets contain a set of observations generated sequentially in time. Organizations of all types and sizes utilize time
series datasets for analysis and forecasting for predicting next year 's sales figures, raw material demand, monthly airline bookings, etc. A time series
model is useful to obtain an understanding of the underlying forces and structure that produced the data.
The rest of this paper is organized as follow: section II presents the analysis of the data with regression analysis and Tableau–based visualization.
Section III presents the conclusions and implications of the data.
II. DATAANALYSIS
The data used to conduct this analysis referred to a monthly sales and advertising expenditures of a dietary weight control product. This data was
provided by DataMarket.com. The data includes sales and advertising expenditures of 36 consecutive months, from January 1st, 2011 to December 3rd,
2013.

Qmet 252

Recommended

Recommended

More Related Content

Similar to Qmet 252

Similar to Qmet 252 (15)

More from Angela Hays

More from Angela Hays (20)

Recently uploaded

Recently uploaded (20)

Qmet 252