Variance Analysis
Amilca Simeon
Grand Canyon University

Variance Analysis
This is a paper to explain the variance in the monthly budget for the hospital department. This will determine if the budget cause our department to run efficient and effective. While the variance analysis is the best way to measure performance the results in the monthly budget showed that salaries were higher and supplies came in lower. What variances allowed salaries to be higher while cost for supplies remained low? In order to do this we must determine the reason for variance in the department budget. The main factors to determine the change can be: input prices, productivity, or departmental volume (Cleverly 2011). First we must determine if the budget was knowing what the variance determine will allow the manger to know what action need to be taken t maintain budget. Let look at the higher salaries over the actual outcome. What the manager must do is identify the cause of the variance and can it occur repeatedly. How are the variances of higher salaries affecting the budget financially? What can be or needs to be done to fix the cause for the variance? The variance report will address if we have address the current trends to determine if we are overspending or under spending for our budget goals. During this time we must revisit the budget to determine if the budget goals are realistic to current trends. This should reveal if we have a serious issue or if minor adjustment need to be made for next month budget. The comparison of the rise in salaries while the supplies cost remained low. Since supplies were lower it made a positive variance for the budget, while salaries made a negative variance. Did the variance cause a break even for the explanation of higher salaried? The explanation of the higher salaries payout will need an explanation because it causes a negative variance. What needs to be determined...

...appear to be large.
(Half normal plot showing the significant factors)
From the minitab output given above after conducting an analysis of factorial design and showing the half normal plot it appear that the Pouring temp (A), Titanium content (B), Heat treatment method (C), and amount of grain refiner used (D) are all large and significant as well as the interaction between Pouring temp (A) and Titanium content (B), and interaction between Titanium content (B) and Heat treatment method (C), and finally the interaction between Pouring temp (A), Titanium content (B) and Heat treatment method (C).
A, B, C, D, A&B, B&C, and A&B&C
b) Conduct an analysis of variance. Do any of the factors affect the cracking? Use α = 0.05.
From the analysis of variance minitab output given above we can conclude that the following factors affect the cracking for having a p-value < α=0.05:
* Factor (A) Pouring temperature
* Factor (B) Titanium content
* Factor (C) Heat treatment method
* Factor (D) Grain refiner
* Interaction of (A) and (B)
* Interaction of (B) and (C)
* Interaction of (A), (B) and (C)
c) Write down the regression model that can be used to predict crack length as a function of the significant main effect and interaction you have identified in part (b).
From the analysis of variance given in part (b) we can...

...
VarianceAnalysis
HCA-530
Sue P. Gombio
Grand Canyon University
VarianceAnalysis is utilized to support the management during the initial stages. It is the procedure of investigating each variance between the actual and budgeted costs to determine the reasons as to why the planned amount was not met, in more detailed explanation (Ventureline, 2012). There are several influences that contribute to the variance report and one is the department’s assumptions, second is the possible risk for this assumption, and third is the actual expense used for the budget. Let’s say the CEO or Director announces the monthly budget that the department needs to meet. Once the department receives the monthly budget outcomes, the budget for supplies was not properly utilized; therefore the salary is higher than the premeditated budget.
Once a monthly budget is received for a given month, the managers have to plan on how to use the given budget wisely. It’s true that the employees need some office materials or equipment to get the job done and there are certain areas where the budget needs to be dispensed accordingly. An example to this is the healthcare industry, specifically within the supplies department where the demand for medical supplies is high and needs to be available whenever needed. For the department to run efficiently they need a reliable technology that could help them...

...APPLIED STATISTICS
TUTORIAL 3: ANALYSIS OF VARIANCE (ANOVA)
1. When ¬¬¬¬¬¬¬¬¬¬¬¬¬¬¬more than two population means are compared, one uses the analysis of variance technique.
2. The distribution used for analysis of variance is F test.
3. Analysis of variance is used to ______________________________.
A. compare nominal data.
B. compare population proportion.
C. simultaneously compare several population means.
4. In ANOVA, F statistic is used to test a null hypothesis such as:
A.
B.
C.
5. If an ANOVA test is conducted and the null hypothesis is rejected, what does this indicate?
A. Too many degrees of freedom
B. No difference between the population means
C. A difference between at least one pair of population means
6. Experiments on accuracy of three types of measuring devices have been implemented. The results were analyzed using SPSS and generate as a following OUTPUT 1:
OUTPUT 1
Sum of Squares df Mean Square F Sig.
Between Groups
Within Groups
Total 9.650
S
T 2
11
13 4.825
R 2.180 .159
Based on the OUTPUT 1, determine the values R, S and T.
ANSWER:
7. Based on the OUTPUT 2, what is your conclusion on the rental rates...

...Analysis of Variance
Lecture 11 April 26th, 2011
A. Introduction
When you have more than two groups, a t-test (or the nonparametric equivalent) is no longer applicable. Instead, we use a technique called analysis of variance. This chapter covers analysis of variance designs with one or more independent variables, as well as more advanced topics such as interpreting significant interactions, and unbalanced designs.
B. One-Way Analysis of Variance
The method used today for comparisons of three or more groups is called analysis of variance (ANOVA). This method has the advantage of testing whether there are any differences between the groups with a single probability associated with the test. The hypothesis tested is that all groups have the same mean. Before we present an example, notice that there are several assumptions that should be met before an analysis of variance is used.
Essentially, we must have independence between groups (unless a repeated measures design is used); the sampling distributions of sample means must be normally distributed; and the groups should come from populations with equal variances (called homogeneity of variance).
Example:
15 Subjects in three treatment groups X,Y and Z.
X Y Z
700 480 500
850 460...

...One-way Analysis of Variance
(Abbreviated one-way ANOVA) is a technique used to compare means of two or more samples (using the F distribution). This technique can be used only for numerical data.
It consists of a single factor with several levels and multiple observations at each level. With this kind of layout we can calculate the mean of the observations within each level of our factor. The residuals will tell about the variation within each level. It can also average the means of each level to obtain a grand mean. And then look at the deviation of the mean of each level from the grand mean to understand something about the level effects. Finally, can compare the variation within levels to the variation across levels. Hence the name analysis of variance.
Used to determine whether there are any significant differences between the means of three or more independent (unrelated) groups. It tests the null hypothesis that samples in two or more groups are drawn from populations with the same mean values. And compares the means between the groups you are interested in and determines whether any of those means are significantly different from each other.
Formula
F= q MSBMSW Where: F = Fisher’s Ratio
K = Number of Columns
N = Total Number of items
MSB= SSBK-1
MSW= SSWN-K
Attitudes of the 1st, 2nd, 3rd and 4th year EHS students towards their computer subject
Attitude | Year |...

...of the MANOVA, check outcomes that test other assumptions for this statistic: equality of covariance matrices (see Box's Test) and sufficient correlation among the DVs (see Bartlett's Test of Sphericity). Also check the results of the Levene's Test of Equality of Error Variances to evaluate that assumption for the univariate ANOVAs that are run and show in the Tests of Between-Subjects Effects output. What have you found about whether the data meet these additional assumptions for the MANOVA and follow-up ANOVAs? Explain.
HINTS:
Once in the Options box, remember to check box for "Residual SSCP matrix" to get results for the Bartlett's test.
Also, remember to ask for post hoc tests for Treatment because there are more than two conditions. Profile plots also help with visualizing interactions.
6. What are the outcomes of the multivariate tests (main effects and interaction)? Report either the Pillai's Trace or Wilks's Lambda for each result, as well as the associated F-value and its statistical significance. Use the following format for notation to report each result: Pillai's Trace OR Wilks' lambda = ____; F(df, df) = ____, p = ____.
HINTS:
Use Pillai's trace if there are problems with heterogeneity of variance-covariance matrices for the DVs. Otherwise, Wilks' lambda is fine.
Eta squared cannot be calculated from the information provided in the multivariate tests results.
7. Given the results of the multivariate tests, would you now move...

...INTRODUCTION TO ONE-WAY ANALYSIS OF VARIANCE
Dale Berger, Claremont Graduate University http://wise.cgu.edu
The purpose of this paper is to explain the logic and vocabulary of one-way analysis of variance (ANOVA). The null hypothesis tested by one-way ANOVA is that two or more population means are equal. The question is whether (H0) the population means may equal for all groups and that the observed differences in sample means are due to random sampling variation, or (Ha) the observed differences between sample means are due to actual differences in the population means.
The logic used in ANOVA to compare means of multiple groups is similar to that used with the t-test to compare means of two independent groups. When one-way ANOVA is applied to the special case of two groups, one-way ANOVA gives identical results as the t-test.
Not surprisingly, the assumptions needed for the t-test are also needed for ANOVA. We need to assume:
1) random, independent sampling from the k populations;
2) normal population distributions;
3) equal variances within the k populations.
Assumption 1 is crucial for any inferential statistic. As with the t-test, Assumptions 2 and 3 can be relaxed when large samples are used, and Assumption 3 can be relaxed when the sample sizes are roughly the same for each group even for small samples. (If there are extreme outliers or errors in...