1- Preliminary arrangements
Progressively and for the sake of qualifying the proposed P-E's gap measurement context, the paper endorsed a number of acknowledged scholar techniques. The objective was to farm those techniques in order to spell out an acceptable managerial instrument, and to anticipate the concept of flexibility.
a) Questionnaire /constructs development
For developing a questionnaire, exploratory research is needed to investigate the likely determinants or attributes to be considered. Personal or focus interviews with the service users would be recommended. And for defining the scale attributes, a manager can capitalize on the previous empirical works cited in literature that are relevant to his or her own service sector. On the other hand, taking on Delphi technique would refine the multiitems questionnaire along the subscales (factors) underlined. Additionally, carrying out a pilot study would help fine-tuning the final script of the scale. However, the above approaches deduced from the works of Lam, Zhang and Jensen (2005). Alternatively, Walk –Thru- Audit practice of Fitzsimmons and Maurer (1991), cited in Olorunniwo and Pennington and Hsu (2002) could also be applied to develop a questionnaire. In their method a third party is getting familiar with the blueprint of a service sequential before experiencing the service itself, and he or she then proceeds into the walk-Thru- Audit while developing his or her own chronological questions; however, these questions are finally phrased to build the statements which are grouped into different dimensions.
b)Rating Scale development
Departing from the use of Likert scale which attaches numerical descriptors to different verbally described categories, and...
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...e grand mean, , which is the average of the four samples’ means.
c) Calculate the mean of the sample ranges, . However it is important to establish R chart to detect changes in variability over time since X-bar chart is founded on the hypothesis that process variability is constant over time (Pitt, 1994; cited in Orme and Cox, 2001); see worksheet 3 for establishing R chart.
d) Finally, -chart is constructed according to the following formulas which define the upper and lower control limits:
(1) UCL = + A2
(2) LCL= - A2
For a sample size n= 5, A2 = 0.577(the constant A2 is tabulated for various sample sizes and normally found in most statistics texts).
g) Manually ,or by Excel, or by any other sophisticated statistical software the graphical presentation of - chart can be developed (see Triola 2004:732 on how to produce the X-bar chart by Excel).
(Total the number of observations. Summarise the observations (risk and prioritise them in a list due to the final figures )
So, for figure 5 which is the means plot, we use the means plots to see if our mean will be different with the groups of data. Because when we are ale to see the visual interpretation of this section, we will come to the following conclusions, which we can the mean scores for the section that is higher than our mean scores for the section 2 and 3.
The overall average of the control Daphnia’s heart rate is 249.38 bpm. 0.01% caffeine’s average is 327.93 bpm, and the caffeine at 0.005% has an average of 268.90 bpm, both making the heart rate speed up. Ethanol had the opposite affect, 0.01% ethanol’s average heart rate for this experiment is 159.58 bpm and 0.005% ethanol had an average of 183.4 bpm. Caffeine has a positive percent change while ethanol has a negative percent change in the data chart. The percent change for 0.01% caffeine is 31.50%change, for 0.005% of caffeine it is 7.83% change and for ethanol 0.01% it is 36.01% change while 0.005% ethanol has a 26.47% change. The standard deviation for the treatments all relatively close. Caffeine 0.01% had a standard deviation of 49.77, 0.005% caffeine’s standard deviation is 58.95. The standard deviation for 0.01% ethanol is 54.19, ethanol 0.005 had a standard deviation of 49.47, and the control groups is 33.31. The p-tests show if and how significant the data
...will fall within the first standard deviation, 95% within the first two standard deviations, and 99.7% will fall within the first three standard deviations of the mean. The Empirical Rule is used in statistics for showing final outcomes. After a standard deviation is found, and before exact data can be collected, this rule can be used as an estimate to the outcome of the new data. This probability can be used for gathering data that may be time consuming, or even impossible to found. When the mean equals the median and the values cluster around the mean and median, producing a bell-shaped distribution, then we can use the empirical rule to examine the variability. In this bell-shaped data set, we can calculate the mean and the standard deviation. The mean means the average value of the set of data. The standard deviation means the average scatter around the mean.
The observation points, days of the week, are marked on the x axis and the frequency of PBA episodes is plotted on the y axis.
Hazan, C., & Shaver, P. (1987). Journal of personality and social psychology and. Retrieved from http://internal.psychology.illinois.edu/~broberts/Hazan & Shaver, 1987.pdf
chart on a later page. First, though, It probably would be good to tell a
Step 1 drawing the axis: Along the x-axis we have the cumulative percentage of households and along the y-axis the cumulative percentage of wealth. Each will range axis will span from 0 to 1.
The data results show a few different things visually from the graphs. One thing shown by the data collected was that the cream at 13° C produced the least amount of milk fat from cream. When the temperature was increased from the control (13°C) by 5 degrees the line rose, therefore indicating an increase of milk fat mass. When the temperature was decreased by 5 degrees from the control, the line rose more than the warmer temperature, indicating an even larger increase. In essence, the control produced the least amount of milk fat compared to the colder and warmer temperatures.
data on excel. By doing the charts on excel I will be able to plot all
Information was collected using a simple questionnaire consisting of 9, questions based on a Likert scale ranging from 0 (Strongly Disagree) to 4 (Strongly Agree). The layout of the Likert portion of the questionnaire was based on a similar structure to the Revised Life Orientation Test (Bridges, Carver & Scheier, 1994). 3 Yes or No questions were also included. An example of the questionnaire is shown in figure 1.
Then use the information in the text book to create a bar graph on the website above.
Now I must compare this data. In order to this, I will do a scatter
SPC Charts use a variety of tests to determine stability. Many software programs will have these as options to include when analyzing data and will even indicate the point(s) and test that each failed.
The dimensions of service quality refer to the attributes which contribute to consumer expectations and perceptions of service quality, thus serving as the determinants of consumers’ quality assessment (Rowley, 1998). The most well-known, commonly used service quality scale is the SERVQUAL, a general instrument for measuring service quality developed by Parasuraman et al., (1988). It includes five dimensions of service quality: (1) tangibles: appearance of physical facilities, equipment, personnel and communication materials; (2) reliability: ability to perform the promised services dependably and accurately; (3) responsiveness: willingness to help customers and provide prompt service; (4) assurance: knowledge and courtesy of employees and their ability to convey trust and confidence; and (5) empathy: caring, individualized attention that a firm provides its