Mean and standard deviation The median is known as a measure of location; that is, it tells us where the data are. As stated in , we do not need to know all the exact values to calculate the median; if we made the smallest value even smaller or the largest value even larger, it would not change the value of the median. Thus the median does not use all the information in the data and so it can be shown to be less efficient than the mean or average, which does use all values of the data. To calculate
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Table of Contents Executive summary The model used in the SERVQUAL states that there are three variables to measure service businesses: service quality, product quality and price/quality ratio. Another study done by Nassrin (2009) has found that there is a difference in the level of satisfaction of students from different academic levels about university restaurants. We used these theories in our hypothesis and variables formulation and we found that that the level of satisfaction of
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-Range -Interquartile rand -Mean absolute deviation -Standard deviation Interquartile range: Lp=(n+1)p/100 Range and interquartile range: easy to calculate, influenced by outliers, do not consider every observation Devations from the mean: -devotions: measure the distance between an observed performance and the mean of its distribution, devation= performance- mean -Mean absolute devation: Measure the average distance the values in a distribution are from their mean Influence of outliers: Resistance:
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Size | | n | Sample Size | | μ | Population Mean. In equations where population means of multiple variables are needed, the variable name will be subscripted, e.g. μx or μincome. | 118 | x̄ | Sample Mean. Putting a bar above a variable is standard shorthand for the sample mean of that variable, which is something you might see in equations
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X “Test Positive” “Test Negative” c d “+” a e f “-” b g h Where, (a,b) is the Marginal Probability Distribution of Condition p(Y ). Note that the rarer of the two is traditionally assigned to “+” and the probability p(a) is called the “incidence” of a. (c,d) is the Marginal Probability Distribution of the Classification p(X) (e,f,g,h) is the Joint Probability Distribution of the Condition and the Classification, p(X, Y ). Another way of representing the confusion matrix is: 1
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Standard Deviation (1 of 3) Introduction So far, we have introduced two measures of spread; the range (covered by all the data) and the inter-quartile range (IQR), which looks at the range covered by the middle 50% of the distribution. We also noted that the IQR should be paired as a measure of spread with the median as a measure of center. We now move on to another measure of spread, the standard deviation, which quantifies the spread of a distribution in a completely different way. Idea
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0.125 0.26252 1.6706271 0.14423333 MM207 Final Project 12 13 14 15 16 hours 3.290909 4 3.93 3.7127273 3.6 11 2 2 11 3 1.4214091 0 0.0098 0.11040182 0.07 2. Select a continuous variable that you suspect would not follow a normal distribution. a) My answer: my continuous variable is “Age” b) Create a graph for the variable you have
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mean is small. mean is large. standard deviation is small. 2. Question : If the population proportion is .4 with a sample size of 20, then is this sample large enough so that the sampling distribution of is a normal distribution. True False 3. Question : For non-normal populations, as the sample size (n) _________, the distribution of sample means approaches a/an __________ distribution. decreases, uniform increases, normal decreases, normal increases, uniform increases, exponential
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for two manufacturers, A and B: a. Construct a frequency distribution, percentage distribution and cumulative percentage for each manufacturer. |Manufacturer A |FREQUENCY | |Mean | 7,377.33 |Mean | 8,260.90 | |Standard Error | 135.38 |Standard Error | | |
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By the way, parametric distribution means normal, binomial distribution etc.. Probability Distribution A listing of all the outcomes of an experiment and the probability associated with each outcome. Mean of probability Distribution μ= Σ[xP(x) Variance of probability Distribution σ2= Σ[x-μ2Px] Number of Cars (x) | Probability (x) | (x-μ) | (x-μ)2 | x-μ2P(x) | 0 | 0.1 | 0 – 2.1 | 4.41 | 0.441 (4.41*0.1) | 1 | 0.2 | 1 – 2.1 | 1.21 | 0.242 | 2 | 0.3 | 2 – 2.1 | 0.01 | 0.003 |
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