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Weekly assignemnts - Speech or Presentation Example

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These variables are labeled as ADDSC, Gender either male or female (respondent), Repeat either 0 or 1, IQ, EngL, EngG, GPA, SocProb and Drop out.
Figure 2 illustrates the descriptive statistics for…

Extract of sample"Weekly assignemnts"

Download file to see previous pages The arithmetic mean is explained in terms of the sum of all observations divided by the total number of observations. The arithmetic mean provides the average value for each of the variable. Therefore the average score for ADDSC is 52.6, for IQ is 100.26 which is very good, for EngG is 2.66 and for GPA is 2.46.
The standard deviation can be explained as “sum of the squared deviations about the mean divided by N-1” (Howell, 2011, pp. 87-88). Since it is measure of dispersion it allows us to predict how much dispersed the data point lie from the average value. In the given case the standard deviations for GPA (0.86), EngG (.95) and EngL (.52) are small and exhibit that the average value for each variable presents the data reasonably well since 66% of the data lie within 1 standard deviation of the arithmetic mean. The standard deviation for ADDSC (12.42) and IQ (12.98) is high suggesting a platykurtic curve, although the mean seems to represent the data very well in such case too.
GPA, EngL and EngG exhibit a slightly negatively skewed data (-.264, -0.06 and -.352), whereas IQ, ADDSC show a slightly positively skewed data (.395 and 0.04). All variables exhibit kurtosis that is somewhat platykurtic. Besides all the statistics discussed are within the normal range of -2 to +2, therefore it can be said that the sample has a symmetrical and normal distribution. The descriptive statistics for SocProb, Dropout, Gender and Repeat seem meaningless.
In order to employ Pearson Coefficient for correlation analysis the only one distributional assumption is needed i.e. linearity. Pearson coefficient assumes that the association between the variables under investigation can be defined using a straight line and the most feasible way to explore linearity is by constructing a scatter plot. The Inferential procedure necessitate that the joint distribution of desired X ...Download file to see next pagesRead More
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