Diabetes Asset Analysis Essay

The histogram has one spike that shows that high concentration of data values is below this point. This histogram might be representing a seasonal product, which customers are ordering high volume of product until they run out and order one more time. Also it might be showing the histogram of a car brand were less expensive cars are sold frequently, but in average the middle range cars are bringing to the company more capital and high luxury cars are sold more expensive and less frequently.

Iterations of analysis eliminated data points that were listed as “unusual observations,” or any data point with a large standardized residual. After 5 iterations, the analysis showed improved residual plots. Randomness in the versus fits and versus order plots means that the linear regression model is appropriate for the data; a straight line in the normal probability plot illustrates the linearity of the data, and a bell shaped curve in the histogram illustrates the normality of the data.

Using a measuring tape on the wall each member of the lab group separately stood with either side against the tape and extended the arm beside the wall as high as possible. This height was recorded in centimeters. Next each lab member separately applied chalk to their fingertips, crouched down beside the tape on the wall, and jumped as high as they could while hitting the measuring tape at their highest point. This measurement was again recorded in centimeters. Then the height the lab member’s arm extended while standing was subtracted from the height when they jumped. This number denoted the vertical height jumped. For the jump height measurements a scatter plot was constructed. In order to do this, the data was again entered into excel, highlighted, insert chart and the first scatter plot choice was chosen. It was then that it was necessary to edit our axes and other parts of the graph. The x-axis was right-clicked and format axis was selected and fixed was selected and 20.0 was entered into the blank. Following this, a trend line was added to the graph by right clicking on a data point. A drop down popped up, and from there add trend line was selected from the choices.

The following Probability Plot clearly exposes any outliers; data entries that are . . . “far removed from the other entries the data set”, (Larson & Farber, 2011. pg. 68). We can see from this plot that there are several outliers on the high and low end of the data. However, one outlier in particular could cause the conclusions made about the data to be flawed. The data point located at 98%, 4600, is of major concern, as it is causing this set of data to appear much higher. It is recommended that this entry be ignored when making conclusions about the data set as a whole.

Indicating the individual number 65 gives a 5 point range to the mean. It seems the median is the most accurate way to discribe the data set, as it uneffected by the outlier value.

8. Review the following frequency distribution. Create a histogram either by hand or by using some other

Determine if the outcome is unusual. Consider as unusual any result that differs from the mean by more than 2 standard

If the observations listed below are considered the normal parameters (range) for adults, identify and list Frank’s abnormal observations?

The variable age is the independent variable and is a ratio level of measurement (Loiselle et al., 2011). The measure of central tendency to describe age are in table 1.2 are the mean of 57.62 which is the average age, the median which is the middle score within the distribution when all scores are organized of 58.5 and the mode of 58 which is the most frequently occurring age (Loiselle et al., 2011). The measures of variability are the range of 69 with a minimum age of 22 and a maximum age of 91, standard deviation which is the average deviation from the sample mean which is a value of 16.26, and the sample variance which is the standard deviation square and the value is 263.46 (Salkind, 2013). The distribution for this sample is described as a negative skew and the value obtained from table 1.2 is -0.22511(Salkind, 2013). A negative skew occurs when the median and the mode value are larger than the mean, within this sample the median is 58.5 the mode is 58 which is greater than the mean of 57.62, the tail would be pointed toward the left (Salkind, 2013). The kurtosis value is -0.65102 and this describes how peak or flat the curve is from the normal distribution curve which is described as mesokurtic (Salkind, 2013). The kurtosis has a large negative value which is representative of a flatter curve also know as playkurtic (Salkind, 2013).

The summary includes variance, mean, median, mode and standard deviation. As shown in the histogram majority of people in the data pool have a height of 62-68 inches. This is a symmetrical distribution seeing how close the mean and median are to each other.

| Based on explicit knowledge and this can be easy and fast to capture and analyse.Results can be generalised to larger populationsCan be repeated – therefore good test re-test reliability and validityStatistical analyses and interpretation are

For this scatterplot the coefficient of determination is no longer very close to zero. This means that the slope may be significantly different from zero which in turn would mean that there is a significant correlation between the two variables. In order to check this we consider a hypothesis test for the slope of the regression line. The null and alternate hypotheses for this test are

Evi’s mean amount of sugar daily was 124.97 grams, and Izzy’s was 28.71 grams. The recommended amount of sugar daily for us is approximately 75 grams, including natural and added sugars. Evi’s sugar intake was very high most days because she included a lot of fruit in her diet that had natural sugars, which greatly increased her sugar intake. Izzy, on the other hand, had little to no fruit in her diet, therefore had very low sugar amounts. Also, because Evi had a daily intake of fruit, heer sugar amounts were pretty even throughout the whole week. Izzy did not have foods with primarily sugary properties all throughout the week, so her data had extreme highs and lows, such as 1 gram of sugar one day, and 50 grams another day. Izzy had a standard deviation of 18.16, and Evi had a standard deviation of 26.51. This shows that even though Izzy had varied data in relation to the scale of her data collection, Evi’s larger amounts of sugar caused her to have a bigger range and more varied data, which is why she had a higher standard deviation. The visual representation of

With c_ij is the variance-covariance matrix entry into the i-th row and j-th column. For the case of data with outliers, should be given different weight to each datum. Search weighting in this paper done by the sequential statistical methods in finding quartile for grouping outliers. Outliers become outliers categorized as minor, major and mix. Major outlier is defined as outliers by the formula interquartile multiplied by 3, while the minor is defined as an outlier outliers as outliers by the formula interquartile multiplied by 1.5.

The Figure. 15 shows the scatter plot of Score2 vs. Score1. It is clear that the 15th observation has the