Exercise 1

*Pre-session*

Medical treatments are noted to be having a huge amount of impact on the social lifestyle of the people. The provided chart reflects on the impact on the interceptions that are available for developing the attribute within the domain of the providing effective care to the patients.

The regression relation between adherence pre-sessions of intervention group and adherence pre-session of control group has been conducted.

From the figure 1 (refer to Appendix 1) it can be observed that the relation between these two variables is positive but near to zero. The regression relation suggested towards a weak relation between the two variables.

It explains that the adherence pre-session of control group has no effects on the intervention group. The adherence provided to asthma medication does not work properly in the pre-session programs between the two groups. So it can be conclude that the three day educational intervention does not improve the process of medication adherence between the two groups of people (Chatterjee & Hadi, 2015).

In pre adherence session the control group does impose any effects on the intervention group. As well as the control group does not trend to increase or decrease as the intervention group increase or decreased. If from the information a graph is created, it can be observed that the points will fall in a circular or horizontal football pattern which will also prove that the control group has no effect on the intervention group in adherence to the pre-session programs (Menard, 2002).

Figure 1: Regression Statistic in Adherence Pre-Session

*Post-session*

The figure 2 (refer to Appendix 2) stated regression chart reflects on the relation between intervention with adherence post-sessions information that was analyzed to test out the relation between two variables. The two variables are intervention group and control group in between which the relation should be determined to check the improvement of medical adherence.

From the figure 2 (refer to Appendix 2) calculations it can be said that the regression between the adherence post-sessions of intervention group and adherence post-session of control group is also very low and near to zero.

This reflects on the fact that the two groups have no or weak relations that helps in developing a proper understanding towards the medications programs and ensures the effective development of the system. It is clear that if the asthma medication works on the intervention group and have an increasing as well as decreasing effect on the group it is observed to be failing to bring any positive or negative effects on the control group (Sharma, 2005).

The control group does not respond with the intervention group while providing asthma medication and there is no improvement among the people of the two different groups. The data thus attained reflects on the fact that the relationship between the two variables is weak. The regression program run on the stated data reflects on the fact that there is no significant impact of the medication on the health conditions of the people (Chatterjee & Hadi, 2015).

Figure 2: Regression Statistic in Adherence Post-Session

* *

*Follow up-session*

The figure 3 (refer to Appendix 3) stated regression programs helps in developing the overall attribute about the effectiveness of the asthma medication on intervention group and control group in the follow up session that has been provided for medication. The relationship between the intervention group and control group should be measured in an effective way and for that regression process has been chosen to check the effectiveness of asthma medication for two stated groups.

The regression test defines that the relation between the two groups has been having a weak regression even when there is a positive correlation among the collected data. This could be duly justified as the regression reflects on the fact that the R square value tends towards zero.

In the follow up session also the increase or decrease state of the intervention group does not impose any kind of positive or negative effect on the control group on the basis of asthma medication. The medication system that was being provided could not enhance the health of the people this could be justified through the fact that the collected data was having weak regression. It can be predicted that asthma medication was quite less effective and low R-square result attained does not necessarily indicate that the model provides biased fit to the data (Sharma, 2005; Archdeacon, 1994).

Figure 3: Regression Statistic in Adherence Follow Up-Session

Exercise 2

The figure 4 (refer to Appendix 4) stated a linear diagram has been used for developing the trend analysis to develop the relation between the three groups on the basis of self-esteem. The three groups include Minimal, Casual and regular exercise in different times with different number of days in a week. From the self esteem exercise level, the performance of three groups is measured and it is clear that their performances have a certain amount of deviation from each other.

The three group’s performances were provided with their individual ranking or it can be said that the Self Esteem Scale of exercise has different types of effects on the groups. All the groups perform their exercise for 30 minutes and the results attained were identified to be different from each other.

It can be observed that the Self Esteem Scale exercises have a tendency to increase or decrease while the number of frequency of exercise were increased or decreased. So it is clear that the group’s performances rely on the effectiveness of the Self Esteem Scale exercises.

From the figure 4 (refer to Appendix 4) diagram it is observed that the members of regular group have the lower fluctuations in their exercise performances as it is identified to be moving in an upward direction. However, in comparison to the regular group the members of the two other groups that are minimal and casual groups have more fluctuating performances. The member of the minimal groups have moderate fluctuation rate while performing the Self Esteem Scale exercise and the graph generated suggest a moderate but upward trend. The members of the casual groups depict a high rate of fluctuation in their performances as though the group perform the exercise less than the number of performances that are given by the regular groups. From the performance line of the casual group it can be observed that the line goes towards a downward direction and frequently fluctuate in their performances (Dodge & Whittaker, 2012).

Figure 4: Trend Analysis on the Basis of Self Esteem Scale

*Physical Self Perception profile*

Here from the given data by the figure 5 (refer to Appendix 5) trend analysis was made on the basis of physical self perception profile exercise. The three groups were similar to the above stated diagram i.e. minimal, casual and regular group. These three groups perform exercises at different time and also the chances of recurrences of the same are altered for each group.

From this analysis it was observed that the performance of three groups was similar and is situated closely with another. It can be said that the exercise had a similar kind of effect on the three groups. On the basis of their numbers of the exercise it was clear that the increase in the exercise level would change the performance of the three groups and had different effects on their health.

When controlling the Physical Self Perception profile of the three group’s self-esteem level it was identified to be significantly different. Where in the Physical Self Perception exercise the performance of regular group was moderate but moved in upwards in the Self Esteem Scale the group performed very well and had less number of fluctuations. So from the result it was clear that compared to Physical Self Perception in Self Esteem Scale the performance of the regular group was different and moderate. Thus it could be identified that the level of self-esteem among the three groups are far more different in the physical self (Dodge & Whittaker, 2012; Chandler & Scott, 2011).

Figure 5: Trend Analysis on the Basis of Physical Self Perception Profile

Exercise – 3

Here in figure 6 (refer to Appendix 6) the relationship of internal subscale and perceived stress was evaluate by the helps of Regression Statistic tools.

From the figure 6 (refer to Appendix 6) of the internal subscale it was observed that the correlation between the internal subscale and perceived stress is very strong. Additionally 19% of the internal is variables justified the perceived stress.

So the internal subscale had a strong effect on determining the levels of the perceived stress. From the R square value attained it could be suggested that the regression between the internal subscale and perceived stress was positive and moderate. It proved that there was a direct relation between the two variables hence the change in one variable is going to create an impact on the other (Menard, 2002).

Figure 6: Regression Statistic of Internal Subscale

Here in figure 7 (refer to Appendix 7) the relationship of chance subscale and perceived stress was evaluate by the helps of Regression Statistic tools.

Correspondingly the data even reflected on the fact that the R square data attained suggested that 35 percent of the chances justified the perceived stress variables.

It refers that the increase or decrease in the value of the chance subscale had strong similar kind of effects on the perceived scale. Here from the analysis the correlation between chance subscale and perceived stress is stronger. The data thus attained reflected on the fact that there was a strong correlation between the two variables (Chatterjee & Hadi, 2015).

Figure 7: Regression Statistic of Chance Subscale

Here in figure 8 (refer to Appendix 8) the relationship of powerful other subscale and perceived stress was evaluate by the helps of Regression Statistic tools.

The data reflected on the fact that the correlation value was positive. However, the regression data attained was identified to be having a very weak impact on the perceived stress.

Thus, it could be duly concluded that even though the variables internal and chance predicts the value of perceived stress the powerful others fails to do the same in a stronger way as the other two variables that are provided (Archdeacon, 1994).

Figure 8: Regression Statistic of Powerful Other Subscale

Exercise – 4

The figure 9 (refer to Appendix 9) diagram was created on the basis of anxiety scale of the smoker.

Here from the trend analysis it was clear that the anxiety level is very high among the smoker.

The anxiety level and the linear level in the figure 9 (refer to Appendix 9) were close to each other and it will impose a negative impact on the behavioral traits of the people. There in little or no fluctuation in trend line as it proves that the anxiety levels are likely to enhance among the smokers with the increase in the years of smoking (Chandler & Scott, 2011).

The figure 10(refer to Appendix 9) diagram was created on the basis of anxiety scale of the smoker.

Hence, it could be stated that even with the fluctuations within the linear graphs there is an upwards thrust in the trend line.

From the figure 10 (refer to Appendix 9) diagram it could be inferred that even with the fluctuations among the linear graphs the trend line suggested an upward increasing trend over the years. In some points the linear depression scale at some points it suggested that there was a significant fall in the rate of depression and showed inconsistency when completing the depression scale test (Dodge & Whittaker, 2012).

Figure 9: Trend Analysis of Smoker in Anxiety Scale

Figure 10: Trend Analysis of Smoker in Depression Scale

The figure 11 (refer to Appendix 10) diagram showed the anxiety levels of the Ex Smoker.

This diagram also concludes that the levels have increased over the years and there was a slight fluctuation in the linear graphs.

The trend line even reflects towards the upward trend of the data (Chen & Gupta, 2011).

The trend analysis was created of Ex Smoker in Depression Scale (refer to Appendix 10).

On a contrary the depression data available for the smokers were identified to be fluctuating over the years.

The data thus attained suggested a fluctuating trend and reflected on the fact that the trend line had a smooth slope. However, the trend line was noted to be having an upward trend (Chandler & Scott, 2011).

Figure 11: Trend Analysis of Ex Smoker in Anxiety Scale

Figure 12: Trend Analysis of Ex Smoker in Depression Scale

The figure 13 trend analysis was created of Never Smoked in Anxiety Scale.

From the figure 13(refer to Appendix 11) diagram the members of never smoked also had the high range of anxiety among them and the levels were almost near to linear line of anxiety that was attained for smokers.

The levels of anxiety scale did not decrease in any point and was noted to be increasing over the years (Dodge & Whittaker, 2012).

The figure 14 (refer to Appendix 11) trend analysis was created of Never Smoked in Depression Scale.

The depression trends reflect on the fact that the linear graphs has been fluctuating over the years.

This could be duly identified that the linear trends are noted to be having a moderate slope but has an upwards trend (Chen & Gupta, 2011).

Figure 13: Trend Analysis of Never Smoked in Anxiety Scale

Figure 14: Trend Analysis of Never Smoked in Depression Scale

Exercise – 5

In the following figures regression statistics were run between the twenty items and health related quality which was hypothetically assumed. Specifically the relationships were conducted on the basis of the value of R square. The values of the R square help to determine the relationship between the variable and the range of the value is 0 to 1. Here the paper evaluates the relationship turning the values into percentage which can make the calculation process easy to comprehend.

Here from the figure 15 (refer to Appendix 12), it can be observed that the regression relation between the Item number 1 and the health related quality (X Variable 1) was near to 10 percentages. From the given data of figure 16 (refer to Appendix 12) the regression between the item and the quality was one percentage. From figure 17 (refer to Appendix 12) the regression between the item and the quality was zero. The regression information of figure 18 (refer to Appendix 12) suggests that there is a positive relation between the item and the health related quality. From the figure 19 (refer to Appendix 12) it can be observed that there is a considerable positive and weak relation between the two variables. The figure 20 shows that item no. 6 had a positive but weak relation with the health related quality. From the given data in figure 21 (refer to Appendix 12) the regression between the item and the quality was zero. The regression value attained from the regression product conducted in figure 22 (refer to Appendix 12) on the item no. 8 and health related quality state that there is a positive but weak relation. From this given data in Figure 23 (refer to Appendix 12) the regression between the item and the quality was zero. Here from the figure 24 (refer to Appendix 12) graphic representation it can be observed that the regression relation between the Item number 10 and the health related quality was positive. The figure 25 (refer to Appendix 12) shows that item no. 11 had also the positive but weak relation with the health related quality. From figure no. 26 the regression between the item and the quality was zero. In figure 27 (refer to Appendix 12) the regression data shows that item no. 13 had also a positive but very weak relation with the health related quality. As of this particular data in figure 28 (refer to Appendix 12) the regression between the item and the quality was zero. From the stated regression in figure 29 (refer to Appendix 12) between the item no. 15 reflects towards a weak regression. From this given data in figure 30 (refer to Appendix 12) the regression between the item and the quality was zero. From this prearranged statistics in figure 31 (refer to Appendix 12) the regression between the item and the quality was nearer to zero. The regression relation in figure 32 (refer to Appendix 12) had been conducted between the item no. 18 and the health related quality that stated towards the fact of having a positive but weak relation. From figure 33 (refer to Appendix 12) the regression between the item and the quality tends to be closer to zero. The data present in figure 34 (refer to Appendix 12) the regression relation between the item no. 20 and the health related quality.

The figure 15 (refer to Appendix 12) refers that the 10 percent of the data had a positive influence on the quality of the health products as the relationship was positive (Menard, 2002). Figure 16 (refer to Appendix 12) denotes there was a positive relation but the increase or decrease of the value of item will impose a lesser impact on the health related quality (Bhujel, 2011). Figure 17 (refer to Appendix 12) denotes there was a positive relation but the increase or decrease of the value of item did not influence the quality that proved there was no relation between them (Archdeacon, 1994). Though in figure 18 (refer to Appendix 12) there was a positive relationship but the relation was so weak that the value of the item could not improve or deteriorate the health related quality (Sharma, 2005). In figure 19 (refer to Appendix 12) the weak relationship denotes the health quality was not dependent on the value of the item so the item could not be justified to be having a significant impact on the health related quality (Menard, 2002). Item No. 6 in the figure 20 (refer to Appendix 12) had little probability to influence the health related quality (Chatterjee & Hadi, 2015). Figure 21 (refer to Appendix 12) denotes there was a positive relation but the increase or decrease of the value of item did not manipulate the quality that proved there was no relation between them (Bhujel, 2011). The figure 22 (refer to Appendix 12) indicates if the item’s value increase or decrease, it would impose a positive but little impact on the performance of the health related quality (Chatterjee & Hadi, 2015). The data in Figure 23 (refer to Appendix 12) denotes there was a positive relation but the increase or decrease of the value of item did not influence the quality that proved there was no relation between them (Archdeacon, 1994). Figure 24 (refer to Appendix 12) it refers that the 16 percent of the data had an influence on the quality of the health products and this was best item which can manipulate the health related quality (Menard, 2002). In figure 25 (refer to Appendix 12) item no. 11 had very little probability to influence the health related quality (Sharma, 2005). Figure no. 26 (refer to Appendix 12) denotes there was a positive relation but the increase or decrease of the value of item did not influence the quality that proved there was no relation between them (Chatterjee & Hadi, 2015). From the calculation in figure 27 (refer to Appendix 12) it was observed that the item had little chances to persuade the health related quality (Bobko, 2001). Though the figure 28 (refer to Appendix 12) specified there was a positive relation but the increase or decrease of the value of item did not influence the quality that proved there was no relation between them (Bhujel,, 2011). In figure 29 (refer to Appendix 12) however, there existed positive and string correlations among the two thus this could be stated that the health quality could be influenced by the item (Menard, 2002). Figure 30 (refer to Appendix 12) denotes there was a positive relation but the increase or decrease of the value of item did not influence the quality of healthcare (Archdeacon, 1994). The regression value in figure 31 (refer to Appendix 12) reflected towards a weak regression. Correspondingly there were very less chances of influence over health quality (Sharma, 2005). In figure 32 (refer to Appendix 12), the item had slight possibility to manipulate the issues of health related quality positively (Bobko, 2001). The R square’s value in figure 33 (refer to Appendix 12) symbolizes that there was a positive relation but the value of item did not have the authority to manipulate the quality of health (Menard, 2002). The relation in figure 34 (refer to Appendix 12) observed to be having an optimistic relation and slight possibility to operate the health related quality (Chatterjee & Hadi, 2015).

Figure 15: Regression statistic of Item No. 1

Figure 16: Regression statistic of Item No. 2

Figure 17: Regression statistic of Item No. 3

Figure 18: Regression statistic of Item No. 4

Figure 19: Regression statistic of Item No. 5

Figure 20: Regression statistic of Item No. 6

Figure 21: Regression statistic of Item No. 7

Figure 22: Regression statistic of Item No. 8

Figure 23: Regression statistic of Item No. 9

Figure 24: Regression statistic of Item No. 10

Figure 25: Regression statistic of Item No. 11

Figure 26: Regression statistic of Item No. 12

Figure 27: Regression statistic of Item No. 13

Figure 28: Regression statistic of Item No. 14

Figure 29: Regression statistic of Item No. 15

Figure 30: Regression statistic of Item No. 16

Figure 31: Regression statistic of Item No. 17

Figure 32: Regression statistic of Item No. 18

Figure 33: Regression statistic of Item No. 19

Figure 34: Regression statistic of Item No. 20

References

Archdeacon, T. J. (1994). *Correlation and regression analysis.* United States: Univ of Wisconsin Press.

Bhujel, R. C. (2011). *Statistics for aquaculture.* New Jersey: John Wiley & Sons.

Bobko, P. (2001). *Correlation and regression.* New York: SAGE.

Chandler, R. & Scott, M. (2011). *Statistical methods for trend detection and analysis in the environmental sciences.* New Jersey: John Wiley & Sons.

Chatterjee, S. & Hadi, A. S. (2015). *Regression analysis by example.* New Jersey: John Wiley & Sons.

Chen, J. & Gupta, A. K. (2011). *Parametric statistical change point analysis.* United States: Springer Science & Business Media.

Dodge, Y. & Whittaker, J. (2012). *Computational statistics.* United States: Springer Science & Business Media.

Menard, S. (2002). *Applied logistic regression analysis.* New York: SAGE.

Sharma, A. K. (2005). *Text book of correlations and regression.* Delhi: Discovery Publishing House.

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