Some of these values are shown in the Table below.
NonMetropolitan 
Metropolitan 
Total 

Tumour stage at diagnosis 
n=366 
n=3697 
n=4063 
Localised 
183 
1836 
2019 
Regional 
154 
1568 
1722 
Distant 
29 
293 
322 
Tumour size at diagnosis 
n=339 
n=3265 
n=3608 
<2cm 
156 
1554 
1710 
25cm 
169 
1587 
1756 
>5cm 
14 
124 
138 
Age (years) 
n=384 
n=3836 
n=4220 
<=50 
31 
479 
510 
5170 
178 
1758 
1936 
7180 
96 
1016 
1112 
>80 
79 
583 
662 
Race 
n=384 
n=3801 
n=4185 
Black 
19 
474 
493 
White 
358 
3146 
3504 
Other 
7 
181 
188 
Using statistical analysis investigate whether there is evidence that the incidence of cancer in these 2 cohorts is related to tumour stage at diagnosis, tumour size at diagnosis, age of patient or recorded ethnicity of patient.
Discuss your results and report any significant association found.Explain the nature of any significant association. 25 marks
In this first question, the test using SPSS is being chosen as Correlation, where this test will be used to analyze the patient status of male breast cancer and the mortality rate of the patients. In this question, the exploratory analysis of the provided data and output file for the correlation test is attached to the SPSS output file.
This question is about the fact that breast cancer in men is lower than in women, where they have a high rate of mortality even if they are diagnosed at a later stage of breast cancer in men. This question also provides the characteristics of the breast cancer patient in relation to cancer and socioeconomic characteristics, where the male breast cancer will be discussed from both nonmetropolitan and metropolitan areas that were reported between the years 19882006. The discussion and the analysis will be based on the difference in survival rate in the two different areas of nonmetropolitan and metropolitan (Waks and Winer, 2019). In this question, the test through using SPSS is being chosen as Correlation, where this will help to establish a relationship of the male breast cancer with the survival rate in the two different areas of nonmetropolitan and metropolitan along with the different ages and races.
In the above table, the statistical result contains the result of male breast cancer, where this data contains two different characteristics of the patients such as metropolitan areas and nonmetropolitan areas. On the above table of correlation, the different variables are tumour, the total number of patients, nonmetropolitan members, and nonmetropolitan members (Britt et al. 2020). The correlation value of the total number of tumour patients is 1, where the correlation value of the nonmetropolitan number of tumour patients is .993, and the correlation value of the nonmetropolitan number of tumour patients is 1.000. This implies that there is a strong relationship between these variables. According to the provided data, age is the greatest factor for this disease, where the total number is found as 4220, where the number for the nonmetropolitan area is 384 and the number for the metropolitan area is 3836. The rate of tumour is mainly seen in the age between 5170, and 7180, where the total number is found as 1936 for the age of 5170, where the number for the nonmetropolitan area is 178 and the number for the metropolitan area is 1758. The total number is found as 1112 for the age of 7180, where the number for the nonmetropolitan area is 96 and the number for the metropolitan area is 1016. According to the provided data, the race is the greatest factor for this disease, where the total number is found as 4185, where the number for the nonmetropolitan area is 384 and the number for the metropolitan area is 3801 (Giordano, 2018). After discussion of the above section, it can be said that breast cancer of men is a rare factor where the mortality rate of men is higher than women, and the factors of race and age are dependable with the factor of male breast cancer.
Each condition contained 10 participants and after treatment the drop in their systolic blood pressure was recorded. The data in file Q2_Nov21.xls shows the drop of systolic blood pressure for each of the patients within each treatment group, following treatment. Using statistical analysis what can be said about the differences observed for the different drug treatments. 25 marks
In this second question, the test using SPSS is being chosen as descriptive, where this test will be used to analyze the patient status of hypertension through the usage of calcium blocker channel drug and measure the systolic blood pressure rate in the body. In this question, the exploratory analysis of the provided data and output file for the correlation test is attached to the SPSS output file.
This question is mainly based on hypertension patients where usage of the calcium blocker channel drug will help to control the systolic blood pressure rate in the body. In this question, the test using SPSS is being chosen as descriptive statistics, where this will help to reach a conclusion of the distributed data and identify the different variables to conduct the statistical analysis (Qureshi, et al. 2020). This statistical test will help to analyze the systolic blood pressure rate in the body that will be controlled through the usage of calcium blocker channel drugs for hypertension patients.
In the above table, the statistical result contains the result of the patient status of hypertension through the usage of calcium blocker channel drug and measuring the systolic blood pressure rate in the body. On the above table of correlation, the different variables are new drug  2 mg dose, a new drug  6 mg dose, a new drug  8 mg dose, and existing drug  6 mg dose (Tsimploulis et al. 2018). For the new drug  2 mg dose, the static range value is 6, minimum statistics value is 4, maximum statistics value is 10, mean statistic value is 7.00 with the standard error is .577, where the standards deviation statistic is 1.826, variance statistic value is 3.333, with the skewness statistic value is .000 and standard error of the skewness is .687. For the new drug  6 mg dose, the static range value is 8, minimum statistics value is 6, maximum statistics value is 14, mean statistic value is 11.00 with the standard error is .789, where the standards deviation statistic is 2.494, variance statistic value is 6.222, with the skewness statistic value is .913 and standard error of the skewness is .687. For the new drug  8 mg dose, the static range value is 14, minimum statistics value is 9, maximum statistics value is 23, mean statistic value is 13.40 with the standard error is 1.424, where the standards deviation statistic is 4.502, variance statistic value is 20.267, with the skewness statistic value is 1.380 and standard error of the skewness is .687. For the existing drug  6 mg dose, the static range value is 14, minimum statistics value is 5, maximum statistics value is 19, mean statistic value is 12.00 with the standard error is 1.183, where the standards deviation statistic is 3.742, variance statistic value is 14.000, with the skewness statistic value is .064 and standard error of the skewness is .687 (Egan et al. 2019). After discussion of the above section, it can be said that usage of the calcium blocker channel drug will help to control the systolic blood pressure rate in the body of hypertension patients, where the different dosage of drugs varies to prevent hypertension.
What can be deduced by statistical analysis about the effect of high versus low protein diet upon the weight of tumours observed in this study? 20 marks
In this third question, the test using SPSS is being chosen as a Ttest, where this test will be used to analyze the patient status of cancer and cancer prevention through the usage of a high protein diet and low protein diet. In this question, the exploratory analysis of the provided data and output file for the correlation test is attached to the SPSS output file.
This question is mainly based on cancer patients where usage of the high protein diet and low protein diet will help to prevent the cancer rate in the body. In this question, the test using SPSS is being chosen as a Ttest, where this will help to compare the average value of different datasets to determine the proper output (Rabbani, et al. 2018). This statistical test will help to analyze the cancer prevention rate by providing the high protein diet and the low protein diet.
On the above table, the statistical result contains the result of patient status suffering from cancer, where the cancer prevention table is presented here. In this presented table of statistical data the variables or the factors are observation and tumour weight, where different values are discussed such as t value, df value, mean difference value, lower and upper value (Amin, et al. 2019). In this table under the observation factor, the t value is 7.937; the df value is 19, mean difference value is 10.500, where the lower value for 95 percent confidence interval of difference is 7.73 and for the upper is 13.27. In this table under the tumour weight factor, the t value is 31.5050 df value is 19, mean difference value is 10.7650, where the lower value for 95 percent confidence interval of difference is 10.050 and for the upper is 11.480. After discussion of the above section, it can be said that there is a storing connection of the high protein along with the patient status suffering from cancer. It is seen that the tumour weight of the patient's tumour has grown faster in the rate of low protein, whereas the tumour weight of the patient's tumour has grown slower in the rate of high protein. This study was conducted among the 20 people, where the first 10 people were provided with low protein and the other 10 people were provided high protein, and the result estimated that the tumour weight for the people with low protein is more than 12 grams on average, where the tumour weight for the people with high protein is less than 10 grams in average (Reader, et al. 2019). This stated that high protein has a major role in the area of cancer prevention, where it helps to prevent the weight of the tumour compared to the low protein.
30 marks
In this fourth question, the test using SPSS is being chosen as oneway ANOVA, where this test will be used to analyze the patient status of male quadriceps muscle strength that is related to the age and height of the men. In this question, the exploratory analysis of the provided data and output file for the correlation test is attached to the SPSS output file.
This question is mainly based on the male patients where quadriceps muscle strength can be explained in the light of age and height of the patients. In this question, the test using SPSS is being chosen as oneway ANOVA, where this will help for the simultaneous examination with two variables such as the height of the patients and age of the patients (Nyberg, et al. 2018). This statistical test will help to analyze the male patients with quadriceps muscle strength in relation to the age and height of the male patients.
In the above table, the statistical result contains the male patients with quadriceps muscle strength in relation to the age and height of the male patients. The main factors in this statistical table are age and height that influence the quadriceps muscle strength (Javadian, et al. 2017). In this table under the age factor, the sum of square value is 4660.000, and the df value is 39, where the mean square values are 119.734, and 119.199, along with the f value is 1.004, and sig value is .501. In this table under the height factor, the sum of square value is 1698.400, and the df value is 39, where the mean square values are 50.229, and 35.755, along with the f value is 1.405, and sig value is .235. After discussion of the above section, it can be said that there is a strong connection of the quadriceps muscle strength along with the different factors such as age and height. This implies that the age of the people is the determining factor where the chance of quadriceps muscle MVC is high (Welling et al. 2019). This also implies that the factor of height is also related to the quadriceps muscle MVC, where the increase in height also affects the quadriceps muscle MVC.
Reference list
Journal
Amin, S., Lux, A. and O'Callaghan, F., 2019. The journey of metformin from glycaemic control to mTOR inhibition and the suppression of tumour growth. British journal of clinical pharmacology, 85(1), pp.3746.
Britt, K.L., Cuzick, J. and Phillips, K.A., 2020. Key steps for effective breast cancer prevention. Nature Reviews Cancer, 20(8), pp.417436.
Egan, B.M., Kjeldsen, S.E., Grassi, G., Esler, M. and Mancia, G., 2019. The global burden of hypertension exceeds 1.4 billion people: should a systolic blood pressure target below 130 become the universal standard?. Journal of hypertension, 37(6), pp.11481153.
Giordano, S.H., 2018. Breast cancer in men. New England Journal of Medicine, 378(24), pp.23112320.
Javadian, Y., Adabi, M., Heidari, B., Babaei, M., Firouzjahi, A., Ghahhari, B.Y. and HajianTilaki, K., 2017. Quadriceps muscle strength correlates with serum vitamin D and knee pain in knee osteoarthritis. The Clinical journal of pain, 33(1), pp.6770.
Nyberg, A., Saey, D., Martin, M. and Maltais, F., 2018. Test–retest reliability of quadriceps muscle strength measures in people with more severe chronic obstructive pulmonary disease. Journal of rehabilitation medicine, 50(8), pp.759764.
Qureshi, A.I., Huang, W., Lobanova, I., Barsan, W.G., Hanley, D.F., Hsu, C.Y., Lin, C.L., Silbergleit, R., Steiner, T., Suarez, J.I. and Toyoda, K., 2020. Outcomes of intensive systolic blood pressure reduction in patients with intracerebral hemorrhage and excessively high initial systolic blood pressure: post hoc analysis of a randomized clinical trial. JAMA neurology, 77(11), pp.13551365.
Rabbani, N., Xue, M., Weickert, M.O. and Thornalley, P.J., 2018, April. Multiple roles of glyoxalase 1mediated suppression of methylglyoxal glycation in cancer biology—Involvement in tumour suppression, tumour growth, multidrug resistance and target for chemotherapy. In Seminars in Cancer Biology (Vol. 49, pp. 8393). Academic Press.
Reader, C.S., Vallath, S., Steele, C.W., Haider, S., Brentnall, A., Desai, A., Moore, K.M., Jamieson, N.B., Chang, D., Bailey, P. and Scarpa, A., 2019. The integrin αvβ6 drives pancreatic cancer through diverse mechanisms and represents an effective target for therapy. The Journal of pathology, 249(3), pp.332342.
Tsimploulis, A., Lam, P.H., Arundel, C., Singh, S.N., Morgan, C.J., Faselis, C., Deedwania, P., Butler, J., Aronow, W.S., Yancy, C.W. and Fonarow, G.C., 2018. Systolic blood pressure and outcomes in patients with heart failure with preserved ejection fraction. JAMA cardiology, 3(4), pp.288297.
Waks, A.G. and Winer, E.P., 2019. Breast cancer treatment: a review. Jama, 321(3), pp.288300.
Welling, W., Benjaminse, A., Lemmink, K., Dingenen, B. and Gokeler, A., 2019. Progressive strength training restores quadriceps and hamstring muscle strength within 7 months after ACL reconstruction in amateur male soccer players. Physical Therapy in Sport, 40, pp.1018.
offer valid for limited time only*