- QUESTION
Dissussion board
Ms. C, 72 years old, has diabetes mellitus, hypertension, rheumatoid arthritis (RA), and coronary artery disease. She lives alone. She recently was discharged from the hospital after treatment for an acute myocardial infarction. She had been doing well before this admission. You are the home health nurse assigned to visit Ms. C. Ms. C’s son tells you he is concerned that his mother will not follow up with all the medications she is supposed to take. He states that she has not been very compliant with treatment in the past.
As the home health nurse, how would you approach Ms. C?
What risk factors might interfere with Ms. C’s plan of care?
What must you evaluate to include in the plan of care?
Titles of books, web pages, and journals should be italicized in the reference list for all references. There should be in text citation within the body of all postings for each reference listed in the reference list. Credentials, such as PhD, should not be listed within the reference list. When 3 or more author or listed for a work, in text citation should include the first authors' last name, followed by et al.FA2020 - RNSG 1301 Discussion Board Grading Rubric 20 Points 10 Points 5 Points 0 Points Points Earned Fully answers the prompt by Wednesday (11:59 p.m. CST) of the week assigned. Initial posting contains at least 2 references.
*Initial post must be aminimum of 150 words.
Fully answers the prompt by Wednesday (11:59 p.m. CST) of the week assigned. Initial posting contains less than 2 references.
*Initial post must be aminimum of 150 words.
Fully answers the prompt by Wednesday (11:59 p.m. CST) of the week assigned. No references included.
*Initial post must be aminimum of 150 words.
No initial posting by Wednesday (11:59 pm CST) or posting is less than the minimum of 150 words. /20 Initial posting is rich in content full of thought, insight, and analysis. Initial posting has substantial information. Thought, insight, and analysis have taken place. Initial posting is generally competent. Information is thin and commonplace. Initial posting is not relevant or no subject matter knowledge is evident. /20 30 Points 20 Points 10 Point 0 Points Points Earned Provides a minimum of two substantive peer replies with reference by the end of the week (Sunday 11:59 pm). A minimum of one (1) reference is required per peer reply.
*Each peer reply must be aminimum of 125 words.
Provides replies to two peers with simple comments and/or reference(s) not provided. A minimum of one (1) reference is required for each peer reply. *Each peer reply must be a minimum of 125 words. Provides only one peer reply by the end of the week (Sunday 11:59 pm). Appropriate references are not included on both peer responses.
*Each peer reply must be aminimum of 125 words.
No replies to peer postings or postings are less than the minimum of 125 words. /30 15 Points 10 Points 5 Point 0 Points Points Earned Correct APA format of in-text citations (within the body of the posting) and references (in the list). Both are required to earn any points. Less than 2 APA errors. 3-4 APA errors. No use of APA format or 5+ errors. /15 5 Points 3.5 Points 2 Points 0 Points Points Earned Spelling, grammar, punctuation, mechanics and word usage are correct and consistent with Standard American English. Spelling, grammar, punctuation, mechanics, and word usage are adequate and consistent with Standard American English; errors do not interfere with meaning or understanding. Spelling, grammar, punctuation, mechanics, and word usage are distracting and could interfere with meaning or understanding. Spelling, grammar, punctuation, mechanics and/or word usage interfere with understanding and do not reflect scholarly writing. /5 All references are relevant, scholarly, and less than 10 years old. Some references are not relevant, scholarly, or less than 10 years old. No use of references. /5 5 Points 2.5 Points 0 Points Points Earned Posts indicate respect and consideration for peers and instructor and are written using scholarly language. Posts indicate respect and consideration for peers and instructor but are not written using scholarly language. No use of professionalism & netiquette. /5 Total Points /100
| Subject | Business | Pages | 7 | Style | APA |
|---|
Answer
-
-
-
-
Estimation of Property Price and Budget: A Literature Review
Correlation and regression analysis are objective measures of identifying variables that can determine the price of a property in the market. Correlation analysis was conducted so as to determine whether variables such as the number of bedrooms, the number of bathrooms, and the size of the floor space in square feet were significantly correlated to each other. Regression analysis was then performed to determine the fixed cost of the property and the impact of the above variables in influencing the price of the property. The number of bedrooms and the number of bathrooms are two major determinants of the price of a property as well as for increasing the price of a property; the size of the floor space has an insignificant effect.
Correlation coefficient of 1 implies that for every positive increase in one variable it results in a proportional (fixed) positive increase in another variable (Armstrong, 2019). Correlation coefficient of 1 indicates positive relationship between variables (Bakar et al., 2019; Humphreys et al., 2019). Multivariate regression models are important in predicting the real estate value. Real estate is competitive in terms of pricing; hence, statistical tools should be used in setting property prices (Breuer & Steininger, 2020; Munjala et al., 2020). Variables that were employed in the study include the price, the number of bedrooms, the number of bathrooms, and the square feet of the property. It indicates that any change in any of the variable would result in a proportional change in any of the variable in a linear manner. The measure of correlation coefficient (R or r) indicates closeness of two variables, regardless of non-linear correlation (Senthilnathan, 2019).
Regression analysis is a traditional econometric model for determining housing prices in the real estate market (Chen, Zhuang, & Zhang, 2020). Regression analysis can be used for both predictive and inferential purposes (Perez-Rave et al., 2019). Tidwell et al. (2019) add that regression analysis is also useful in financial decision-making process. Regression statistics established that coefficient of determination (R square = 0.6613) indicates that the model is nearly perfect. In a perfect model, R2 is equal to 1 (Jenkins & Quintana-Ascencio, 2020). The real estate auction market has grown increasingly important in the economic, financial, and investment fields. The floor space as well as number of bathrooms and bedrooms may be important in determining the real estate auction prices (Kang et al., 2020).
Regression analysis established that the price of a property is the sum of the fixed cost, the number of bedrooms, the number of bathrooms, and the square feet of the floor space. The fixed cost was found to be $105,261.10 (P-value = 0.00905; 95% confidence interval (CI)). Terms such as price, worth, cost, and value are important when selling or buying a property in real estate market. Cost is the amount of money incurred on inputs such as labor, land, enterprise, capital, and other materials used to produce a product (Olajide et al., 2016). Value is the highest price estimate in which a property can fetch when put in the open market for a sufficient amount of time to attract the right buyer (Olajide et al., 2016).
Value in use is referred to as worth. Price is the amount of money spent by the buyer to pay the seller in exchange of any service or product. Value is the utility of a service or goods (Olajide et al., 2016). Profitability is determined by substantial up-front investment in outdoor areas, infrastructure, and common amenities (Barlindhaug & Nordahl, 2017). Fixed cost is considered as the asking price for the property (Barlindhaug & Nordahl, 2017). Therefore, the asking price for the property in the market will be set at least at $105,261.10. Competitive setting of the fixed cost of a property is a credible mechanism that firms uses to exclude rivals in the market (Hviid & Olczak, 2016). The coefficient of determination/R-squared (0.6612) implied that 66.12% of the variation in the price of the property can be explained by the independent variables in the models. The independent variables in this case include the square feet of the floor space, number of bedrooms, and number of bathrooms.
Regression analysis also established that increase in the number of bedrooms results in proportional rise in the price of the property. The findings indicate that with every increase of a single bedroom, the price of the property rises by $17,835.41 (P-value = 0.2818; 95% CI); however, this effect is not significant. On the other hand, an increase in the number of bathrooms in a property by only one (1) results in an increase in the price of the property. An increase in the number of bathrooms in a given property is associate by a proportional rise in the price of the property by $79,901 (P-value=0.0005654; 95% CI). Increasing the number of bedrooms and bathrooms are countering drop of the housing prices in real estate market (Castelli et al., 2020).
The other finding is that the square feet unit change does not play a significant role in influencing the price of the property. The effect of size of the property only resulted in rise of the price of the property by $21.48 (P-value = 0.04076; 95% CI). The floor place was found as an insignificant factor in rising property rise. However, Ndegwa (2018) includes the size of an apartment as an important determinant of the price of a property among other factors such as proximity to slums, periodic rental income, land value, present of swimming pool, proximity to shopping malls, presence of balcony, proximity to the central business district, and proximity to schools (Ndegwa, 2018). Access to local facilities, neighborhood amenities, and services is a key priority in urban policy (Aziz et al., 2020). The plot size is another important variable that can be used for determining the price of a property in the real estate market (Edvinsson, Eriksson, & Ingman, 2020).
In conclusion, the number of bedrooms, the number of bathrooms, and the size of the floor space in square feet are significantly correlated. However, only two variables (number of bedrooms and bathrooms) have a significant impact on increasing the price of the property. The size of the floor space in square feet is an insignificant determinant of the property price. On the other hand, increasing the number of bathrooms has a greater impact in increasing the price of the property than in increasing the number of bedrooms. Therefore, property developers should prioritize on increasing the number of bathrooms so as to have a greater impact on fetching a much greater property price in the market.
-
-
-
References
-
-
Armstrong, R. A. (2019). Should Pearson’s correlation coefficient be avoided? Ophthalmic and Physiological Optics, 39(5), 316-327. https://doi.org/10.1111/opo.12636
Aziz, A., Anwar, M. M., & Dawood, M. (2020). The impact of neighborhood services on land values: an estimation through the hedonic pricing model. GeoJournal. https://link.springer.com/article/10.1007/s10708-019-10127-w
Bakar, A. H. A., Hassan, M. N. M., Zakaria, A., & Halim, A. A. A. (2019). Pearson’s correlation coefficient analysis of non-invasive jaundice detection based on colour card technique. J. Phys.: Conf. Ser. 1372, 012012. https://iopscience.iop.org/article/10.1088/1742-6596/1372/1/012012/pdf
Barlindhaug, R., & Nordahl, B. I. (2017). Developers’ price setting behavior in urban residential redevelopment projects. Emerald Insight. https://www.emerald.com/insight/content/doi/10.1108/JERER-03-2017-0014/full/pdf?title=developers-price-setting-behaviour-in-urban-residential-redevelopment-projects
Breuer, W., & Steininger, B. I. (2020). Recent trends in real estate research: a comparison of recent working papers and publications using machine learning algorithms. Journal of Business Economics, 90, 963-974. https://link.springer.com/article/10.1007/s11573-020-01005-w
Castelli, M., Dobreva, M., Henriques, R., & Vanneschi, L. (2020). Predicting days on market to optimize real estate sales strategy. Hindawi, 2020, Article ID 4603190. https://doi.org/10.1155/2020/4603190
Chen, S., Zhuang, D., & Zhang, H. (2020). GIS-based spatial autocorrelation analysis of housing prices oriented towards a view of spatiotemporal homogeneity and nonstationarity: a case study of Guangzhou, China. Hindawi, 2020, Article ID 1079024. https://doi.org/10.1155/2020/1079024
Edvinsson, R., Eriksson, K., & Ingman, G. (2020).A real estate price index for Stockholm, Sweden 1818–2018: putting the last decades housing price boom in a historical perspective. Scandinavian Economic History Review. https://doi.org/10.1080/03585522.2020.1759681
Humphreys, R. K., Puth, M-T., Neuhauser, M., & Ruxton, G. D. (2019). Underestimation of Pearson’s product moment correlation statistic. Oecologia, 189, 1-7. https://link.springer.com/article/10.1007/s00442-018-4233-0
Hviid, M., & Olczak, M. (2016). Raising rivals’ fixed costs. International Journal of the Economics of Business, 23(1), 19-36. https://www.tandfonline.com/doi/full/10.1080/13571516.2015.1055913
Jenkins, D. G., & Quintana-Ascencio, P. F. (2020). A solution to minimum sample size for regressions. PLoS ONE, 15(2), e0229345. https://doi.org/10.1371/journal.pone.0229345
Kang, J., Lee, H. J., Jeong, S. H., Lee, H. S., & Oh, K. J. (2020). Developing a forecasting model for real estate auction prices using artificial intelligence. Sustainability, 12, 2899. https://www.mdpi.com/2071-1050/12/7/2899
Munjala, R., Jain, S., Srivastava, S., & Kher, P. R. (2020). Real estate value prediction using multivariate regression models. IOP Conference Series: Materials Science and Engineering, 263(4). https://iopscience.iop.org/article/10.1088/1757-899X/263/4/042098
Ndegwa, J. (2018). Determinants of apartment prices within housing estates of Nairobi metropolitan area. International Journal of Economics and Finance, 10(6), 104. https://www.researchgate.net/publication/325065425_Determinants_of_Apartment_Prices_within_Housing_Estates_of_Nairobi_Metropolitan_Area
Olajide, B. S. E., MohdLizam, & Olajide, E. B. (2016). Understanding the conceptual definitions of cost, price, worth and value. IOSR Journal of Humanities and Social Science, 21(9), 53-57. https://www.researchgate.net/publication/307855722_Understanding_The_Conceptual_Definitions_of_Cost_Price_Worth_and_Value
Perez-Rave, J. I., Correa-Morales, J. C., & Gonzalez-Echavarria, F. (2019). A machine learning approach to big data regression analysis of real estate prices for inferential and predictive purposes. Journal of Property Research, 36(1), 59-96. https://doi.org/10.1080/09599916.2019.1587489
Senthilnathan, S. (2019). Usefulness of correlation analysis. SSRN Electronic Journal. https://www.researchgate.net/publication/334308527_Usefulness_of_Correlation_Analysis
Tidwell, J. B., Chipungu, J., Chilengi, R., Curtis, V., & Aunger, R. (2019). Theory-driven formative research on on-site, shared sanitation quality improvement among landlords and tenants in peri-urban Lusaka, Zambia. International Journal of Environmental Health Research, 29(3), 312-325. https://doi.org/10.1080/09603123.2018.1543798
-