Correlation coefficients

Tuyển tập các báo cáo nghiên cứu về y học được đăng trên tạp chí y học quốc tế cung cấp cho các bạn kiến thức về ngành y đề tài:Formation of translational risk score based on correlation coefficients as an alternative to Cox regression models for predicting outcome in patients with NSCLC
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Chapter 8 – Correlation and regression. After studying this chapter you will be able to understand: Define and interpret a scatter plot, calculate and interpret a sample covariance, calculate and interpret a sample correlation coefficient, explain how outliers can affect correlations,...
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Phân tích tương quan (Correlation) là kỹ thuật rất thường dùng trong thống kê y học nhằm khảo sát mối liên quan giữa 2 biến số đo trên cùng các đối tượng thông qua hệ số tương quan (correlation coefficient). Có nhiều loại hệ số tương quan (HSTQ) nhưng bài này chỉ trình bày hệ số tương quan r của Pearson (Pearson r correlation coefficient). Pearson r là số đo mối liên quan tuyến tính của 2 biến số, và được sử dụng khi 2 biến số thuộc thang đo lường tỉ số hoặc thang khoảng.
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After studying this chapter you will be able to: comprehend the nature of correlation analysis, understand bivariate regression analysis, become aware of the coefficient of determination, R2. Inviting you to refer.
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Chapter 13  Linear regression and correlation, after studying this chapter you will be able to: Identify a relationship between variables on a scatter diagram, measure and interpret a degree of relationship by a coefficient of correlation, conduct a test of hypothesis about the coefficient of correlation in a population,...and other contents.
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Parametric correlation requires two continuous variables measured on an interval or ratio scale. The coefficient does not distinguish between independent and dependent variables
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C H A P T E R 7 Twoway traffic – summarizing and representing relationships between two variables Chapter objectives This chapter will help you to: ■ ■ ■ ■ ■ ■ explore links between quantitative variables using bivariate analysis measure association between quantitative variables using Pearson’s product moment correlation coefficient and the coefficient of determination quantify association in ordinal data using Spearman’s rank correlation coefficient represent the connection between two quantitative variables using simple linear regression analysis use the technology...
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In a world where ownership is divorced from control, characterised by economic and geopolitical uncertainty, our companion text Portfolio Theory and Financial Analyses (PTFA henceforth) began with the following question. We then observed that if investors are rational and capital markets are efficient with a large number of constituents,economic variables (such as share prices and returns) should be random, which simplifies matters.
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Once a company issues shares (common stock) and receives the proceeds, it has no direct involvement with their subsequent transactions on the capital market, or the price at which they are traded. These are matters for negotiation between existing shareholders and prospective investors, based on their own financial agenda.
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When sitting in statistics classes or when trying to read and understand statistical material, too many otherwise intelligent and capable students and researchers feel dumb. This book is intended as an antidote. It is designed to make you feel smart and competent. Its approach is conservative in that it attempts to identify and present the essentials of data analysis as developed by statisticians over the last two or three centuries.
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In the first edition of this book, I claimed that humanity could be divided into three groups: (1) those who conduct their own research studies, (2) those who do not formally engage in the research process but nonetheless encounter the results of others’ investigations, and (3) those who are neither “makers” nor “consumers” of research claims. Now, nearly 40 years since I made that statement, I still believe that every person on the face of the Earth can be classified into one of those three groups.
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The population elasticity is now slightly below one at 0.92. A higher rate of urbanization has the expected positive impact on emissions. The share of population in the economically active age groups now becomes marginally insignificant. The reason for this could be the high correlation between the two variables (partial correlation coefficient of 0.65). Lastly, in column V we add the average household size to our model. Note that this variable is available for all countries in the sample, but not over the entire estimation period.
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This connection can be shown empirically, e.g. for commodities. Gorton and Rouwenhorst show that the yields on commodities futures have a negative correlation with the yields on longdated bonds and – for long holding periods – with equities. Commodities futures could therefore be employed effectively in order to diversify equity and bond portfolios. 4 On the other hand, according to Yee’s analysis, high correlation coefficients are evident in a comparison of the yields on financial assets with those of metal or energy producers or with REITs.
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Note that the population share of the economically active age groups now becomes more clearly insignificant. This suggests that its initial statistical significance might be entirely due to its correlation with the urbanization rate as pointed out above and its correlation with the average household size (partial correlation coefficient of )0.58). In other words, it would appear that the urbanization rate and average household size are the demographic factors that really matter.
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A web search with double checking model is proposed to explore the web as a live corpus. Five association measures including variants of Dice, Overlap Ratio, Jaccard, and Cosine, as well as CoOccurrence Double Check (CODC), are presented. In the experiments on RubensteinGoodenough’s benchmark data set, the CODC measure achieves correlation coefficient 0.8492, which competes with the performance (0.8914) of the model using WordNet.
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The kinetics of the reversible thermal unfolding, irreversible thermal unfolding, and reductive unfolding processes of bovine pancreatic ribonuclease A (RNase A) were investigated in NaCl/Pi solutions. Image parameters including Shannon entropy, Hamming distance, mutual information and correlation coefficient were used in the analysis of the CD and 1D NMR spectra. The irreversible thermal unfolding transition of RNase A was not a cooperative process, pretransitional structure changes occur before the main thermal denaturation....
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Charities Invest in Volunteers in a Variety of Ways. Thus far, we have discussed investments in paid staff and in volunteer management practices. Hiring someone who has training in volunteer management also demonstrates a greater investment in volunteer management. To derive an overall assessment of investment in volunteer management, we combined these three items into a single measure, describe below. Investments and Benefits Vary Together. We expect that the charities that invest in volunteers will be those that say they derive greatest benefits from volunteers.
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This chapter explains the use of several techniques including correlation analysis and regression analysis. After reading this chapter, you should understand: How correlation analysis may be applied to study relationships between two or more variables; the uses, requirements, and interpretation of the product moment correlation coefficient;...
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Chapter 14  Multiple regressions and correlation analysis. In this chapter, the learning objectives are: Describe the relationship between several independent variables and a dependent variable using multiple regression analysis; set up, interpret, and apply an ANOVA table compute and interpret the multiple standard error of estimate, the coefficient of multiple determination, and the adjusted coefficient of multiple determination; conduct a test of hypothesis to determine whether regression coefficients differ from zero;...
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Chapter 14  Multiple regression analysis. This chapter include objectives: Describe the relationship between several independent variables and a dependent variable using multiple regression analysis; set up, interpret, and apply an ANOVA table compute and interpret the multiple standard error of estimate, the coefficient of multiple determination, and the adjusted coefficient of multiple determination.
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