what does regression analysis allow banking professionals to do?

Provided by the Springer Nature SharedIt content-sharing initiative, Robust Regression Analysis in Analyzing Financial Performance of Public Sector Banks: A Case Study of India, $$Min \sum \left| {e_{i} } \right|{ - }$$, $$Min \mathop \sum \limits_{i = 1}^{n} \rho \left( {r_{i} } \right){ - }$$, $$\widehat{\beta } = \arg \mathop {\min }\limits_{\beta } \mathop \sum \limits_{i = 1}^{n} \rho \left( {\frac{{y_{i} - x_{i}^{T} \beta }}{{\hat{\sigma }}}} \right)$$, $$\widehat{\beta } = \arg \mathop {\min }\limits_{\beta } Med\left\{ {\left( {y_{i - } x_{i}^{T} \beta )^{2} } \right)} \right\}$$, $$\widehat{\beta } = \arg \mathop {\min }\limits_{\beta } \mathop \sum \limits_{i = 1}^{q} r_{\left( i \right)} (\beta )^{2}$$, \(r_{i} (\beta )^{2} \le . Access more than 40 courses trusted by Fortune 500 companies. (2022)Cite this article. Real-world financial data, which is generally collected for a continuous period, shows a correlation that makes the errors in the regression model interdependent and rejects the assumption of independent errors. Legislative changes have been carried out to offer customers the same quality of experience, regardless of the service provider that they opt for. Identify the most likely time and location of fraudC. but you do need to correctly understand and interpret the analysis created by your colleagues. Nearly 48 percent of customers report theyre likely to stop using an app if theyre unhappy with its performance. The inclusion of irrelevant independent variables in the model will indeed allow a better fit with the data set under study, but, because of random effects, . And in the past, for every additional inch of rain, you made an average of five more sales. They make use of personal data that has to be protected in the . For this purpose, the following models were developed was used based on previous literature. Thats interesting to know, but by how much? Trust me. But do you know how to parse through all the data available to you? Pakistan J Appl Econ 21(1&2):1536, Kosmidou K, Tanna S, Pasiouras F (2005) Determinants of profitability of domestic UK commercial banks: panel evidence from the period 19952022. Financial crises have negatively impacted the NIM of public sector banks in India. Int J Soc Behav Educ Econ Bus Indust Eng 4:21732178. Always ask yourself what you will do with the data. The estimate of is \(\hat{\beta }\). Typically you start a regression analysis wanting to understand the impact of several independent variables. The Reserve Bank of India database has been used as the main source of secondary data for extracting data on selected parameters of the public sector from 2005 to 2018. Income diversification (NONIITI) had a negative and significant relationship with the net interest margin of public sector banks, implying that diversified income is related to the lower NIM of banks. Regression analysis helps underwriters, substantially, by running the numbers through the tool and the variables to help them remove biases and apply the right pricing to a certain loan than they . World 4(3):8296, Tan Y, Floros C (2012) Bank profitability and GDP growth in China: a note. Introduction. Then a comparison is made of asymptotic biases under contamination of optimal bounded influence estimates and MM-estimates. Robust estimation is a statistical method to analyze such financial data with outliers. You keep doing this until the error term is very small, says Redman. As with denial rates, differences in rates of override or exception do not necessarily indicate a fair lending issue, because the ability of You know that dozens, perhaps even hundreds of factors from the weather to a competitors promotion to the rumor of a new and improved model can impact the numbers. The Tennessean Names Ncontracts a Top Workplace for Third Consecutive Year, Regulatory Brief for May 2023: 1071, enforcement actions, and the consequences of persistent weaknesses, Ncontracts Congratulates David Hales, Chairman and CEO of Global Innovations Bank, For Being Named an Innovator of the Year Finalist by American Banker, How Regression Analysis Can Help Your Fair Lending Program, Related: How to Build a Strong Fair Lending & Redlining Compliance Management System, Reducing Compliance Risk with Regression Analysis. Exploring usage of mobile banking apps in the UAE: a categorical Other robust estimators include S-estimator and MM-estimators. Accessed on 10 Dec 2022, Huber PJ (1964) Robust version of a location parameter. Background Clustering of observations is a common phenomenon in epidemiological and clinical research. The problem of multicollinearity exists if the correlation coefficient is greater than 0.80. . Analyzing Performance of Banks in India: A Robust Regression Analysis This isnt the only reason why such a process is essential every single time, regardless of the scope of the modification. Data mining can also be used to analyze enormous amounts of information and datasets, which can help business organizations to solve various managerial problems, manage risks, and find opportunities [4]. That doesnt mean eating ice cream makes people want to murder (In my experience, ice cream makes people happy). In this article, you'll learn the basics of simple linear regression, sometimes called 'ordinary least squares' or OLS regression a tool commonly used in forecasting and financial analysis. Regression analysis is a statistical method to analyze financial data, commonly using the least square regression technique. And this is his advice to managers: Use the data to guide more experiments, not to make conclusions about cause and effect. means that one variable caused the other to occur. The M-estimator replaces the squared residuals by a function of the residuals \(r_{i}^{2}\). Which can we ignore? However, a robust regression method can detect outliers even in complex data and give efficient results. A lot of people skip this step, and I think its because theyre lazy. https://doi.org/10.1108/JEFAS-07-2017-0084, Naifar N (2010) The determinants of bank performance: an analysis of theory and practice in the case of an emerging market. Regression analysis is driven by data. and Technology. Want to cite, share, or modify this book? J Econ, Finance Admin Sci. Dr Asif Pervez: main author; Dr Irfan Ali: Corresponding Author. Mobile banking is governed by a set of strict regulations aimed to ensure standardization and customer protection. Business intelligence in banking is a complex and robust pipeline that incorporates a variety of tools. Accelerate your career with Harvard ManageMentor. It can be seen from Table 2 that the average value of Operating expenses to total assets (OPETA), a measure of inefficiency used in the study, is 2.19%. J Money Laund Control. What Is Regression Analysis? (Plus Steps and Types) - Indeed However, there is a tradeoff between the logistic regression model and machine learning-based techniques regarding interpretability and model performance because machine . Liquidity (LATA) had an inverse, and significant relationship with bank performance across all the seven models except models 3 and 6, which indicates thats banks have more return on equity for shareholders by lending more and maintaining lower liquid assets. 686, Cpraru B, Ihnatov I (2015) Determinants of banks profitability in EU15. Abstract. In other words, dont ask your analysts to look at every variable they can possibly get their hands on all at once. Full article: Using a panel data approach to determining the key How. And, he says, never forget to look beyond the numbers to whats happening outside your office: You need to pair any analysis with real-world study. Banks should also change their business model to comply with Basel III requirements cost-efficiently. As Redman points out, If the regression explains 90% of the relationship, thats great. Beyond the expectations, financial crises were positively related to ROE. Money Macro and Finance Res Group Conf 45:127, Sarpong-Kumankoma E, Abor J, Aboagye AQQ, Amidu M (2018) Freedom, competition and bank profitability in Sub-Saharan Africa. Everything You Need to Know about Fair Lending Regression Analysis Our mission is to improve educational access and learning for everyone. = 1 indicates a perfect positive correlation. Yohai [14] proposed a class of robust estimates for the linear model called MM-estimates, one of the most commonly employed robust regression techniques. This is the precise reason why regression testing has unique characteristics and importance in the field of mobile banking. Let's say you are analyzing your call center. If you are serious about growing the popularity of your mobile banking app and increasing the number of users, thorough testing during every single step of the way will be required. Regression testing is done after the launch of an app enhancement for the purpose of identifying bugs and glitches. It refers to the fact that regression isnt perfectly precise. The deviation in financial data because of changes in financial policies and commercial cycles gives rise to outliers in the data. Some of them have iOS devices, some rely on Android smartphones, some will be accessing the app via a tablet or a hybrid device. And, perhaps most important, how certain are we about all these factors? These are two very important processes, and both of them should be taking place. 1. Product Insight, A general equation for the regression model is: where Y is the dependent variable, is the vector of true residuals n1, and X is np in the design matrix. The goal is not to figure out what is going on in the data but to figure out is what is going on in the world. .,r_{n} \left( \beta \right)} \right),$$, \(r_{1} \left( \beta \right) = y_{i} - x_{i}^{T} \beta\), $$\frac{1}{n} \mathop \sum \limits_{i = 1}^{n} \rho_{0} \left( {\frac{{r_{i} \left( {\hat{\beta }} \right)}}{{\hat{\sigma }}}} \right) = 0.5$$, $$\mathop \sum \limits_{i = 1}^{n} a_{n} \left( {R_{i} } \right)r_{i} = min$$, $$\mathop \sum \limits_{i = 1}^{n} a_{n} \left( i \right) = 0$$, $$r_{i} = \frac{{y_{i} - x_{i}^{T} \widehat{{\beta_{0} }}}}{{\hat{\sigma }}}$$, $$y = X\beta + \gamma + \varepsilon , \varepsilon \sim N(0, \sigma^{2} I$$, \({\text{y}} = \left( {{\text{y}}_{{1}} , \ldots ,{\text{ y}}_{{\text{n}}} } \right)^{{\text{T}}} ,{\text{ x}} = \left( {{\text{x}}_{{1}} , \ldots \,,{\text{ x}}_{{\text{n}}} } \right)^{{\text{T}}} = \gamma (\gamma_{{1}} , \ldots \,,\gamma_{{\text{n}}} )^{{\text{T}}}\), $${\mathbf{ROE}}_{{{\mathbf{it}}}} = {{\varvec{\upbeta}}}_{{\mathbf{1}}} + \, {{\varvec{\upbeta}}}_{{\mathbf{2}}} {\mathbf{LNA}}_{{{\mathbf{it}}}} + \, {{\varvec{\upbeta}}}_{{\mathbf{3}}} {\mathbf{NNPANA}}_{{{\mathbf{it}}}} + \, {{\varvec{\upbeta}}}_{{\mathbf{4}}} {\mathbf{CAR}}_{{{\mathbf{it}}}} + \, {{\varvec{\upbeta}}}_{{\mathbf{5}}} {\mathbf{LATA}}_{{{\mathbf{it}}}} + \, {{\varvec{\upbeta}}}_{{\mathbf{6}}} {\mathbf{OPEXTA}}_{{{\mathbf{it}}}} + \, {{\varvec{\upbeta}}}_{{\mathbf{7}}} {\mathbf{PPE}}_{{{\mathbf{it}}}} + \, {{\varvec{\upbeta}}}_{{\mathbf{8}}} {\mathbf{NONIITI}}_{{{\mathbf{it}}}} + \, {{\varvec{\upbeta}}}_{{\mathbf{9}}} {\mathbf{GDP}}_{{{\mathbf{it}}}} + \, {{\varvec{\upbeta}}}_{{{\mathbf{10}}}} {\mathbf{CPI}}_{{{\mathbf{it}}}} + \, {{\varvec{\upbeta}}}_{{{\mathbf{11}}}} {\mathbf{B2}}_{{{\mathbf{it}}}} + \, {{\varvec{\upbeta}}}_{{{\mathbf{12}}}} {\mathbf{B3}}_{{{\mathbf{kit}}}} + \, {{\varvec{\upvarepsilon}}}_{{{\mathbf{it}}}}$$, https://doi.org/10.1007/s40745-022-00427-3, https://doi.org/10.1108/JFRA-05-2015-0060, https://doi.org/10.1108/JMLC-10-2015-0044, https://doi.org/10.1108/CFRI-10-2016-0118, https://doi.org/10.1108/JEFAS-07-2017-0084, https://doi.org/10.1108/01443581211274610, https://doi.org/10.1108/JFRC-12-2017-0107. Some lenders and collection agencies try to keep a debt around longer with an illegal practice known as re-aging collection accounts. https://doi.org/10.1108/CFRI-10-2016-0118, Majumder MTH, Li X (2018) Bank risk and performance in an emerging market setting: the case of Bangladesh. PubMedGoogle Scholar. Tables 4 give the empirical findings of the study indicating that the impact of bank size (LNA) and Bank risk (NNPANA) on the bank performance (ROE) of public sector banks in India were the same except for models 4 and 7. A regression line always has an error term because, in real life, independent variables are never perfect predictors of the dependent variables. Robust Regression Analysis in Analyzing Financial Performance of Public Sector Banks: A Case Study of India. This is critical. Compliance can be a challenge. Unless youre selling umbrellas, it might be difficult to prove that there is cause and effect. Research Report No. 1. Twenty-one public sector banks that were operating in India from 2005 to 2018 and whose data were available for all the selected parameters were selected for the present study. What actions will you take? Thus, once a shortcoming is identified and fixed, the process does not end there. Regression Analysis - Formulas, Explanation, Examples and Definitions May 20, 2022 OpenStax. A must-read for banking industry professionals who care about creating value and building resilient institutions. In almost all the models, Labour productivity (PPE) and income diversification (NONIITI) had a positive and significant relationship with bank performance, implying that higher labour productivity and diversified income lead to higher profit for the banks. If a prohibited basis factor had any bearing on the credit decision or the price of the loan, regression analysis should flag it. It can resist the effects of outliers even when they comprise nearly half of the data. It can be concluded from the findings that bank size had no significance on the public sector banks performance during the study period. Using the Excel command =CORREL, the correlation coefficient is calculated to be 0.947. The present study is descriptive as well as analytical in nature. The study analyzes the performance of Public sector banks under a set of variables, including bank-specific, bank regulation, macroeconomic variables and financial events. And then you have your independent variables the factors you suspect have an impact on your dependent variable. \le r_{q} (\beta )^{2}\), \({\text{q }} = \, \left[ {{\text{n}}\left( {{1} - \alpha } \right) \, + { 1}} \right],\), $$\widehat{\beta } = \arg \mathop {\min }\limits_{\beta } \widehat{\sigma }\left( {r_{1} \left( \beta \right), . Theor Econ Lett 8(14):3046, Bouzgarrou H, Jouida S, Louhichi W (2018) Bank profitability during and before the financial crisis: domestic versus foreign banks. Typically, the independent variable (s) changes with the dependent variable (s) and the regression analysis attempts to . A regression test isnt just about managing a massive security or data privacy concern. It is followed by the computation of an M-estimate of the scale of the errors with the help of residuals based on the initial estimate in the second stage. Ann. Blatna D (2015) Application of Robust Regression Methods in An Analysis of The European Countries Share Of Renewable Energy In Gross Final Energy Consumption, 18th international scientific conference on application of mathematics and statistics in economics, 26 septembet, 2015. Credit Unions, Data science is the study of analysing these large volumes of data by employing various modern methods of data analyses like complex machine learning algorithms to get meaningful information which helps in efficient and timely decision making. . Here, the subscript i indicates cross-sectional dimension across the selected banks, t is for years, and indicates random error term. You might be tempted to say that rain has a big impact on sales if for every inch you get five more sales, but whether this variable is worth your attention will depend on the error term. By using regress ROE is positively associated with PPE, CAR, NNPANA, CPI, LNA, and NONIITI while negatively associated with GDP, LATA, and OPEXTA. Banks are suggested to develop information technology and human resource skills and should adopt an advanced database management system to implement Basel III. The assumption of normality is also essential for most statistical data analysis methods. Pervez, A., Ali, I. A Refresher on Regression Analysis - Harvard Business Review Robust regression offers a better and more realistic analysis owing to reducing the impact of outliers and influential data, and it is recommended for more precise results. The robust alternatives, such as Least Median Squares estimators (LMS), Least Trimmed Squares estimators (LTS), and M estimators, can tolerate up to a certain level of contaminations [6]. These are potential signs of a lending compliance issuebut how can you know for sure? LNA is the log of total assets (bank size), NNPANA is for credit risk, while CAR is the capital adequacy ratio (Bank capital). The regression analysis has significance for all the fields of study, and almost all the fields apply least square regression methods for data analysis. PDF Fair lending monitoring: Where to focus statistical analysis Regression analysis is a set of statistical methods used for the estimation of relationships between a dependent variable and one or more independent variables. Repeated median estimates are unbiased and Fisher consistent for bivariate linear regression with symmetric errors. Developers should work to mitigate such security concerns, and regression testing of updates is an essential process. People who arent experienced with mobile banking could find the process to be confusing and even overwhelming. Textbook content produced by OpenStax is licensed under a Creative Commons Attribution License . Also keep in mind whether you can do anything about the independent variable youre considering. Its your job to identify the factors that you suspect are having an impact and ask your analyst to look at those. Banking apps enable financial transactions. It also lets you know how certain the results are using probability. Profit per employee (PPE), a measure of productivity, has an average value of 0.57. Assume the DJIA has reached a value of 32,000. These estimates are efficient when the errors have a normal distribution and a breakdown point of 0.5. A general framework for the inclusion of case-specific parameters in regularization problems, describing the impact on the effective loss for a variety of regression and classification problems is as: where, \({\text{y}} = \left( {{\text{y}}_{{1}} , \ldots ,{\text{ y}}_{{\text{n}}} } \right)^{{\text{T}}} ,{\text{ x}} = \left( {{\text{x}}_{{1}} , \ldots \,,{\text{ x}}_{{\text{n}}} } \right)^{{\text{T}}} = \gamma (\gamma_{{1}} , \ldots \,,\gamma_{{\text{n}}} )^{{\text{T}}}\), and the mean shift parameter i is non-zero when the ith observation is an outlier and zero. However, the necessary assumption in regression analysis that as normality, homoscedasticity, multicollinearity etc., presence of outliers and influential observations makes the least square regression analysis vulnerable to such data and may give misleading and wrong results. In: Franke J, Hardle W, Martin RD (eds) Lectures Notes in Statistics, vol 26. Asian Acad Manag J Account Fin4(2):91112, Abdullah MN, Parvez K, Ayreen S (2014) Bank specific, industry specific and macroeconomic determinants of commercial bank profitability: a case of Bangladesh. Mortgage Lenders, OPEXTA is management efficiency, PPE is Productivity, while NONIITI is for income diversification. Understanding one of the most important types of data analysis. There are some other robust procedures, LTS estimates by Rousseeuw [12]. The launch of a new phone could lead to serious issues for mobile app developers. Rev Pac Basin Financial Mark Policies 21(01):1850007, Yksel S, Mukhtarov S, Mammadov E, zsar M (2018) Determinants of profitability in the banking sector: an analysis of post-soviet countries. Regulators must root out bias in AI-based lending. It enables the identification and characterization of relationships among multiple factors. And one of the most effective ways to determine data value and monitor trends (and the relationships between them) is to use regression analysis, a set of statistical methods used for the estimation of relationships between independent and dependent variables. The formula for a simple linear regression is: y is the predicted value of the dependent variable ( y) for any given value of the independent variable ( x ). Most companies use regression analysis to explain a phenomenon they want to understand (for example, Why did customer service calls drop last month? Ann Math Stat 36:17531758, Siegel AF (1982) Robust regression using repeated medians. \right)\)a robust loss is function and \(\hat{\sigma }\) is an error scale estimate. Rousseeuw, P. For some of the companies included in the survey, the cost of downtime reached 300,000 dollars per hour. Regression Analysis: Step by Step Articles, Videos, Simple Definitions Math Probl Eng 2021:9. If you are redistributing all or part of this book in a print format, Pearsons correlation coefficients between the study variables in Table 3 indicate the degree of association between the variables. Regression testing is needed to make sure that the safety of personal and financial data is maintained. 192, Center for Statistics and Operations research, VUB Brussels. A.It saves time B. Int J Account Res 42(3968):110, Ali MA, Pervez A, Bansal R, Khan MA (2022) Analyzing performance of banks in India: robust regression analysis approach. For instance, it can be used to see how rainfall correlates with crop growth or GDP correlates with employment rates. x is the independent variable ( the . Rather the line is an estimate based on the available data. Regression analysis is the mathematically measured correlation of a link between two variables: the independent variable X and the dependent variable Y. Regression analysis evaluates how strongly related the two elements are to help you make stronger business plans, decisions and forecasts. In a three-stage procedure, an initial regression estimate is computed in the first stage, not necessarily efficient, but consistent robust with a high breakdown point. Ordinary least-squares (OLS) estimators for a linear model are very sensitive to unusual values in the design space [9]. It finds variables that correlate (i.e. First, dont tell your data analysts to figure out what is affecting sales. Step 3: Click a variable name for the dependent value in the left-hand window. We help you break down the silos, allowing your organization to collaborate for seamless, comprehensive risk management and compliance on the enterprise level. While regression testing can be complex and resource-consuming on occasions, the implications of not going through the process can be severe. The robust regression is a good substitution for the least square regression for these data. So, in this case, lets say you find out the average monthly rainfall for the past three years as well. Bugs caused by the absence of regression testing can lead to the loss of loyal customers and long-term financial implications for the business. Were here to help. Regression analysis is used extensively in finance-related applications. The average value of Non-Interest Income to Total Income (NIITI), a measure of business diversification, is 11.28%. The Least Median Square estimates by Siegel [11] are found by minimizing the median of the squared residuals. Ann Data Sci 7:613628, Article Ncontracts and QuestSoft Lending Compliance customers can find their respective support resources and contacts here! 1999-2023, Rice University. All the data doesnt need to be correct or perfect, explains Redman, but consider what you will be doing with the analysis. The aim of the regression test is to make sure that the modification hasnt led to bugs or functionality problems. Regression analysis models the relationships between a response variable and one or more predictor variables. From security to database and functionality testing, the process should cover it all. This value of the correlation coefficient is significant using the test for significance referenced earlier in, Using the Excel commands =SLOPE and =INTERCEPT, the value of the slope and. The legal responsibility of this statement shall be borne by us. OpenStax is part of Rice University, which is a 501(c)(3) nonprofit. Updated June 24, 2022 Professionals across various fields use programs like Excel to run calculations that provide them with helpful insights regarding profits, trends and any other factors that affect the success of their projects. However, Cost inefficiency had positively impacted Net Interest Margin during the study period. The robust regression offers an efficient and more realistic analysis of financial data by eliminating or reducing the impact of outliers and influential observations. This book uses the A stocks beta is a measure of the volatility of the stock compared to a benchmark such as the S&P 500 index. = 0 indicates no correlation. McGraw-Hill/Irwin, New York, Kumar BS, Ravi V, Miglani R (2021) Predicting Indian stock market using the psycho-linguistic features of financial news. This line will help you answer, with some degree of certainty, how much you typically sell when it rains a certain amount. According to many statisticians, robust estimation is required to analyze the data with outliers. Econ Chang Restruct 49(1):7193, Bougatef K (2017) Determinants of bank profitability in Tunisia: does corruption matter? The authors declare that they have no conflict of interest. Correspondence to Simple Linear Regression | An Easy Introduction & Examples - Scribbr Better management builds a better bank. Based on these results, the corresponding linear regression model is. The present study is mainly based on secondary data on selected parameters of the public sector banks operating in India. They make use of personal data that has to be protected in the best possible way. Origination or fallout rates might concern you. Lending Compliance, Lending Compliance Management. This is dangerous because theyre making the relationship between something more certain than it is. This is called the regression line, and its drawn (using a statistics program like SPSS or STATA or even Excel) to show the line that best fits the data. Join Ncontracts for a one-hour webinar, Reducing Compliance Risk with Regression Analysis, on March 18, 2020 at 2 p.m. CST. Procedia Soc Behav Sci 44(2012):406416, Sufian F, Chong RR (2008) Determinants of bank profitability in a developing economy: empirical evidence from Philippines. But if it explains 10%, and you act like its 90%, thats not good. The point of the analysis is to quantify the certainty that something will happen. The mean value of the Capital Adequacy Ratio (CAR) during the study period was 12.21%. The tendency is for further growth in the years to come mobile banking transactions are set to double in the years until 2022. The regression shows that they are indeed related. A fair lending analysis of underwriting should include a specific analysis of overrides to automated system decisions and exceptions to guidelines, if applicable. As a consumer of regression analysis, you need to keep several things in mind. 14.4 Regression Applications in Finance - OpenStax Could Your FI Benefit from Regression Analysis? J Chin Econ Bus Stud 10(3):267273, Sufian F (2012) Determinants of bank profitability in developing economies: empirical evidence from the South Asian banking sectors. The scatter plot that graphs S&P monthly return versus Nike monthly return is shown in Figure 14.8. The scatter plot clearly shows a linear pattern; the next step is to calculate the correlation coefficient and determine if the correlation is significant. Consumers have a lot of power because they can easily switch between service providers. and you must attribute OpenStax. Because the banking sector changes rapidly, an app will need to be functional and maintained professionally in order to attract a big number of users. A single hour of app downtime could cost a company $100,000, a survey by ITIC suggests. You must pound the pavement, he says. Res Int Bus Finance 44:2639, Sufian F, Habibullah MS (2009) Determinants of bank profitability in a developing economy: empirical evidence from Bangladesh. Get in touch and let us see how we can work together. When running regression analysis, be it a simple linear or multiple regression, it's really important to check that the assumptions your chosen method requires have been met. ); or to decide what to do (for example, Should we go with this promotion or a different one?).
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