Analysis of Factors That Can Contribute to NBA Player Salary Rate to analyze and determine the factors that influence the salary rate of National Basketball Association (NBA) players. It is of great importance, in light of financial constraints, to determine whether it is their skills, scores, wins, losses and even fouls that can significantly contribute to determining a player's salary. the NBA. The purpose of this investigative report is to identify the variables most likely to contribute to NBA player salaries. There is a serious deficiency of empirical literature and evidence regarding the topic of determinants of NBA player salary, as the battle of these factors has been mostly forcefully blamed on discrimination, performance, etc. And in the past, many articles have been written about NBA player salary discrimination, the lack of empirical evidence related to player performance and its impact on their salary. Say no to plagiarism. Get a tailor-made essay on “Why Violent Video Games Should Not Be Banned”? Get an original essay Therefore, in this report, we analyze and try to describe those factors that can contribute to the salary rate of NBA players using the SAS data we studied. data which consists of a sample of 335 NBA players observed with 23 variables associated with different statistics based on their games played, scoring points, player totals, player turnovers, etc. Throughout the description of the report, we observed on the data and we noticed that to perform an analysis on the sample of players, we need to make the salary of NBA players (in millions) the target variable to conclude more on our research question which is based on identifying whether the NBA player's physical abilities influence his salary, but the gist of this is described in more detail in the descriptive analysis. Most of the results and results of the PROC procedure that we used to analyze the data will be discussed in the Results and Discussion section, which gives us an overview of what the dataset is like and how it we place according to our hypothesis. Powerful software designed to provide researchers with a wide variety of data management and analysis capabilities. SAS has two main concepts: how to import data into SAS and how to perform analysis. The steps we'll apply to our data are: enter the SAS data, tell SAS what analysis to do, and then see the result. Then, from our data output, we will conclude which variables affect our data. The method we're going to use to read our data into SAS is the free point form because it's simple and has very few specifications. The analysis we will use to analyze our data is multiple regression which is an extension of linear regression. It is used when we want to predict the value of a variable based on the value of two or more variables. The dependent variable is Salary. The procedure we will use is PROC REG, and the independent variables are age, time, losses and fouls. Further analysis based on these concepts is conceptualized in the following subheadings giving deeper insights and research into the data. Descriptive analysis Based onthe data sample provided to us on all 335 NBA players during the season between 2016 and 2017, the hypothesis is that the more a player has the more GP (Game Player Total) and less than L (Player Losses), then this player will have a higher salary rate. The objective is to check whether there is sufficient evidence to accept or reject the hypothesis based on the sample data. The SAS data provided to us consists of a sample of 335 NBA players observed with 23 variables associated with different statistics based on their games played, goal points, player totals, player losses, etc. As we observed from the data, we noticed that to perform analysis on the sample of players, we need to make NBA player salary (in millions) the target variable to be able to draw deeper conclusions about our research question which consists of identifying whether the physical abilities of NBA players influence their salary. To perform the data analysis, we used the SAS system, a powerful software program designed to provide researchers like us with a wide variety of data management and analysis capabilities. So, in analyzing the dataset, we make the NBA players' salary (in millions) the independent variable and the other 22 player statistics will be the dependent variables. Since we have a variety of SAS (PROC, for procedures) analyzes to perform, in this case we will use a few of them to analyze the dataset by manipulating the data to prepare it for our analysis. But since this is a large data set, we can subset most of the variables, because some will not be very useful to the research we are conducting and we will use a lot of SAS statements to do this. The initial analysis we observed on the dataset was that looking at each player's GP (Game Player Total) with minutes played, the more a player plays in a game or the more a player has minutes, the GP was also larger, so based on this we first made the assumption that this was based on the epoch, but with the results of the analysis we did not have enough evidence to conclude that it was true because most of the actors with the first theory we created about the wage rate did not support the theory. Additionally, changing the theory, we used explanatory variables such as height, weight, games played, minutes, etc. to take a different approach to understanding salaries. Therefore, by using these variables, at least a broader realistic view was obtained, more effective in making an inference about the data. Most of the results and results of the PROC procedure that we used to analyze the data will be discussed in the next session which is Results and Discussion. In this section it will mainly be a breakdown of the results we observed and an analysis of them by interpreting the results. The guide to this initial analysis is to give the reader an overview of the SAS data and analysis procedures we used to reach conclusions about the NBA player's game statistics, physical and athletic abilities, etc. and indicate whether they influence his salary rate based on it. on the given static sample. But through all the above, the other steps we will follow to get more conclusive evidence on the research question is to use different variables by the subset method and observe the output results we will observe. Methodology The methodology that we are going to follow is based on the DataSAS provided to us consists of a sample of 335 NBA players observed with 23 associated variables. We will do data analysis using SAS essentials software to build a multiple regression model, predicting NBA player salary (in millions) as the target variable. Following this methodology will bring us to a point where we examine it and decide whether or not we reject the hypothesis that was predicted. This is the most appropriate methodology to effectively answer the research question because we will have a visual view using a powerful tool to conclude on the hypothesis. As in this research, we mainly work with NBA players (basketball players) for the season which is between the years 2016 and 2017. Our interest is to find the main determinant of an NBA player in during the season mentioned above. In this review, we want to look at 335 NBA players and 23 possible determinants. To perform the analysis of this data, we will use one of the most popular statistical programming languages called SAS. We will kindly use several linear regression model which is represented mathematically as where the error is assumed to be zero and our estimation equation will be the same as the linear regression model equation mentioned above, accept that the estimation does not involve no error since the error is zero. We assume that our data are normally distributed with a noted mean and variance. Using this model we are now allowed to look at more than one determinant at the same time and this model is useful in giving us the mean and variance of each determinant (independent variable). This might even allow us to identify some outliers in the grid (plane). In our predictions, we will use the SAS built-in function called PROG REG for multiple linear regression. To test this, we will use a hypothesis test. Our hypothesis from this statistical model if it turns out that the determinant of players' salary amounts is insignificant in predicting players' salaries. For this test if it happens that the p-value is less than 0.05 then we will not reject our null hypothesis in favor of our alternative otherwise we will reject our null hypothesis. There are many other models we can use, such as hierarchical clustering. This model deals with unknown patterns. Hierarchical grouping is used to place similar variables in one group and not assign any identical variables to a different group. The algorithm of this model cannot, however, replace what has been done previously. It is also difficult to identify the correct number of clusters per dendrogram. Results and Discussion Basically, the goal of this study was to identify the variables most likely to contribute to NBA player salaries. The existing literature on strategic behavior in the context of the National Basketball Association has shown inconsistent results using different empirical variables. Therefore, the initial analysis above indicates that the result of the time-based analysis did not provide us with enough evidence to support the time theory. we had to use other explanatory variables shown in the analysis above. Our results could potentially benefit NBA teams in their player evaluation process, primarily for determining a player's salary, as it allows them to more accurately determine the effects of the contract cycle on player performance. As an illustration, when trying to decide whether to sign a new contract with a player who is.

Essay / Analysis of Factors That Can Contribute to NBA Player Salary Rate to analyze and determine the factors that influence the salary rate of National Basketball Association (NBA) players. It is of great importance, in light of financial constraints, to determine whether it is their skills, scores, wins, losses and even fouls that can significantly contribute to determining a player's salary. the NBA. The purpose of this investigative report is to identify the variables most likely to contribute to NBA player salaries. There is a serious deficiency of empirical literature and evidence regarding the topic of determinants of NBA player salary, as the battle of these factors has been mostly forcefully blamed on discrimination, performance, etc. And in the past, many articles have been written about NBA player salary discrimination, the lack of empirical evidence related to player performance and its impact on their salary. Say no to plagiarism. Get a tailor-made essay on “Why Violent Video Games Should Not Be Banned”? Get an original essay Therefore, in this report, we analyze and try to describe those factors that can contribute to the salary rate of NBA players using the SAS data we studied. data which consists of a sample of 335 NBA players observed with 23 variables associated with different statistics based on their games played, scoring points, player totals, player turnovers, etc. Throughout the description of the report, we observed on the data and we noticed that to perform an analysis on the sample of players, we need to make the salary of NBA players (in millions) the target variable to conclude more on our research question which is based on identifying whether the NBA player's physical abilities influence his salary, but the gist of this is described in more detail in the descriptive analysis. Most of the results and results of the PROC procedure that we used to analyze the data will be discussed in the Results and Discussion section, which gives us an overview of what the dataset is like and how it we place according to our hypothesis. Powerful software designed to provide researchers with a wide variety of data management and analysis capabilities. SAS has two main concepts: how to import data into SAS and how to perform analysis. The steps we'll apply to our data are: enter the SAS data, tell SAS what analysis to do, and then see the result. Then, from our data output, we will conclude which variables affect our data. The method we're going to use to read our data into SAS is the free point form because it's simple and has very few specifications. The analysis we will use to analyze our data is multiple regression which is an extension of linear regression. It is used when we want to predict the value of a variable based on the value of two or more variables. The dependent variable is Salary. The procedure we will use is PROC REG, and the independent variables are age, time, losses and fouls. Further analysis based on these concepts is conceptualized in the following subheadings giving deeper insights and research into the data. Descriptive analysis Based onthe data sample provided to us on all 335 NBA players during the season between 2016 and 2017, the hypothesis is that the more a player has the more GP (Game Player Total) and less than L (Player Losses), then this player will have a higher salary rate. The objective is to check whether there is sufficient evidence to accept or reject the hypothesis based on the sample data. The SAS data provided to us consists of a sample of 335 NBA players observed with 23 variables associated with different statistics based on their games played, goal points, player totals, player losses, etc. As we observed from the data, we noticed that to perform analysis on the sample of players, we need to make NBA player salary (in millions) the target variable to be able to draw deeper conclusions about our research question which consists of identifying whether the physical abilities of NBA players influence their salary. To perform the data analysis, we used the SAS system, a powerful software program designed to provide researchers like us with a wide variety of data management and analysis capabilities. So, in analyzing the dataset, we make the NBA players' salary (in millions) the independent variable and the other 22 player statistics will be the dependent variables. Since we have a variety of SAS (PROC, for procedures) analyzes to perform, in this case we will use a few of them to analyze the dataset by manipulating the data to prepare it for our analysis. But since this is a large data set, we can subset most of the variables, because some will not be very useful to the research we are conducting and we will use a lot of SAS statements to do this. The initial analysis we observed on the dataset was that looking at each player's GP (Game Player Total) with minutes played, the more a player plays in a game or the more a player has minutes, the GP was also larger, so based on this we first made the assumption that this was based on the epoch, but with the results of the analysis we did not have enough evidence to conclude that it was true because most of the actors with the first theory we created about the wage rate did not support the theory. Additionally, changing the theory, we used explanatory variables such as height, weight, games played, minutes, etc. to take a different approach to understanding salaries. Therefore, by using these variables, at least a broader realistic view was obtained, more effective in making an inference about the data. Most of the results and results of the PROC procedure that we used to analyze the data will be discussed in the next session which is Results and Discussion. In this section it will mainly be a breakdown of the results we observed and an analysis of them by interpreting the results. The guide to this initial analysis is to give the reader an overview of the SAS data and analysis procedures we used to reach conclusions about the NBA player's game statistics, physical and athletic abilities, etc. and indicate whether they influence his salary rate based on it. on the given static sample. But through all the above, the other steps we will follow to get more conclusive evidence on the research question is to use different variables by the subset method and observe the output results we will observe. Methodology The methodology that we are going to follow is based on the DataSAS provided to us consists of a sample of 335 NBA players observed with 23 associated variables. We will do data analysis using SAS essentials software to build a multiple regression model, predicting NBA player salary (in millions) as the target variable. Following this methodology will bring us to a point where we examine it and decide whether or not we reject the hypothesis that was predicted. This is the most appropriate methodology to effectively answer the research question because we will have a visual view using a powerful tool to conclude on the hypothesis. As in this research, we mainly work with NBA players (basketball players) for the season which is between the years 2016 and 2017. Our interest is to find the main determinant of an NBA player in during the season mentioned above. In this review, we want to look at 335 NBA players and 23 possible determinants. To perform the analysis of this data, we will use one of the most popular statistical programming languages called SAS. We will kindly use several linear regression model which is represented mathematically as where the error is assumed to be zero and our estimation equation will be the same as the linear regression model equation mentioned above, accept that the estimation does not involve no error since the error is zero. We assume that our data are normally distributed with a noted mean and variance. Using this model we are now allowed to look at more than one determinant at the same time and this model is useful in giving us the mean and variance of each determinant (independent variable). This might even allow us to identify some outliers in the grid (plane). In our predictions, we will use the SAS built-in function called PROG REG for multiple linear regression. To test this, we will use a hypothesis test. Our hypothesis from this statistical model if it turns out that the determinant of players' salary amounts is insignificant in predicting players' salaries. For this test if it happens that the p-value is less than 0.05 then we will not reject our null hypothesis in favor of our alternative otherwise we will reject our null hypothesis. There are many other models we can use, such as hierarchical clustering. This model deals with unknown patterns. Hierarchical grouping is used to place similar variables in one group and not assign any identical variables to a different group. The algorithm of this model cannot, however, replace what has been done previously. It is also difficult to identify the correct number of clusters per dendrogram. Results and Discussion Basically, the goal of this study was to identify the variables most likely to contribute to NBA player salaries. The existing literature on strategic behavior in the context of the National Basketball Association has shown inconsistent results using different empirical variables. Therefore, the initial analysis above indicates that the result of the time-based analysis did not provide us with enough evidence to support the time theory. we had to use other explanatory variables shown in the analysis above. Our results could potentially benefit NBA teams in their player evaluation process, primarily for determining a player's salary, as it allows them to more accurately determine the effects of the contract cycle on player performance. As an illustration, when trying to decide whether to sign a new contract with a player who is.

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