its below my answers are wrong in the excel which makes my analysis wrong the teachers notes are...

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its below my answers are wrong in the excel which makes my analysis wrong the teachers notes are there for the things that are wrongFinal Exam                                       Mathematics and Statistics for Social Sciences Author:                                                      Vanda Salari   LL.B 2019/2020 year student   Student Number: B019080 Introduction The definition of success in the world of sports can be interpreted in a number of ways.  Success in basketball can be defined using the value of the player. The National  Basketball Association (NBA) uses different factors to determine its most successful  player. It is important to look into characteristics which could be attributed to a player’s  success.  From College to the NBA, Evans examines the characteristics the NBA considers to  determine a player’s success. The study gathered data from 2006 to 2013 draftees.  Evans looked into how the duration of the player’s career prior to being drafted, as well  as their qualification. The variables included statistics from the players’ games. Other variables which were considered were their age, height, and BMI. The result of the  research showed that height and in game statistics were correlated to success.  Meanwhile, number of fouls, age, and late start were correlated to higher draft positions  written by NBAstuffer themself to help their fans and was last updated on june 14, 2019   Moxley and Towne also focus on the early career success of NBA players. They studied  multiple factors and found the top variable to be the players’ performance history. In  addition, height, arm span, vertical leap, and agility are also taken into consideration.  The results of the study showed that although physical attributes are taken into account,  they are not distinguishing factors to the player’s success. The factors which were found  to be useful were age, win shares, and college quality.  Measuring Greatness in the NBA looks into the question of how greatness and success  is measured in the NBA. Whitmoyer believes that a single factor is insufficient when  determining the success of the player, noting that other factors should also be taken into  account. The player’s compensation is often used as a reflection of their value,  however, it is not always reliable. The perception of the player is also key to a player’s  value.  The articles took into account qualitative and quantitative factors to justify their thesis.  All three journals look into qualitative and quantitative profiles to find what determines  success. They found physical attributes to not be as important of a factor as the  performance and other ratings. Age was also found to be rather important, as well as  the history of the player. How long they have been playing professionally is also a big  determining factor. Evans takes into account more in game statistical data as compared  to the other two, especially Whitmoyer. Whitmoyer looks into the measurement of  success more than the actual statistics. Physical attributes aren’t much mentioned in the  3rd journal, but are taken into account in Evans and Moxley’s findings. The articles have  used more specific and thorough data to explore the topic.  There are a number of factors which need to be considered to find what indicates  success. Different studies have been conducted to find which factors are most  correlated to NBA Career success. Although the data acquired has been useful in  finding possible factors to success, it is important to gather more information to narrow  down the information.  Research Questions What is the minimum wage for an NBA player?  How many NBA players out of 202 have an average yearly rating?  During which are the most successful and earn most? Methodology A. Study of NBA players and their values The Data used for the research is taken for the NBA PlayOffs between the year of 2018- 2019 from the official NBA official webpage; https://www.nbastuffer.com/2018-2019-nba- player-stats/. The data summarized the NBA players yearly value and wage according to  their performance throughout the year which affect their yearly ratings data Analysis.  Due to each NBA player not having the same exact characteristics the variables were  researched separately such as each NBA player's net worth and income depending on  their yearly ratings which is calculated at the end of each playoffs. B. Data Analysis The results are achieved and will be analyzed in the Microsoft Excel system. It can be used to  test outcomes, to verify hypotheses, degrees of trust and association. Three theories will be  tested out. Hypothesi: 1 Ho: p=>0.77  Ha: p≠ 0.77 It is a one-sided theory test of 0.06 alpha and 90 per cent of the Confidence level. Before the  hypothesis is tested, the data must be modeled and the modeling parameters and assumptions  followed. Independence: The values of this database are independent from each NBA player. Sample Size: The Sample is large enough due to 157/202 students being only american nationals. https://www.nbastuffer.com/2018-2019-nba-player-stats/ https://www.nbastuffer.com/2018-2019-nba-player-stats/ User Sticky Note Incorrect null hypothesis. User Sticky Note How can alpha be 0.06 when your confidence level is 90%? User Sticky Note These hypotheses don`t match the table below in the results section.   Randomization: There is an application of randomization to the applicant of the database. 10% Condition: The data includes more than half of the population of the American players therefore this  satisfies the 10% condition. The conditions are satisfied, so I will use the Usual Template to  consider a z-interval in one proportion. Hypothesis: 2 H0: μ = 45  HA: μ > 45 Hypothesis 2 is a one-sided hypothesis with an alpha of 0.06 and a confidence interval of 95 per  cent, with data on average NBA players ranked between 75-77 percent on an annual basis. To  run an independence, randomization and 10 per cent t-test.To carry out a t-test of equality,  randomization and 10 percent criteria must be met, and all of these conditions and assumptions  have been met, as stated in the Hypothesis 1 test. Hence we can carry on the work. Hypothesis: 3 H0: μ = 0.6  Ha: μ = 0.6 As mentioned before, all the assumptions and parameters for this data set are satisfied, and a  two-sided hypothesis test for the null hypothesis and alternate hypothesis was carried out as  follows: this data was analyzed with a confidence interval of 95% and an alpha of 0.06 Results Hypothesis 1 Sample proportion 0.777227723 Z-stat 0.31240578  User Sticky Note How can alpha be 0.06 when your confidence level is 95%? Why are you changing confidence levels during the paper? User Sticky Note Incorrect alternative hypothesis.   sample size n 202 P(Z

Biswajit · Accepted Answer

Data Analysis:
Hypothesis test 1: Z proportion test nationality
	 
	 
	proportion test
	 
	 
	 
	 
	 
	 
	 
	 
	H0
	proportion of American players is 0.5
	0.5
	Assumed proportion
	 
	Ha
	proportion of American players is more than 0.5
	 
	 
	 
	 
	 
	 
	 
	 
	sample size
	 
	202
	 
	 
	P
	proportion of American players in sample
	0.77
	Proportion as per data
	 
	level of significance
	 
	0.05
	 
	 
	 
	 
	 
	 
	 
	Z statistic
	9.118642058
	 
	 
	 
	p value
	0.0000
	 
	 
	 
	 
	 
	 
	 
	 
	As the p value is lower than level of significance (0.05), we reject the null hypothesis.so we say that there are substantially more no of American players in comparison to others
	 
	 
	 
	
	 
	 
	 
	
	 
	 
	 
	
	 
	 
	conclusion
	
	 
	 
	 
	 
	 
	 
	 
	 
	Assumptions
	np >10
	156
	yes
	 
	 
	np(1-p)>10
	36
	yes
	 
	 
	samples are independent
	 
	yes
	 
	 
	Data points more than 30 to use normality assumption as per central limit theorem 
	202
	yes
Hypothesis test 2: T test for difference of ratings between American & rest of others players
	F-Test Two-Sample for Variances
	 
	 
	 
	 
	 
	 
	American yearly ratings
	Rest of others yearly ratings
	Mean
	77.24516129
	78.36170213
	Variance
	35.77067449
	49.14893617
	Observations
	155
	47
	df
	154
	46
	F
	0.727801602
	 
	P(F .05
	t-Test: Two-Sample Assuming Equal Variances
	 
	 
	 
	 
	 
	 
	American yearly ratings
	Rest of others yearly ratings
	Mean
	77.24516129
	78.36170213
	Variance
	35.77067449
	49.14893617
	Observations
	155
	47
	Pooled Variance
	38.84767467
	 
	Hypothesized Mean Difference
	0
	 
	df
	200
	 
	t Stat
	-1.075799136
	 
	P(T .05
	 
	 
	 
	t-Test: Two-Sample Assuming Equal Variances
	 
	 
	 
	 
	 
	 
	American Age
	Others Age
	Mean
	26.86451613
	27.19148936
	Variance
	15.10490155
	17.81036078
	Observations
	155
	47
	Pooled Variance
	15.

Final Exam XXXXXXXXXXMathematics and Statistics for Social Sciences Author: XXXXXXXXXXVanda Salari LL.B 2019/2020 year student Student Number: B019080 Introduction The definition of success in the...

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