Question

The purpose of this study was to identify the performance variables
i.e. scoring, assists, and fouls that significantly contributed to
determine a NBA player’s salary. It was hypothesized that scoring
performance variables such as points per game; field goal, free throw,
and three point percentage would be significant contributors to player
salaries.

Data set link :- https://1drv.ms/x/s!AnCfB5fz9u5ZgpUTQyJKMkTVIVb0LQ

Task Contents:-

1. Title and name.

2. Abstract. A single sentence or 2-3 bullet points (50 words) summarising the problem and the most important finding(s).

3. Some background information and questions of interest. Key variables and relevant assumptions/hypotheses should be included.

4. Data Analysis. Present appropriate descriptives and charts. Provide your best regression model and interpret it. Briefly comment on the assumptions of the model (residual diagnostics).

5. Conclusions. Summarise the key findings and make recommendations.

Please make sure you cover:-

- Data collection methods, data reliability and trustworthiness of the source.  

- Estimation of two (competing/alternative) regression models (using a minimum of five predictors) and discussing results in detail.

- Include References .

Answer 1

Title:- Determinants of NBA Player Salaries

Abstract:-  

• The purpose of this study was to identify the performance variables i.e. scoring, assists, and fouls that significantly contributed to determine a NBA player’s salary.
• It was hypothesized that scoring performance variables such as points per game; field goal, free throw, and three point percentage would be significant contributors to player salaries.
• Multiple regression cn be used to analyze past salaries of 400 NBA players and their career performance variables. Results indicated that points per game, rebounds, and personal fouls contributed significantly to a player’s salary.

Some background information and questions of interest:-

Hypothesis:- scoring components would significantly determine the level of compensation for NBA players.

variables :-. scoring, assists, and fouls.

The independent variables for this study were players’ field goal percentage, three point percentage, free throw percentage, rebounds per game, assists per game, turnovers per game, steals per game, blocked shots per game, personal fouls per game, and points per game.

non-performance variables such as height and race were considered.

explanatory variables such as height, weight, career games played, team revenue and draft position.

Data Analysis.

Column1		Column2		Column3
Mean	7781809	Mean	8.1725	Mean	26.1725
Standard Error	382337	Standard Error	0.214293	Standard Error	0.214293
Median	4520810	Median	8	Median	26
Mode	1312611	Mode	5	Mode	23
Standard Deviation	7646741	Standard Deviation	4.285868	Standard Deviation	4.285868
Sample Variance	5.85E+13	Sample Variance	18.36867	Sample Variance	18.36867
Kurtosis	0.715295	Kurtosis	0.007075	Kurtosis	0.007075
Skewness	1.253769	Skewness	0.591855	Skewness	0.591855
Range	34582550	Range	21	Range	21
Minimum	100000	Minimum	1	Minimum	19
Maximum	34682550	Maximum	22	Maximum	40
Sum	3.11E+09	Sum	3269	Sum	10469
Count	400	Count	400	Count	400

How salary is dependent on points:-

regression analysis

SUMMARY OUTPUT
Regression Statistics
Multiple R	0.701021
R Square	0.49143
Adjusted R Square	0.490152
Standard Error	5460049
Observations	400
ANOVA
	df	SS	MS	F	Significance F
Regression	1	1.14654E+16	1.14654E+16	384.586949	2.08268E-60
Residual	398	1.18652E+16	2.98121E+13
Total	399	2.33306E+16
	Coefficients	Standard Error	t Stat	P-value	Lower 95%	Upper 95%	Lower 95.0%	Upper 95.0%
Intercept	-573780	506028.676	-1.133888908	0.257523661	-1568603.494	421042.9	-1568603	421042.9
PTS AV	880462.5	44896.61552	19.61088853	2.08268E-60	792198.3658	968726.7	792198.4	968726.7

Conclusions:-

The dependent variable for this study was NBA player salaries and the independent variables were the ten offensive and defensive statistical categories. an NBA player’s monetary value is based on metrics that are similar and equitable to his teammates.

The multiple regression analysis was conducted to determine which explanatory variables were predictors of NBA player salaries. We screened the data to determine if multicollinearity existed among our chosen explanatory variables. Key indicators that measure multicollinearity are tolerance and the variance inflation factor (VIF). Tolerance values that are less than .1 suggest that multicollinearity exists

References :- http://thesportjournal.org/article/determinants-of-nba-player-salaries/