Problem statement:- The purpose of this study was to identify the performance va

Question

Problem statement:-

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 analysis output:-

To Do:-

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

2. Key variables and relevant assumptions/hypotheses should be included.

3. Interpret the regression model. Briefly comment on the assumptions of the model (residual diagnostics).

4. 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.

ata Analysis Salary Years in League Mean 7781809 Mean 26.1725 0.214293 26 23 8.1725 Mean Standard Error Standard Error 382337 Standard Error 0.214293 Median Mode Standard Deviation 4520810 Median 1312611 Mode 7646741 Standard 8 Median 5 Mode 4.285868 Standard Deviation Sample Variance Deviation 18.36867 0.007075 Kurtosis0.007075 0.591855 Skewness 0.591855 Sample Variance 5.85E+13 Sample Variance 18.36867 Kurtosis Skewness Range Minimum Maximum Sum Count 0.715295 Kurtosis 1.253769 Skewness 34582550 Range 100000 Minimum 34682550 Maximum 3.11E+09 Sum 21 Range 1Minimum 22 Maximunm 21 19 40 10469 400 3269 Sum 400 Count 400 Count

Explanation / Answer

1) Problem Summary:

a) The purpose of these study is to predict the salary of basketball players.

b) The salary of the players depends on his overall performance in the game.

c) Using regression analysis and provided information we can predict easily salary of the player.

In given example, only two predictor variable provided

x1 = Age of player and

x2 = years in the league (experience of players) and

Response variable is salary

The regression model is,

Salary = Intercept + x1 * Age + x2 * years in the league

2) we can estimate the effect of each predictor on the response variable but in ANOVA table we have given combined mean effect then the test of the hypothesis is,

H0: The mean effect of age and years of the league has the same effect on the salary of the player

against,

H1: The mean effect of age and years of the league has the different effect on the salary of the player

Decision Rule: If p-value is less than 0.05 level of significance then we reject the null hypothesis

here p-value (significant F value from ANOVA table) is very less so we reject the null hypothesis

I.e model is not good not perform better with less information

3) From the summary of the regression model,

the value of R square is 0.49143

Adjusted R square 0.490152 is not good

the value of adjusted R square required at least more than 70

therefore the model is not good

4) The finding for these model is the model are not good

We required to use for this study secondary data the results of the past matches

Required more predictor to study,

Response variable = Salary of players

Predictor Variable,

a) Age of players

b) No of goals

c) No of matches played

d) no of hours he played

c) rate of the successive ball passing

d) rate of success to do goal

Linear Regression Model is good aproach to use here because the response variable is continueous and follows normal distribution we can also use suport vector machine here.

>>>>>>>>>>>>>>>>>>>> Best Luck >>>>>>>>>>>>>>>>

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