STATISTICAL ANALYSIS

Statistical Analysis

Table of Contents

Executive Summary3

Hypotheses4

Variables4

Methodology5

Results5

Hypothesis Assessment Summary8

Conclusion9

Executive Summary

This paper studies the effect or relationship of batting average with the at bats, hits, triples, home runs, runs batted-in and NL or Not of the players. The data of the study is obtained from 100 players, which is gathered from the Major League Baseball players batting stats of season 2012. To get the results, multiple liner regression analysis with the Backward Elimination method is used which reveals that runs batted-in, at bats, triples, hits and home runs are related with the average batting score of MLB players.

Introduction

This study is based on the analysis of batting of Major League Baseball of 2012. The study particularly focuses in finding the effect or relationship of batting average with the independent variables which will help in getting the better knowledge or way of getting runs.

Hypotheses

H o 1: There is an effect of average batting on at-bats.

H o 2: There is an effect of average batting on the hits.

H o 3: There is an effect of average batting on the triples.

H o 4: There is an effect of average batting on the home runs.

H o 5: There is an effect of average batting on the runs batted-in.

H o 6: There is an effect of average batting on NL or Not.

Variables

In the study, following variables are included to find the effect of dependent variable that is batting average on independent variables:

AB = At Bats;

H = Hits;

3B = Triples;

HR = Home Runs;

RBI = Runs Batted In;

NL Or Not.

Methodology

The methodology is an integral part of any research study, for that reason, data of 100 players is collected from Major League Baseball player batting stats of season of 2012. Moreover, the ages of players are from 17 years to 51 years. In the study, the statistical technique that is applied is the regression analysis, in this context, to get the valid model of regression that is to get the significance of the variables, Backward Elimination method is used.

Results

Model Summary c

Model

R

R Square

Adjusted R Square

Std. Error of the Estimate

1

.996 a

.992

.992

.00179

2

.996 b

.992

.992

.00179

a. Predictors: (Constant), NL Or Not, HR, AB, 3B, H, RBI

b. Predictors: (Constant), HR, AB, 3B, H, RBI

c. Dependent Variable: AVG

The above given table pertains to model summary which presents two regression models, the basis of finding the two regression models is that backward elimination method is used to eliminate the insignificant variable or variables from the regression model. For that reason, it can be observed in both the models that there is strong relationship of average batting score with the independent variables as the value of R-square is 0.992 and the value of adjusted R-square is also 99.2% indicating strong relationship with the variables. Moreover, to know about the further impact, it is vital to consider the given below table;

ANOVA a

Model

Sum of Squares

df

Mean Square

F

Sig.

1

Regression

.038

6

.006

1944.732

.000 b

Residual

.000

93

.000

Total

.038

99

2

Regression

.038

5

.008

2356.748

.000 c

Residual

.000

94

.000

Total

.038

99

a. Dependent Variable: AVG

b. Predictors: (Constant), NL Or Not, HR, AB, 3B, H, RBI

c. Predictors: (Constant), HR, AB, 3B, H, RBI

The above table of analysis of variance table shows two models for the regression analysis which reflects ...