Muliple Regression

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Table of Contents

Introduction....................................................................................................................................................4

Descriptive Analysis......................................................................................................................................4

Correlation Analysis......................................................................................................................................4

Justification of Hierarchical MR Model........................................................................................................5

Basic Assumptions.........................................................................................................................................5

Assumption #1:..........................................................................................................................................5

Assumption #2:..........................................................................................................................................5

Assumption #3:..........................................................................................................................................5

Assumption #4:..........................................................................................................................................5

Assumption #5...........................................................................................................................................6

Assumption #6:..........................................................................................................................................7

Assumption #7:..........................................................................................................................................7

Assumption #8:..........................................................................................................................................8

Regression Analysis.......................................................................................................................................9

Conclusion...................................................................................................................................................11

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List of Table

Table 1: Descriptive Analysis........................................................................................................................4

Table2: Correlation........................................................................................................................................4

Table 3: Model 1 and 2..................................................................................................................................9

Table 4: Anova.............................................................................................................................................10

Table 5: Coefficient.....................................................................................................................................10

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List of Figures

Figure 1: Linear Relationship........................................................................................................................6

Figure 2: Homoscedasticity...........................................................................................................................7

Figure 3: Outliers...........................................................................................................................................8

Figure 4: Residual Errors...............................................................................................................................9

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Body Shape and Body Image Affect Self-Esteem

Introduction

Self-esteem is shaped by someone’s thoughts, relationships and experiences. Self-esteem, a general

overall evaluation of oneself, has been associated with being dissatisfied with one’s appearance such that

the more dissatisfied a human is with their body image and/or shape, the lower their self-esteem.

Therefore, this study will analyze the body image and body shape effect on the self-esteem.

Descriptive Analysis

The descriptive analysis in Table 1 shows the overall statistics of data, the descriptive statistics chosen

include: N, Minimum, Maximum, Mean, and Standard Deviation. N explains the number of respondents,

mean is showing the average values of each variables.

Table 1: Descriptive Analysis

Descriptive Statistics

N Minimum Maximum Mean Std. Deviation

Gender 291 0 1 .50 .501

Age 291 13 65 38.56 15.036

Height 291 135.31 224.54 172.1307 14.49341

Weight 291 31.84 170.38 68.1964 16.52109

BMI 291 14.24 60.58 22.8031 3.87238

BIS 291 62 146 100.81 13.988

SE 291 53 158 100.79 19.594

Valid N (listwise) 291

Correlation Analysis

The correlation analysis is conducted to find the relationship among variables. The Table 2 is showing

that there is positive significant relation between Self-esteem and body shape, while there is negative

relationship between self-esteem and BMI.

Table2: Correlation

Correlations

Gender Age Height Weight BMI BIS SE

Gender 1

Age -.027 1

Height .451** -.026 1

5

Weight .325** .008 .753** 1

BMI -.002 .038 .083 .711** 1

BIS .292** -.024 .164** -.120* -.360** 1

SE .217** .112 .132* -.050 -.207** .526** 1

**. Correlation is significant at the 0.01 level (2-tailed).

*. Correlation is significant at the 0.05 level (2-tailed).

Justification of Hierarchical MR Model

Hierarchical MR entry methods is used in the analysis because it does not rely on statistical results for

selecting predictors. It allows us a greater control on the regression process. The items are entered in a

given order based on theory, logic or practicality. This method is appropriate for analyzing the Self-

esteem because we have an idea as per previous theory that, which predictors may impact the dependent

variable.

Basic Assumptions

Assumption #1:

The dependent variable is Self Esteem which has been measured by on a continuous interval scale.

Assumption #2:

There are two major independent variables Body Image Satisfaction and Body Mass Index which are

measured by standardized questionnaire by following interval scale.

Assumption #3:

Durbin-Watson statistic explains the independence of observations by detecting the presence of

autocorrelation. As shown in the Table 3 the value of Durbin-Watson is 2.103 which indicates that there

is no autocorrelation.

Assumption #4:

The F-test is highly significant, thus we can assume that there is a linear relationship between the

variables in our model. The Figure 1 given below showing the linear relationship and satisfy the

assumption of linear relationship.

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Figure 1: Linear Relationship

Assumption #5

The data is showing homoscedasticity and the variances along the line of best fit remain similar as you

move along the line as shown in Figure 2.

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Figure 2: Homoscedasticity

Assumption #6:

Beta expresses the relative importance of each independent variables in standardized terms, there is no

issue of collinearity is data.

Excluded Variablesa

Model Beta In t Sig. Partial Correlation Collinearity Statistics

Tolerance VIF Minimum

Tolerance

1 BIS .518b 9.650 .000 .494 .871 1.148 .871

a. Dependent Variable: SE

b. Predictors in the Model: (Constant), BMI

Assumption #7:

There are only one and two outliers as shown in Figure 3, which cannot have much impact on the data.

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Figure 3: Outliers

Assumption #8:

The plot in Figure 4 indicates that in our multiple linear regression analysis there is no tendency in the

error terms and the residuals (errors) are approximately normally distributed.

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Figure 4: Residual Errors

Regression Analysis

The following Table 3 provides the R and R2 values of both variable BMI and BIS. The R value

represents the simple correlation. The first model show there is low degree of correlation 0.207,

while the second model which includes both predictors BMI and BIS indicates high degree of

correlation 0.526. The R2 value indicates how much of the total variation in the dependent

variable Self-esteem can be explained by the independent variable BMI and BIS. In first case

only 4.3% can be explained by predictor BMI, which is very small while in model 2 the value is

27.7% which is quite significant.

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Table 3: Model 1 and 2

Model Summaryc

Model R R Square Adjusted R

Square

Std. Error of the

Estimate

Durbin-Watson

1 .207a .043 .039 19.204

2 .526b .277 .272 16.723 2.192

a. Predictors: (Constant), BMI

b. Predictors: (Constant), BMI, BIS

c. Dependent Variable: SE

The Anova Table reports how well the regression equation fits the data, it predicts the dependent

variable as shown in the Table 4 below:

Table 4: Anova

ANOVAa

Model Sum of Squares df Mean Square F Sig.

1

Regression 4752.230 1 4752.230 12.885 .000b

Residual 106585.998 289 368.810

Total 111338.228 290

2

Regression 30794.847 2 15397.424 55.057 .000c

Residual 80543.380 288 279.665

Total 111338.228 290


b. Predictors: (Constant), BMI

c. Predictors: (Constant), BMI, BIS

This table indicates that the regression model predicts the dependent variable significantly well.

This indicates the statistical significance of the regression model that was run. Here, p < 0.000,

which is less than 0.05, and indicates that, overall, the regression model statistically significantly

predicts the Self-Esteem.

The Coefficients table provides us with the necessary information to predict Self-Esteem from

body shape and body image as shown in the Table 5. It determine whether independent variables

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contributes statistically significantly to the model by considering the Sig. values. The table

indicates that all the independent variables are significant.

Table 5: Coefficient

Coefficientsa

Model Unstandardized Coefficients Standardized

Coefficients

t Sig. Collinearity Statistics

B Std. Error Beta Tolerance VIF

1(Constant) 124.630 6.736 18.504 .000

BMI -1.045 .291 -.207 -3.590 .000 1.000 1.000

2

(Constant) 29.944 11.432 2.619 .009

BMI -.103 .272 -.020 -.377 .706 .871 1.148

BIS .726 .075 .518 9.650 .000 .871 1.148


Conclusion

The Hierarchal Multiple regression model is used to analyze the effect of body image and body

shape on the Self-Esteem. All the assumption are tested and satisfied before using the multiple

regression model. The result shows that the BMI and BIS are quite significant predictors but not

in case if they are used individually. The BMI is not able to explain the Self-Esteem significantly

when it used individually.

Muliple Regression

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