Question

Hello, appreciate if anyone could help me on Multiple Regression analysis. Thanks!

Question 4 Use the multistep process to interpret the regression result below. This model has been run by a researcher trying to explain user pleasure of browsing Facebook. The independent variables are user perceptions of Perceived Usefulness, Complementary Convenience and Entertainment. Model Summary Change Statistics Std. Error R of the Adjusted R R Sig. F Change Model R df2 df1 Square Change Square Estimate Change Square 392 .097a 1.003 317 000 600 .009 3 66620 5405 314 .000 282 41.665 56611 291 278 2 31.238 311 000 675c 164 3 49865 456 440 3 a. Predictors: (Constant), Level of Education, Gender, Age b. Predictors: (Constant), Level of Education, Gender, Age, Convenience, Perceived Usefulness Complementary c. Predictors: (Constant), Level of Education, Gender, Age, Convenience, Perceived Usefulness Complementary, Entertainment, Socializing, Curiosity d. Dependent Variable: Pleasure Coefficients Callinearity Statistics Correlations Sig. Unstandardized Coefficients Std Error 180 Standardized Model Coefficients Partial VIF Tolerance Zero- Part Beta order .000 376 (Constant Age Gender 19.580 3.524 1.032 .969 050 050 056 .887 060 050 047 053 1.032 .969 056 -1.002 317 -.066 -.057 -.072 072 286 2.620 009 749 (Constant) Age Gender 2 Perceived Usefulness 955 1.047 1.051 159 079 067 1.413 060 .045 068 064 951 772 015 323 747 -066 018 .016 020 061 1.295 248 214 000 4.552 428 046 .244 210 .761 1.314 248 214 424 246 4.553 000 051 231 Complementary Convenience (Constant) Age Gender 1.251 799 233 200 000 650 407 224 4.258 049 207 -455 259 -.118 1.051 044 952 043 275 062 1.093 732 060 .045 039 1.055 948 041 029 465 -.066 030 .052 .038 1.392 718 428 .136 097 2.418 016 Perceived 3 Usefulness .115 099 041 687 1.456 .110 079 Complementary 051 004 424 1.959 046 .095 090 1.338 747 161 116 407 3 Convenience Entertainment a. Dependent Variable: Pleasure of Browsing 124 1.418 188 189 705 4.698 513 257 225 000 040 Based on the results of the Model Summary and Coefficient, i) Are there any multicollinearity problems? Explain your answer statistics quoting any relevant (5 marks) ii) What is the role of Gender and Age variables and dependent variable? Explain your answer statistics on the relationship between the independent quoting any relevant (7 marks) ili)At the 5% significance level, does the results appear that any of the predictor variables can be removed from the full model as unnecessary? Explain your answer quoting any relevant statistics. (8 marks) iv) What substantive conclusions would you recommend to increase the pleasure of browsing the Facebook by Facebook user? (10 marks) (Total: 30 marks)

Answer 1

i)

Multicollinearity of the predictor variables are defined by Variance Inflation Factors(VIF) statistic. If the measures VIF statistic value is between 1 to 10, then there is no sign of multicollinearity.

From the regression model summary,

Model	Predictor variable	VIF	Multicollinearity
1	Age	1.032	no
	Gender	1.032	no
2	Age	1.047	no
	Gender	1.051	no
	Perceived Usefulness	1.295	no
	Complementary	1.314	no
	Convenience	1.251	no
3	Age	1.051	no
	Gender	1.055	no
	Perceived Usefulness	1.392	no
	Complementary	1.456	no
	Convenience	1.338	no
	Entertainment	1.418	no

ii)

The significance of predictor variables, age and gender determine by significance p-value of the respective coefficient value.

From the regression model summary,

Model	Predictor variable	Coefficient	Sig.
1	Age	0.047	0.376	>0.05	Not Significant
	Gender	-0.071	0.317	>0.05	Not Significant
2	Age	0.064	0.159	>0.05	Not Significant
	Gender	0.02	0.747	>0.05	Not Significant
3	Age	0.043	0.275	>0.05	Not Significant
	Gender	0.038	0.465	>0.05	Not Significant

In each of the three model both the predictor variable Age and Gender are not significant at 5% significance level.

iii)

Model	Predictor variable	Significance P-value
1	Age	0.376	Not Significant
	Gender	0.317	Not Significant
2	Age	0.159	Not Significant
	Gender	0.747	Not Significant
	Perceived Usefulness	0	Significant
	Complementary	0	Significant
	Convenience	0	Significant
3	Age	0.275	Not Significant
	Gender	0.465	Not Significant
	Perceived Usefulness	0.016	Significant
	Complementary	0.051	Significant
	Convenience	0.004	Significant
	Entertainment	0	Significant

All the other predictor variable except Age and Gender are significant hence only these two variable need to be removed from the model.

iv)

The R-square value and adjusted R-square value both increased by adding the predictor variables Perceived Usefulness, Complementary and Convenience in model 2 and further increase by adding Entertainment in model 3. These variables significantly explains the model while the variables Age and Gender need to be remove to improve the model further.