Page 440 - MARSIUM'21 COMP OF PAPER

ANOVA
a
Model Sum of Squares df Mean Square F Sig.
b
1 Regression 10.438 3 3.479 1.696 .173
Residual 209.281 102 2.052
Total 219.719 105
a. Dependent Variable: PD
b. Predictors: (Constant), GI, CQ, C
Table 4.7 (b) ANOVA

The table shows that the regression model accurately predicts the dependent variable. What proof do we have for
this? Look for the "Sig." column in the "Regression" row. The statistical significance of the regression model that was
conducted is indicated below.

a
Coefficients
Standardiz

Unstandardized ed 95.0% Confidence
Coefficients Coefficients Interval for B
Lower Upper

Model B Std. Error Beta t Sig. Bound Bound
1 (Constan 4.186 1.311 3.194 .002 1.586 6.785
t)
CQ .243 .309 .108 .786 .433 -.369 .855

C 2.213 1.086 .774 2.038 .044 .059 4.366

GI -2.400 1.072 -.874 -2.239 .027 -4.527 -.274

a. Dependent Variable: PD

Table 4.7 (c) Coefficients

We can observe from this table that B-coefficients are the same as we saw in our scatterplot. These indicate the linear
regression equation that best predicts the influencer marketing factors from purchase decision in our sample, as demonstrated.

Second, for content quality (CQ), 0.059 for consistency (C), and 0.027 for good influencer strategy, the B coefficient is "Sig"
or p = 0.433. (GI). It is statistically significant when compared to zero.

4.7 MULTIPLE REGRESSION ANALYSIS

Multiple regression analysis (MRA) is a statistical method for evaluating the relationship between a dependent
variable and independent variables, according to Petchko (2018). Coefficients are the estimates derived by this statistical
method. The magnitude of regression coefficients represents how much each predictor variable contributes to the variation in
the dependent variable by itself after all other predictor variables in this study have been analytically removed. When it's
been well-established for a long time, the multiple regression analysis approach is extensively employed by most practitioners
and academicians (Shetty et al., 2020). Furthermore, multicollinearity is a statistical phenomenon in which two or more
predictor variables in a multiple regression model have a strong correlation (Jamal, 2017). Whenever tolerance

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