Critically review the importance of residual analysis in regression

1. A researcher wants to assess the impact of interet rate and exchange rate on S&P/ASX 200 Benchmark Index. So, he gathered a data set from 1992 -2021. The below is the summarizes regression output. However, it is incompete to a certain extend.

SUMMARY OUTPUT
Regression Statistics
Multiple R	0.748527
R Square	0.560293
Adjusted R Square	A
Standard Error	1159.889
Observations	30

ANOVA
	df	SS	MS	F	Significance F
Regression	2	46285802	23142901	D	0.0000152
Residual	B	36324234	C
Total	29	82610035
	Coefficients	Standard Error	t Stat	P-value	Lower 95%	Upper 95%
Intercept	6394.028643	572.7939949	11.16288	0.0000000	5218.752444	7569.304841
Inflation Rates	E	20261.7341	1.38328	0.1779114	-13545.99714	69601.29154
Interest Rates	-63459.36165	10886.28913	-5.82929	0.0000033	-85796.18191	-41122.5414

Based on the above incomplete regression output, you are required to:

1. State the multiple regression equation.

2. Interpret the meaning of regression coefficients.

3. Calculate the adjusted R square and interpret its meaning.

4.Determine whether there is a significant relationship between dependent variable and the two independent variables at the 5% level of Significance.