1.Discuss minimum of two (2) Ordinary least square (OLS) assumptions and their importance for a validity of regression model

1.The Head of production at Amiba private limited needed to examine the key determinants of company profitability($,000). So, he collected relevant(monthly) data and following is the summary of his regression output.

SUMMARY OUTPUT
Regression Statistics
Multiple R	0.5776
R Square	0.3336
Adjusted R Square	0.2670
Standard Error	2.2743
Observations	34

ANOVA
	df	SS	MS	F	Significance F
Regression	3	77.6774	25.8925	5.0060	0.0062
Residual	30	155.1693	5.1723
Total	33	232.8467

	Coefficients	Standard Error	t Stat	P-value	Lower 95%	Upper 95%	Lower 95.0%	Upper 95.0%
Intercept	10.49	2.9211	3.5927	0.0012	4.5291	16.4605	4.5291	16.4605
Variable X	0.99	1.2461	0.7937	0.4336	-1.5559	3.5340	-1.5559	3.5340
Variable Y	2.56	1.1291	2.2663	0.0308	0.2529	4.8648	0.2529	4.8648
Variable Z	2.06	1.2777	1.6107	0.1177	-0.5515	4.6674	-0.5515	4.6674

1. Interpret the meaning of intercept coefficients and at 5% level review the significance of those variables to the model
2. Calculate the adjusted R square and interpret its meaning
3. Based on your opinion, decide the names of the explanatory variables used in the study. (Note: variable selection should have a valid justification)