NAME: 							
							
We want to predict the sales of a company based on two independent variables: 							
advertising budget (in thousands of dollars) and store size (in square meters). 							
							
	Advertising Budget (X1)	Store Size (X2)	Sales (Y)				
	10	200	300				
	12	220	350				
	15	240	400				
	14	210	380				
	13	230	370				
	11	200	310				
	16	250	420				
	18	260	450				
	17	270	440				
	19	280	470				
							
1) Calculate coefficients and write the equation:							
							
							
"2) We can use this equation to predict sales for values of Advertising Budget is  $16,000 "							
and Store Size is 250 square meters. 							
The predicted sales would be: 							
							
							
							
3) How would you interpret the coefficient β1 for Advertising Budget?							
							
							
4) How would you interpret the coefficient β2 for Store Size?							
							
							
5) Test at the 5% significance level whether the regression coefficient β1 is statistically significant? 							
Why yes or why no?							
							
							
6) Test at the 5% significance level whether the regression coefficient β2 is statistically significant? 							
Why yes or why no?							
							
							
7) Is multicollinearity present? Calculate the correlation matrix.							
							
							
							
Read the following paragraph about multicollinearity.							
What effect does multicollinearity have on testing the significance of coefficients?							
Multicollinearity occurs when independent variables are highly correlated with each other. 							
This can affect the estimation of coefficients and their statistical significance because 							
high correlation between variables makes it difficult to determine which 							
variable has a real effect on the dependent variable.							
" In such cases, coefficients may have high standard errors, leading to high p-values, "							
even if the model is correct.