Question

A sales manager is interested in determining the relationship between the amount spent on advertising and total sales. the manger collects data for the past  months and runs a regression of sales on advertising expenditures. The results wıth some missing values are presented below.

Regression Statistics
Multiple R R Square Adjusted R Square Standard Error Observations	0.492 0.242 0.208 40.975

ANOVA
	df	SS	MS	F	Significance
Regression Residual Total	1

	Coefficients	Standard Error	t Stat	P-value
Constant Advertising

To test the significance of the regression what is the test statistic value?

Answer 1

It is given that F value = 7.034. Presently we need to ascertain degrees of opportunity for numerator and denominator.

Degrees of freedom for numerator = k - 1 where k = number of parameters = 2(intercept and advertising).

Along these lines, Degrees of freedom for numerator = k - 1 = 2 - 1 = 1

Degrees of opportunity for denominator = n - k where k = number of boundaries = 2 and n = number of perceptions = 24.

Along these lines, Degrees of opportunity for denominator = 24 - 2 = 22

Hence forth Using F table with above degrees of opportunity we have F critical for centrality = 0.01 is 7.945 and subsequently P value must be more noteworthy than 0.01 in light of the fact that f critical is lesser than f significance = 0.01.

Utilizing F table with above degrees of freedom we have F critical for centrality = 0.05 is 4.3009 and in this manner P esteem must be lesser than 0.05 in light of the fact that f basic is more than f significance determined at significance = 0.05.

Thus P value is somewhere in the range of 0.01 and 0.05

Thus, the right answer is (B) Smaller than 0.05.

2)

t statistic in regression = Parameter value/Standard error
Here,

t statistic = 2.652 Parameter value = 2.015 and we need to ascertain Standard blunder.

Thus Using above formula we have : Standard blunder = Parameter esteem/t measurement = 2.015/2.652 = 0.76

Thus, the right answer is (B) 0.76