Checklist for each problem 1 State the Model used sampling d

Check-list for each problem:

1) State the Model used: sampling distribution of the means, sampling distribution of the proportion, chi square, regression

2) State: Ho, Ha

3) Shade the diagram and label (Shade 1TT or 2TT, )

4) Determine / State/ Generate the following: Significance level, confidence level, decision rule, p-value, Interpretation of the p-value, Conclusion statistically worded, Conclusion worded in English, Significant of the p-value, Probability of Type I error, Type I error - English interpretation, Probability of Type I I error, Type I I error - English interpretation , independent variable, dependent variable, regression equation, correlation coefficient, interpretation of the coefficient of determination, Chi Square value

5) Show: calculations for all related values and the exact calculator functions used. Use all the appropriate symbols for each variable and concept.

Craig, a marketing executive for a microbrewery company, wants to determine if there is a positive linear relationship between advertising expenditures and sales for their new lite beer product, Less Ale. He randomly samples data for the past 7 sales years and records the sample data in the following table, where advertising expenditure is measured in thousands and the beer sales in millions of dollars. a = 1%

Expenditure Sales Year(thousand $) 2008 2009 2010 2011 2012 2013 2014 Using the regression equation, predict the beer sales for the (million $) 48 45 52 60 50 45 58 35 47 65 92 25 82 advertising budget expenditure of $57,000.

Solution

H0: The regression is insignificant

H1: The regression is significant

Excel is used for the analysis.

The results

p value is less thn 0.5, hence we reject the null hypothesis and conclude that the regression is significant.

Regression Analysis
	r²	0.891	n	7
	r	0.944	k	1
	Std. Error	2.154	Dep. Var.	sales
ANOVA table
Source	SS	df	MS	F	p-value
Regression	189.6668	1	189.6668	40.89	.0014
Residual	23.1904	5	4.6381
Total	212.8571	6
Regression output				confidence interval
variables	coefficients	std. error	t (df=5)	p-value	99% lower	99% upper
Intercept	37.8556	2.2316	16.964	1.30E-05	28.8575	46.8536
expenditure	0.2319	0.0363	6.395	.0014	0.0857	0.3782
Predicted values for: sales
		99% Confidence Interval	99% Prediction Interval
expenditure	Predicted	lower	upper	lower	upper	Leverage
57,000	13,258.862	4,930.933	21,586.791	4,930.929	21,586.796	919738.735