Solutions To Statistics Exam Questions

Cumulative Frequency distibution	Relative frequency	Cumulative relative frequency distribution	Interval	Percent frequency distribution
0	0	0	50	0%
3	0.15	0.15	50-60	15%
5	0.1	0.25	60-70	10%
11	0.3	0.55	70-80	30%
15	0.2	0.75	80-90	20%
20	0.25	1	90-100	25%

		Bin	Frequency
		0	1
		0.15	2
		More	3


Question two
a) The sample size is 41.

b) MSreg =


MSres =

F-statistic =

F-tabulated = 4.091
Decision rule : F-tabulated > F calculated hence we conclude that demand and unit price and related.

c)

It means that only 4.8% of the variation in Y is explained by the variation in X.

d)


Supply and unit price are positively correlated but the correlation is weak.

e)
Employee’s output for a day’s work
Program A	Program B	Program C	Program D
150	150	185	175	Anova: Single Factor
130	120	220	150
120	135	190	120	SUMMARY
180	160	180	130	Groups	Count	Sum	Average	Variance
145	110	175	175	Program A	5	725	145	525
				Program B	5	675	135	425
				Program C	5	950	190	312.5
				Program D	5	750	150	637.5


				ANOVA
				Source of Variation	SS	df	MS	F	P-value	F crit
				Between Groups	8750	3	2916.667	6.140351	0.00557	3.238872
				Within Groups	7600	16	475

				Total	16350	19


Weekly sales data
Week	Price	Advertising	Sales			SUMMARY OUTPUT
1	0.33	5	20
2	0.25	2	14			Regression Statistics
3	0.44	7	22			Multiple R	0.877814
4	0.4	9	21			R Square	0.770558
5	0.35	4	16			Adjusted R Square	0.655837
6	0.39	8	19			Standard Error	1.83741
7	0.29	9	15			Observations	7

						ANOVA
							df	SS	MS	F	Significance F
						Regression	2	45.35284	22.67642	6.716801	0.052644
						Residual	4	13.5043	3.376075
						Total	6	58.85714

							Coefficients	Standard Error	t Stat	P-value	Lower 95%	Upper 95%	Lower 90.0%	Upper 90.0%
						Intercept	3.597615	4.052244	0.887808	0.424805	-7.65322	14.84845	-5.04115	12.23638
						Price	41.32002	13.33736	3.098065	0.036289	4.289567	78.35048	12.88681	69.75324
						Advertising	0.013242	0.327592	0.040422	0.969694	-0.8963	0.922782	-0.68513	0.711617

a) Regression equation: Sales = 3.597615086 + 41.32002219Price + 0.013241819Advertising

b) Significance of the model: Since the F statistic ( 0.05264) is less than the level of significance (0.1) we conclude that the model is significant in explaining the variation in sales.

c)At 0.1 level of significance, we conclude that price is significantly related to sales because its P-value is less than 0.1 while advertising is not significantly related to sales since its P-value > 0.1

d)
Weekly sales data
Week	Price	Sales
1	0.33	20		SUMMARY OUTPUT
2	0.25	14
3	0.44	22		Regression Statistics
4	0.4	21		Multiple R	0.877761
5	0.35	16		R Square	0.770464
6	0.39	19		Adjusted R Square	0.724557
7	0.29	15		Standard Error	1.643765
				Observations	7

				ANOVA
					df	SS	MS	F	Significance F
				Regression	1	45.34733	45.34733	16.78311	0.009385
				Residual	5	13.50981	2.701963
				Total	6	58.85714

					Coefficients	Standard Error	t Stat	P-value	Lower 95%	Upper 95%	Lower 90.0%	Upper 90.0%
				Intercept	3.581788	3.608215	0.992676	0.366447	-5.69342	12.857	-3.68894	10.85252
				Price	41.60305	10.15521	4.096719	0.009385	15.49825	67.70786	21.13981	62.0663

Regression equation: Sales = 3.5818 + 41. 603 Price

Turn in your highest-quality paper
Get a qualified writer to help you with

“ Solutions To Statistics Exam Questions ”

Get high-quality paper

NEW! AI matching with writer

Order an Essay Now & Get These Features For Free:

Turnitin Report

Formatting

Title Page

Citation

Outline

Place an Order