ADMS 2320 Study Guide - Sse5, Formula One, Sse4

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greenbat668

27 Apr 2014

School

York University

Department

Administrative Studies

Course

ADMS 2320

Professor

Michael Rochon

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Document Summary

In one way anova, the amount of total variation that is unexplained is measured by the: sum of squares for treatments a. sum of squares for error b. c. total sum of squares: degrees of freedom. In a two tail pooled variance t test (equal variances t test), the null and alternative hypotheses are exactly the same as in one way anova with a. exactly one treatment: exactly two treatments c. exactly three treatments, any number of treatments. The analysis of variance is a procedure that allows statisticians to compare two or more. Analysis of variance: means, proportions, variances d. If we want to conduct a test to determine whether a population mean is greater than another population mean, we a: must use the independent samples t test for difference between two means, must use the chi square test, both a and b are correct.

Related Questions

Y Annual restaurant sales (in thousands)

X1 Disposable per capital income (in thousands) of the residents living within 5 miles of a restaurant

X2 Population (in thousands) within a 5-mile radius of a restaurant

X3 Number of competing restaurants within a 5-mile radius

The following information was obtained from the regression analysis:

Multiple R: 0.92

R-Square: 0.85

Std. Error of Est.: 0.40

Analysis of Variance

DF Sum Squares Mean Sqr. F-Stat

Regression 3 220 73.3 18.2

Residual 36 60 1.7

Variable Coefficient Std. Error T-Value

Constant 0.4 0.2 2.0

X1 0.01 0.004 2.5

X2 0.02 0.015 1.3

X3 -20.2 4.50 -4.6

Answer the following questions:

a. Give the estimated demand equation for predicting restaurant sales.

b. Provide an interpretation for each of the regression coefficients.

c. Which of the coefficients are statistically significant and which are not? Explain.

d. What percent of variation are restaurant sales explained by this equation?

redhornet138

Chez Henri is a restaurant chain that operates in 40 different cities. It hired an economist to estimate the factors affecting the demand for its sales. The following equation was estimated using cross sectional data from each of its 40 restaurants.

Y
Annual restaurant sales (in thousands)

X1
Disposable per capital income (in thousands) of the residents living within 5 miles of a restaurant

X2
Population (in thousands) within a 5-mile radius of a restaurant

X3
Number of competing restaurants within a 5-mile radius

The following information was obtained from the regression analysis:
Multiple R: 0.92
R-Square: 0.85
Std. Error of Est.: 0.40

Analysis of Variance

DF
Sum Squares
Mean Sqr.
F-Stat

Regression
3
220
73.3
18.2

Residual
36
60
1.7

Variable
Coefficient
Std. Error
T-Value

Constant
0.4
0.2
2.0

X1
0.01
0.004
2.5

X2
0.02
0.015
1.3

X3
-20.2
4.50
-4.6

Answer the following questions:
a. Give the estimated demand equation for predicting restaurant sales.
b. Provide an interpretation for each of the regression coefficients.
c. Which of the coefficients are statistically significant and which are not? Explain.
d. What percent of variation are restaurant sales explained by this equation?

taupeclam801

*** Please see p. 2 for Question 2 ***

Question 2 (7 points)

The following Excel output shows the outcome of a linear regression of individuals%u2019 wage per hour (in dollars) on the number of years they attended school (in years).

SUMMARY OUTPUT

Regression Statistics
Multiple R	0.381932619
R Square	0.145872525
Adjusted R Square	0.144267022
Standard Error	4.753758428
Observations	534

ANOVA
	df	SS	MS	F	Significance F
Regression	1	2053.22554	2053.22554	90.8578469	5.45998E-20
Residual	532	12022.25261	22.59821919
Total	533	14075.47815

	Coefficients	Standard Error	t Stat	P-value	Lower 95%	Upper 95%	Upper 95.0%
Intercept	-0.745942699	1.045403804	-0.71354504	0.475821452	-2.799566599	1.307681201	1.307681201
Years of School	0.750448943	0.078729942	9.531938255	0.000545998	0.595789385	0.9051085	0.9051085

Part (a) (1 point)

What is the value of the estimated slope %u201Cb%u201D?

Part (b) (2 points)

Interpret the estimated value of the slope (i.e., explain what the number means in this regression).

Part (c) (1 point)

Is the estimate of the slope statistically significant? Please answer %u201Cyes%u201D or %u201Cno%u201D and explain how you can tell.

Part (d) (2 points)

Explain why we want to be able to reject the null hypothesis H0: %u03B2 = 0.

Part (e) (1 point)

How much of the total variation in wages can be explained by individuals%u2019 education?

ADMS 2320 Study Guide - Sse5, Formula One, Sse4

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