Question

Here is another set of regression results, for a generic DV called y and a generic IV called x. SUMMARY OUTPUT Regression Statistics Multiple R 0.09855694 R Square 0.00971347 Adjusted R Square -0.0040405 Standard Error 20.5897879 Observations 74 ANOVA df SS MS F Significance F Regression 1 299.3986 299.3986 0.70623 0.40348 Residual 72 30523.63 423.9394 Total 73 30823.03 Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Intercept 44.3618365 5.205352 8.522351 1.62E-12 33.98516 54.73852 x -0.1587303 0.18888 -0.84037 0.40348 -0.53526 0.217796 7) How many observations were in this dataset? 8) Follow the four steps in Ch 14: Handout #2 to perform the hypothesis test to answer the following question: Is there a statistically significant relationship between y and x? Use an ? = 0.05 significance level. The guidelines from the test in Question #1 apply here too. 9) Given your result in Question #8, is it appropriate to continue interpreting this model?

          Here is another set of regression results, for a generic DV called y and a generic IV called x.

SUMMARY OUTPUT

Regression Statistics
Multiple R	0.09855694
R Square	0.00971347
Adjusted R Square	-0.0040405
Standard Error	20.5897879
Observations	74

ANOVA
		df	SS	MS	F	Significance F
Regression	1	299.3986	299.3986	0.70623	0.40348
Residual	72	30523.63	423.9394
Total	73	30823.03

Coefficients	Standard Error	t Stat	P-value	Lower 95%	Upper 95%
Intercept	44.3618365	5.205352	8.522351	1.62E-12	33.98516	54.73852
x	-0.1587303	0.18888	-0.84037	0.40348	-0.53526	0.217796

7) How many observations were in this dataset?

8) Follow the four steps in Ch 14: Handout #2 to perform the hypothesis test to answer the following question: Is there a statistically significant relationship between y and x? Use an ? = 0.05 significance level. The guidelines from the test in Question #1 apply here too.

9) Given your result in Question #8, is it appropriate to continue interpreting this model?

Here is another set of regression results, for a generic DV called y and a generic IV called x.

SUMMARY OUTPUT

Regression Statistics
Multiple R 0.09855694
R Square 0.00971347
Adjusted R Square -0.0040405
Standard Error 20.5897879
Observations 74

ANOVA
df SS MS F Significance F
Regression 1 299.3986 299.3986 0.70623 0.40348
Residual 72 30523.63 423.9394
Total 73 30823.03

Coefficients Standard Error t Stat P-value Lower 95% Upper 95%
Intercept 44.3618365 5.205352 8.522351 1.62E-12 33.98516 54.73852
x -0.1587303 0.18888 -0.84037 0.40348 -0.53526 0.217796

7) How many observations were in this dataset?

8) Follow the four steps in Ch 14: Handout #2 to perform the hypothesis test to answer the following question: Is there a statistically significant relationship between y and x? Use an ? = 0.05 significance level. The guidelines from the test in Question #1 apply here too.

9) Given your result in Question #8, is it appropriate to continue interpreting this model?

Added by Irene R.

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