Question

# Q3) In a difference of proportion test with alpha= .01, the critical value for a one...

Q3) In a difference of proportion test with alpha= .01, the critical value for a one tailed lower test is/are: A) -1.96 B) -1.645 and 1.645 C) -2.33 D) -1.28

Q4) If the test statistic for a small sample difference of means test is t*=-2.15, we could reject the Null Hypothesis at alpha =.01 for a two tailed test with degrees of freedom equal to 20: true or false

Q9) In a difference of MEans test there are two sources of variability that must be considered to calculate the standard error: True or false

Q6) For a small sample difference of means test where n1 = 20 and n2 = 20, and we can assume the population variances are equal, the formula for the pooled variance is: A) (s1+s2)/(n1+n2) B)(s1^2 + s2^2)/2 C)(s1^2 + s2^2)/(n1+n2) D) (s1+s2)/2

Q10)When we pool the variance in a difference of means problem, it changes the way the degrees of freedom are calculated (compared to situation when we don’t pool the variance). True or false

Q24)A covariance coefficient is bounded between 1 and -1. True or false

Q27)A correlation coefficient shows both strength and direction of a linear relationship. True or false

Q29) A regression analysis was done of residential property values in a city based on 157 properties. The independent variable used to predict the Sale Price (Y) is Square Footage (X). The estimated regression equation is: Y = 96,600 + 22.5X   R-square = .77      Where Y= Sale Price   X = Square Footage.

Interpret the value of R-square. :

 A) We are 77% confident that Square Footage is linearly related to Sale Price (Y).
 B) 77% of the observed sale process will fall within two standard deviations from the least squares line.
 C) 77% of the variability in Sale Prices can be explained by Square Footage

D) There is a moderately strong negative correlation between Sale Price and Square Footage.

Q30) A regression analysis was done on 81 different SUV vehicles. The variables analyzed Included are the average gas mileage in miles per gallon (Average MPG) and the engine size in liters (Engine Size). The average miles per gallon is a weighted average of city and highway driving to reflect a 40/60 percent split.

The estimated regression equation is: Average MPG = 27.654 - 2.114*Engine Size

R-squared = .4615

Based on this result, the correlation between Average MPG and Engine Size is:

 A) SQRT(.4615) *1, since the relationship between Average MPG and Engine size is positive
 B) 0.4615
 C) Cannot be determined from the data given
D)

SQRT(.4615) *-1, since the relationship between Average MPG and Engine size is negative

Q31) In regression, the variable we are trying to “explain” is referred to as the independent variable. : True or false

Q32) In the equation, Y = 6 + 12(X), the slope value is 6. True or false

Q33) A regression analysis was done on 81 different SUV vehicles. The variables analyzed Included are the average gas mileage in miles per gallon (Average MPG) and the engine size in liters (Engine Size). The average miles per gallon is a weighted average of city and highway driving to reflect a 40/60 percent split.

The estimated regression equation is: Average MPG = 27.654 - 2.114*Engine Size

R-squared = .4615

Based on this result, the relationship between Average MPG and Engine Size is:

 A)There is no relationship between Engine Size and Average MPG
 B)Positive. As Engine Size increases the Average MPG also increases.
 C)Negative. As Engine Size increases the Average MPG decreases.
 D)The average MPG for the SUVs is 27.654

Q 3) For left tailed test at 1% significance level, the critical value of Z is -2.33 (From Standard normal table)

Q 4) test statistic t* = -2.15

Significance level

Df = 20

Two tailed test,

The critical value of t for two tailed test with 20 df at 1% significance level is +/-2.845

Since |t| calculated is less than |t| tabulated . Fail to Reject H0

Q9) In a difference of MEans test there are two sources of variability that must be considered to calculate the standard error:

Q 6)

The pooled variance is

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