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Economics
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12 Dec 2019

Which of the following is true of the t-statistic?
It is the value of the coefficient estimate divided by its standard error.
It is the coefficient’s standard error normalized to lie between 0 and 1.
It tells us how many standard errors the coefficient estimate is equal to zero.
It measures the overall statistical validity of the regression equation.
It is the sum of the value of the coefficient estimate and its standard error.

The standard error of the regression:
is equal to the slope of the regression equation.
measures the explanatory power of the regression equation and lies between 0 and 1.
is equal to the sum of squared errors minus the total sum of squares.
measures the explained variation in the dependent variable.
measures the unexplained variation in the dependent variable.

A regression analysis is said to suffer from multicollinearity when:
the dependent and independent variables move in the same direction.
the degrees of freedom in the regression is equal to zero.
the explanatory variables vary independently of one another.
the correlation coefficient between the predicted and the explanatory variables is equal to zero.
two or more explanatory variables tend to move together.

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Sixta Kovacek
Sixta KovacekLv2
13 Dec 2019
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Related questions

Econ 3050

7. When every member of the accessible population has an equal chance of being selected to participate in the study, the researcher is using:

a) Simple random sampling

b) Stratified random sampling

c) Convenience sampling

d) Purposive random sampling

8. A regression line has been found that has nearly all the points lying directly on the line, and only a few of them stray away from the line. Therefore, the standard error of the estimate is likely to be

Huge.

Small.

0.

Undefined.

9. Correlation coefficient ranges from _______ to ____________.

a) - 1 to 0.

b) 0 to 1.

c) -1 to 1.

d) None of the above.

10. The correlation coefficient of determination is the:

a) Ratio of the explained variation to the total deviation.

b) Ratio of the unexplained deviation to the explained deviation.

c) Ratio of the unexplained deviation to the total variation.

d) Ratio of the explained variation to the total variation.


11. If the correlation coefficient r = 0.5 then the coefficient of determination is:

a) 0.10

b) 0.25

c) 1.00

d) 2.50

12. Which one of the following is NOT appropriate for studying the relationship between two quantitative variables?

Scatterplot

Bar chart

Correlation

Regression

13. In regression, an independent variable is sometimes called a response variable.

True

False

14. The simple linear regression equation can be written y = b0 + b1x. In the simple linear regression equation, the symbol y represents:

The average or predicted response

Estimated intercept

Estimated slope

Explanatory variable

TYPE 1.300** (0.590)

PAR -0.195** (0.291)

SR -0.005 (0.068)

CR 0.483 (0.329)

LOC -0.575 (0.655)

THANDICAP -0.037 (0.091)

PHANDICAP -0.181 (0.075)

FEMALE 0.159 (0.708)

The table shows regression coefficients (standard errors in parenthesis) for the independent variables. The dependent variable is score. (again, under the stabelford system higher is better).

Type is a dummy variable with 1 for net score tournaments and 0 for gross score tournaments. Explain the meaning of the coefficient.

Is it significant at the 5% level? Explain.

What is the t-statistic on TYPE? ________________

The variables PAR and CR are measures of difficulty of the course, and PHANDICAP is a measure of the golfer's ability. Why are these variables included in the regression equation?

Which variable should be added to the regression if you want to test whether the effect of TYPE is different for male and female players? Why?

For the record, this is a homework question. A statistics student wishes to understand the relationship between a used car's price and the number of miles that car has been driven. The student goes to a local car lot and writes down the prices and the odometer reading for 100 cars. The student takes this information and completes a regression. The goal is to examine how the miles drove impacts the car's price. The regression output appears at the end of this assignment. Use the output to answer the following questions.

a) (3 points) Dependent variables Independent variable

b) (8 points) Do you expect a positive, negative, or no relationship between miles driven and price? Draw a scatterplot that corresponds to your relationship.

Be sure to include which variable (miles or price) is on the x-axis and which is on the y-axis.

b) (3 points) Write the regression equation (I want the specific estimates for the coefficients)

c) (3 points) What is the interpretation of the slope coefficient of b 1 (in terms of this problem)?

d) (4 points) Is b 1 significant from zero? How do you know?

e) (3 points) Use the regression equation to predict the price for a car that was driven for 30,000 miles. Is this mileage appropriate to make a prediction? Why or why not?

f) (3 points) Use the regression equation to predict the price for a car that was driven for 80,000 miles. Is this mileage appropriate to make a prediction? Why or why not?

g) (3 points) What percentage of price variation is explained by the variation in miles driven?

Multiple R 0.398635

R square 0.15891

Adjusted R Squared 0.150327

Standard Error 1481.414

Observations 100

df ss ms f significance f

Regression 1 40633716.14 40633716 18.51542325 3.99E-05

Residual 98 215069573.5 2194587

Total 99 255703289.7

coefficient standard error t stat p value lower 95%

intercept 20261.42 598.0313643 33.8802 6.53E-56 19074.64824

x1 -0.07332 0.017039697 -4.30296 3.99E-05 -0.107135778

Sample range for Y 14281 20511

Sample range for X 19100 49200

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