ECON 203 Lecture Notes - Lecture 1: Form 10-Q, Statistical Hypothesis Testing, List Of Statistical Packages

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29 Mar 2017

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Statistical technique that describes the relationship among variables. Makes all residuals as small as possible- minimizes the sum of the squared residuals. Where the regression line crosses the vertical axis (exp variable 0) Average expenditure on x by those who have zero y. How the explanatory variable effects the response variable how much the dependent variable changes if the explanatory variable changes (what happens to y as x changes) Y = a + bx if x increases by 1 unit, y will increase by b units. Difference between the actual and the predicted value of the dependent variable. The amount of variation predicted by the model (goes from 0 to 1) A high r2 means that the model perfectly predicts the information, and a low r2 means that it doesn"t predict things very well. Predict the level how much someone with x income will spend on food.

Related Questions

ECON 3050

Q24. The coefficient of determination (r²) is calculated as 0.49, then the correlation coefficient:

Cannot be determined without the data

Should be - 0.70 or 0.70

Should be 0.

Neither of the above

Q25. A regression line is used for all of the following except one. Which one is not a valid use of a regression line?

To estimate the average value of Y at a specified value of X.

To predict the value of Y for an individual, given that individual's X-value.

To estimate the change in Y for a one-unit change in X.

To determine if a change in X causes a change in Y.

Q26. Which choice is not an appropriate description of YË in a regression equation?

Estimated response

Predicted response

Estimated average response

Observed response

Q27. Which of the following is the best way to determine whether or not there is a statistically significant linear relationship between two quantitative variables?

Compute a regression line from a sample and see if the sample slope is 0.

Compute the correlation coefficient and see if it is greater than 0.5 or less than â0.5.

Conduct a test of the null hypothesis that the population slope is 0.

Conduct a test of the null hypothesis that the population intercept is 0.

Q28. There is no relationship between variables unless the data points lie in a straight line.

True

False

Q29. The sample regression analysis

Is the same as the population regression line

Is used to estimate the population regression line

Shows the true relation between dependent and independent variables

None of the above.

Q30. In regression, a dependent variable is sometimes called a predictor variable.

True

False

Q31. In a linear regression equation of the form y = a + bx, the intercept shows

The amount that y changes when x changes by one unit

The amount that x changes when y changes by one unit

The value of y when x is zero

The value of x when y is zero.

Q32. In a linear regression equation of y = a + bx, the slope b shows

Y / b

X / b

Y / X

X / Y

yellowturtle964

Demand Estimation 1. A multiple regression model, Y = a + bX + cX 2, is estimated by creating a new variable named âX2â that equals X 2 (X square). A computer package produces the following output:

Dependent Variable Y	R Square	F-Ratio	P-Value on F
Observations :27	0.8766	85.25	0.0001
	Parameter	standard
Variable	Estimate	Error	T-ratio	P-value
Intercept	8000	3524.0	2.27	0.0325
X	-12.00	4.50	-2.67	0.0135
X2	0.005	0.002	2.5	0.0197

a. Provide the estimated equation. b. Test to see if the estimates of a, b, and c are statistically significant at the 5 percent significance level. c. What is the exact level of significance for a, b, and c? d. What is the fraction of total variation in Y that is explained by the regression equation? e. Is the overall regression equation statistically significant at the 5 percent level? What is the exact level of significance of the equation as a whole? f. If X is equal to 1,200, how much is Y?

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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ECON 203 Lecture Notes - Lecture 1: Form 10-Q, Statistical Hypothesis Testing, List Of Statistical Packages

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