CRIM 320 Lecture Notes - Mahalanobis Distance, Scatter Plot, Analysis Of Variance

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blueseal520

13 Apr 2012

School

Simon Fraser University

Department

Criminology

Course

CRIM 320

Professor

Patrick Lussier

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Document Summary

Research hypothesis: h1: older offenders have had more opportunities to offend and, therefore, have committed more crimes in adulthood. Null hypothesis: h0: age is not related to the number of crimes committed by offenders in adulthood. The change in x associated with one-unit of change in y: e=residuals. Represents the prediction error or the gap between the predicted value of y and the actual value of y. The regression line uses y to best fit the data at each value of x: the ordinary least squares regression . Residual variance: gap between regression line and each y. Regression variance/residual variance how well the regression line explains all observations. The bigger the slope, the stronger the relationship. Improve the prediction of y by using multiple (several) independent variables (xi) Looking for the combination of weights for the iv (xi) to get predicted values of. Y as close as possible from the actual values of y.

Related Questions

PROBLEM 1: Analyze the following data using LINEAR TRENDANALYSIS. How many cars will be sold in time period 13?

TIME PERIOD	NUMBER OF CARS SOLD
1	70
2	80
3	66
4	74
5	64
6	76
7	72
8	83
9	82
10	76
11	84
12	80

PROBLEM 2: A sample of 20 cars was taken, and the miles pergallon (MPG), horsepower, and total weight were recorded.

Develop a simple regression model to predict the dependentvariable, MPG, using horsepoweras the independent variable.

Develop a simple regression model to predict the dependentvariable, MPG, using weight asthe independent variable.

Using RÂ², which of the 2 causal forecasting models, A or B, isbetter?

Develop a multiple regression model to predict the dependentvariable, MPG, using horsepowerand weight as the 2 independent variables.

Using RÂ², how does the multiple regression model compare witheach of the 2 simple regression models?

MPG (Y)	HORSEPOWER (X1)	WEIGHT (X2)
44	67	1844
44	50	1998
40	62	1752
37	69	1980
37	66	1797
34	63	2199
35	90	2404
32	99	2611
30	63	3236
28	91	2606
26	94	2580
26	88	2507
25	124	2922
22	97	2434
20	114	3248
21	102	2812
18	114	3382
18	142	3197
16	153	4380
16	139	4036

give answer to the problem 1 and 2 in excel.

chocolateyellowjacket504

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

Photon Dynamics has run two simple regressions and one multipleregression to estimate FOH costs: the simple regressions use # ofunits produced and the # of machine hours as the independentvariables whereas the multiple regression uses both variables. Theoutputs of these 3 regressions are provided below:

Item	Simple regressiom (units)	Simple regression (machine hours)	Multiple regression (both variables)
Intercept	$3,000	$2,600	$3,200
R²	0.89	0.67	0.92
Coefficient/slope	$2.50	$1.82	$1.80 (units)
			$4.90 (machine hours)

Using the best cost estimation model determine the estimated FOHcosts if Photon expects to produce 300 units and use 50 m/chours?

Question 16 options:

	$3,750
	$3,985
	$2,691
	$4,760

ochrehornet688

CRIM 320 Lecture Notes - Mahalanobis Distance, Scatter Plot, Analysis Of Variance

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