PASTE INTO EXCEL AND USE THE DATA ANALYSIS ADD-IN TO PERFORM REGRESSIONS ON THE MODELS TO ANSWER THESE QUESTIONS

Download the State Crime Excel sheet; row 2 gives detailed descriptions of each variable, while row 4 gives short versions that should be used in your regressions. Estimate the following multiple regression models (remember that all of your independent variables will have to be in adjacent columns in Excel). Look at each set of results critically and consider how you would interpret the strengths and weaknesses of each model. Save your results from each model for use when completing the end-of-module assessment. Use Burglary as your dependent variable in each model. The notation f(X,Y,Z) means a function of X, Y, Z; i.e., X, Y, and Z are your independent variables. Even though it isnt listed, each model will include an intercept.

NOTE: when Excel reports a value like 2.4E-06, this is scientific notation for 2.410?6, or 0.0000024. Model A: Burglary = f(Population, PerCapIncome, %Poverty) Model B: Burglary = f(%Unemploy, %young) Model C: Burglary = f(%Unemploy, %youngmale) Model D: Burglary = f(%Unemploy, %youngfemale) Model E: Burglary = f(PerCapIncome, Rain, MedianAge, %Metro) Model F: Burglary = f(PerCapIncome, Temp, MedianAge, %Metro) Model G: Burglary = f(PerCapIncome, Sun, MedianAge, %Metro)

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In model A, how much of the variation in burglary is explained by population, per capita income, and % below the poverty line? Select one: a. 27.5% b. 44.8% c. 65.4% d. 55.2% Question 20 Not yet answered Marked out of 1.00 Flag question Question text

In model A, how would you interpret the coefficient for % poverty? Select one: a. As % below the poverty line increases by 27.6, the burglary rate increases by about 1. b. As the burglary rate increases by 1, % below the poverty line increases by 27.6. c. As % below the poverty line increases by 1, the burglary rate increases by about 27.6. d. As the burglary rate increases by 27.6, % below the poverty line increases by 8.88. Question 21 Not yet answered Marked out of 1.00 Flag question Question text

Download the State Crime Excel sheet; row 2 gives detailed descriptions of each variable, while row 4 gives short versions that should be used in your regressions. Estimate the following multiple regression models (remember that all of your independent variables will have to be in adjacent columns in Excel). Look at each set of results critically and consider how you would interpret the strengths and weaknesses of each model. Save your results from each model for use when completing the end-of-module assessment. Use Burglary as your dependent variable in each model. The notation f(X,Y,Z) means a function of X, Y, Z; i.e., X, Y, and Z are your independent variables. Even though it isnt listed, each model will include an intercept. NOTE: when Excel reports a value like 2.4E-06, this is scientific notation for 2.410?6, or 0.0000024.

Model A: Burglary = f(Population, PerCapIncome, %Poverty)

Model B: Burglary = f(%Unemploy, %young)

Model C: Burglary = f(%Unemploy, %youngmale)

Model D: Burglary = f(%Unemploy, %youngfemale)

Model E: Burglary = f(PerCapIncome, Rain, MedianAge, %Metro)

Model F: Burglary = f(PerCapIncome, Temp, MedianAge, %Metro)

Model G: Burglary = f(PerCapIncome, Sun, MedianAge, %Metro)

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In model A, how much of the variation in burglary is explained by population, per capita income, and % below the poverty line?

Select one:

a. 27.5%

b. 44.8%

c. 65.4%

d. 55.2%

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In model A, how would you interpret the coefficient for % poverty?

Select one:

a. As % below the poverty line increases by 27.6, the burglary rate increases by about 1.

b. As the burglary rate increases by 1, % below the poverty line increases by 27.6.

c. As % below the poverty line increases by 1, the burglary rate increases by about 27.6.

d. As the burglary rate increases by 27.6, % below the poverty line increases by 8.88.

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In model A, the coefficient for population is statistically significant at the 10% level.

Select one:

True

False

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In models B through D, what seems to be the relationship between the burglary rate and the percent of the 18-64 population who are young adults (18-24)?

Select one:

a. It is difficult to describe the relationship; the young adult variables were all significant at 5% in models B, C, and D, but the signs and sizes of the coefficients were very different between models.

b. Conclusions about the relationship between young adults and the burglary rate are difficult to draw since the unemployment rate variable is consistently positive and significant, which reduces the reliability of the estimates for the young adult variables.

c. The burglary rate seems to increase as the proportion of young adults increases (models B, C, and D), though the effect is only significant (at the 10% level) for young adult males (model C).

d. We can be confident that the relationship between the burglary rate and proportion of young adults is negative, given the signs of the young adult coefficients in all models and the high adjusted R-square values.

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In models B through D, the relationship between the unemployment rate and the burglary rate

Select one:

a. is unimportant since, even though it was statistically significant and of the expected sign, its coefficient estimates were always smaller than the young adult coefficients.

b. cannot be assumed to be different from zero since unemployment was insignificant in all models. This data seem to show that the unemployment rate has no effect on the burglary rate.

c. seems relatively stable at about a 60-unit increase in the burglary rate for every 1% increase in the unemployment rate.

d. is expected to be negative, so the only reliable model is C since it has the largest negative coefficient for the intercept. Models B and D, with smaller negative intercept coefficients, should not be used to draw inferences about unemployment and burglary.

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Of all the variables in models E through G, the % in a metro area is the only one that is statistically insignificant (at the 10% level) in all models.

Select one:

True

False

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In model A, it was seen that per capita income was negative and significantly (at the 10% level) related to the burglary rate. In models E through G, the per capita income variable is

Select one:

a. still negatively related to the burglary rate, but is no longer significant at the 10% level.

b. more difficult to interpret since the adjusted R-squares in models E through G are higher with the different independent variables than in model A.

c. still significant, even at the 1% level, but the sign of the relationship has switched to being positively related to the burglary rate.

d. still negatively related to the burglary rate, and still significant even at the 1% level.

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In looking at models E and G, consistent with theory and logic, the burglary rate is negatively related to average rainfall and positively related to the number of sunny days.

Select one:

True

False

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The state of West Dakota currently has per capita income of $35,500, average annual temperature of 43.0 degrees, median age of 37.5 years, and 48.0% (or 0.48) of the population in a metro area. The state suddenly experiences an improvement in its economy, and increases its per capita income to $38,600. This improvement will likely be associated with the burglary rate changing from about 519 annual burgles to about ___ annual burgles (round your answer to the nearest whole number, no decimals).

Answer:

Table 5, Crime in the United States by State, Rate per 100,000 inhabitants										Non-crime variables; units are as specified in row 2 descriptors
State	Violent crime	Murder and nonnegligent manslaughter	Robbery	Aggravated assault	Property crime	Burglary	Larceny-theft	Motor vehicle theft		Total state population	Per capita disposable personal income, $	unemployment rate, %	average annual rainfall, inches	average annual temperature, degrees F	# of sunny days per year	% below poverty line	% 18-24/18-64, % of the 18-to-64 year old population that is a young adult (18-24 years old)	% male 18-24/18-64, % of the 18-to-64 year old male population that is a young adult male (18-24 years old)	% female 18-24/18-64, % of the 18-to-64 year old female population that is a young adult female (18-24 years old)	Median age	Median age, Male	Median age, Female	% 25 years old and older with a bachelor's degree	% of state population living in a metropolitan area	% living in same house one year ago
State	Violent	Murder	Robbery	Assault	PropCrime	Burglary	Larceny	CarTheft		Population	PerCapIncome	%Unemploy	Rain	Temp	Sun	%Poverty	%young	%youngmale	%youngfemale	MedianAge	MedAgeMale	MedAgeFemale	%Bachelors	%Metro	%SameHouse
ALABAMA	430.8	7.2	96.2	285.2	3,351.3	877.8	2,254.8	218.7		4,833,722	32448	7.2	58.3	62.8	99	16.2	16.2%	16.7%	15.8%	38.4	36.9	39.8	23.5%	76.0%	85.5
ARIZONA	416.5	5.4	101.1	263.9	3,399.1	732.4	2,403.5	263.2		6,626,624	32997	7.7	13.6	60.3	193	16.1	16.7%	17.3%	16.1%	36.8	35.5	38.1	27.4%	94.8%	81.7
ARKANSAS	460.3	5.4	76.3	330.5	3,602.6	1,030.1	2,380.6	191.9		2,959,373	32509	7.2	50.6	60.4	123	16	16.2%	16.6%	15.8%	37.7	36.3	39	20.6%	61.1%	84.2
CALIFORNIA	402.1	4.6	139.9	232.3	2,658.1	605.4	1,621.5	431.2		38,332,521	41932	8.9	22.2	59.4	146	13.9	16.4%	16.9%	16.0%	35.8	34.6	37	31.0%	97.8%	85.7
COLORADO	308.0	3.4	59.8	189.1	2,658.5	476.1	1,944.5	237.9		5,268,367	41137	6.9	15.9	45.1	136	8.5	15.0%	15.5%	14.6%	36.5	35.5	37.5	37.8%	87.0%	81.1
CONNECTICUT	262.5	2.4	98.2	135.4	1,974.1	358.5	1,442.6	173.0		3,596,080	53842	7.8	50.3	49	82	9.8	15.3%	16.0%	14.6%	40.6	38.8	42.1	37.2%	94.8%	87.8
DELAWARE	491.4	4.2	132.4	313.7	3,065.5	662.3	2,259.4	143.9		925,749	38891	6.7	45.7	55.3	97	11.6	16.0%	16.5%	15.6%	39.5	37.9	41	29.8%	100.0%	86.7
FLORIDA	470.4	5.0	118.7	312.3	3,105.3	710.5	2,216.3	178.6		19,552,860	36601	7.2	54.5	70.7	101	13	15.0%	15.6%	14.5%	41.5	40	42.9	27.2%	96.4%	83.9
GEORGIA	365.7	5.6	125.0	209.3	3,346.6	823.2	2,254.9	268.5		9,992,167	33111	8.2	50.7	63.5	112	15.4	16.0%	16.7%	15.4%	36	34.7	37.3	28.3%	82.3%	84
IDAHO	217.0	1.7	13.6	161.0	1,864.3	411.9	1,357.1	95.3		1,612,136	32348	6.1	18.9	44.4	120	11.1	16.3%	16.6%	16.0%	35.4	34.6	36.2	26.2%	66.2%	82.5
ILLINOIS	380.2	5.5	137.6	204.0	2,274.3	452.1	1,659.8	162.5		12,882,135	40584	9	39.2	51.8	95	12.1	15.5%	15.9%	15.0%	37.2	35.9	38.6	32.1%	88.3%	86.7
INDIANA	357.4	5.4	108.2	211.3	2,854.0	653.0	1,984.9	216.2		6,570,902	35171	7.6	41.7	51.7	88	11.8	16.5%	16.9%	16.1%	37.3	36	38.6	23.8%	77.6%	84.9
IOWA	271.4	1.4	30.4	204.6	2,193.9	513.5	1,543.0	137.4		3,090,416	38702	4.7	34	47.8	105	9.8	17.0%	17.4%	16.7%	38	36.6	39.4	26.4%	58.3%	84.6
KANSAS	339.9	3.9	46.6	248.1	2,946.8	600.4	2,117.0	229.5		2,893,957	41139	5.3	28.9	54.3	128	11.2	17.0%	17.6%	16.4%	36.1	34.8	37.4	31.1%	67.1%	83.5
KENTUCKY	209.8	3.8	73.9	95.5	2,362.9	596.4	1,629.3	137.2		4,395,295	31960	8	48.9	55.6	93	15.2	15.7%	16.1%	15.2%	38.4	37	39.8	22.6%	58.2%	84.5
LOUISIANA	518.5	10.8	119.9	352.8	3,582.0	890.4	2,493.6	198.0		4,625,470	36241	6.7	60.1	66.4	101	20.2	16.3%	16.7%	16.0%	36.1	34.8	37.4	22.5%	83.4%	86.5
MAINE	129.3	1.8	25.2	68.7	2,292.2	488.1	1,735.3	68.8		1,328,302	35617	6.7	42.2	41	101	12.7	13.8%	14.3%	13.3%	43.8	42.6	45	28.2%	58.8%	86.3
MARYLAND	473.8	6.4	169.5	272.0	2,663.5	538.9	1,898.3	226.3		5,928,814	45652	6.6	44.5	54.2	105	7.1	14.9%	15.6%	14.2%	38.3	36.6	39.8	37.4%	97.4%	86.5
MASSACHUSETTS	413.4	2.0	100.2	270.5	2,051.2	459.2	1,455.7	136.3		6,692,824	48472	6.7	47.7	47.9	98	9.6	16.1%	16.5%	15.8%	39.3	37.8	40.8	40.3%	98.5%	87.1
MICHIGAN	449.9	6.4	102.1	274.8	2,327.6	569.4	1,510.0	248.3		9,895,622	34853	8.8	32.8	44.4	71	11.1	16.5%	16.9%	16.0%	39.5	38.1	40.9	26.9%	81.8%	85.3
MINNESOTA	234.4	2.1	67.8	127.5	2,420.4	419.0	1,854.4	147.0		5,420,380	40924	5	27.3	41.2	95	8.7	15.0%	15.2%	14.8%	37.7	36.6	38.7	33.5%	77.2%	85.5
MISSISSIPPI	274.6	6.5	80.5	156.5	2,724.7	835.6	1,742.3	146.7		2,991,207	30608	8.5	59	63.4	111	21.1	17.1%	17.7%	16.6%	36.5	35	37.9	20.4%	45.5%	86.1
MISSOURI	433.4	6.1	90.7	298.7	3,137.0	643.0	2,223.9	270.1		6,044,171	35625	6.7	42.2	54.5	115	13	16.0%	16.4%	15.5%	38.2	36.7	39.6	27.0%	74.3%	84
MONTANA	252.9	2.2	20.1	190.2	2,556.5	400.3	1,974.0	182.2		1,015,165	35379	5.4	15.3	42.7	82	11.7	15.9%	16.5%	15.3%	39.9	38.7	41.1	29.0%	35.4%	83.5
NEBRASKA	262.1	3.1	55.7	160.5	2,623.4	476.3	1,908.2	238.9		1,868,516	41135	3.8	23.6	48.8	117	9.6	16.5%	16.8%	16.2%	36.2	35.1	37.4	29.4%	64.2%	83.9
NEVADA	603.0	5.8	185.8	360.6	2,837.7	826.0	1,653.4	358.3		2,790,136	34855	9.6	9.5	49.9	158	10.1	14.5%	14.6%	14.3%	37.3	36.7	37.9	22.5%	90.3%	79.7
NEW HAMPSHIRE	215.3	1.7	49.0	112.7	2,194.3	373.0	1,750.3	71.0		1,323,459	46653	5.1	43.4	43.8	90	6.4	15.1%	15.4%	14.8%	42.4	41.4	43.3	34.6%	62.5%	85.9
NEW JERSEY	288.5	4.5	135.8	135.6	1,882.8	403.1	1,325.2	154.5		8,899,339	47893	8.2	47.1	52.7	94	9.4	14.3%	14.9%	13.6%	39.4	37.8	41	36.6%	100.0%	90.1
NEW MEXICO	613.0	6.0	86.8	449.9	3,704.8	1,029.9	2,391.8	283.2		2,085,287	31574	6.9	14.6	53.4	167	17.8	16.5%	17.1%	15.9%	36.9	35.5	38.4	26.4%	66.7%	85.9
NEW YORK	393.7	3.3	138.6	234.7	1,824.8	287.2	1,458.8	78.8		19,651,127	45620	7	41.8	45.4	63	11.9	15.8%	16.3%	15.3%	38.2	36.6	39.8	34.1%	92.9%	89.3
NORTH CAROLINA	342.2	4.8	94.9	218.4	3,128.0	921.0	2,058.7	148.3		9,848,060	33682	8	50.3	59	109	13.6	15.9%	16.8%	15.1%	38.1	36.6	39.5	28.4%	77.6%	84.7
NORTH DAKOTA	270.1	2.2	22.4	199.9	2,094.0	405.6	1,492.7	195.7		723,393	49385	2.9	17.8	40.4	93	11.1	19.8%	20.3%	19.3%	35.3	34.2	36.7	27.1%	49.2%	82.3
OHIO	286.2	3.9	124.2	123.2	2,927.5	790.2	1,968.5	168.8		11,570,808	36175	7.4	39.1	50.7	72	13.7	15.6%	16.0%	15.2%	39.2	37.7	40.7	26.1%	79.5%	85.1
OKLAHOMA	441.2	5.1	78.7	300.8	3,273.7	866.1	2,116.4	291.2		3,850,568	38648	5.3	36.5	59.6	139	14.6	16.7%	17.1%	16.2%	36.2	34.9	37.5	23.8%	66.7%	82.7
OREGON	254.0	2.0	61.0	142.7	3,173.9	528.5	2,394.5	250.9		3,930,065	34606	7.9	27.4	48.4	68	11.8	14.8%	15.1%	14.5%	38.9	37.9	40.1	30.7%	83.4%	81.9
PENNSYLVANIA	335.4	4.7	115.6	185.7	2,060.8	407.3	1,545.6	107.8		12,773,801	40849	7.3	42.9	48.8	87	11.1	15.8%	16.1%	15.4%	40.6	38.9	42.1	28.7%	88.3%	87.8
RHODE ISLAND	257.2	2.9	65.0	147.4	2,442.0	533.2	1,696.4	212.4		1,051,511	41149	9.3	47.9	50.1	98	11.4	17.5%	17.8%	17.3%	39.9	38.1	41.6	32.4%	100.0%	86.5
SOUTH CAROLINA	508.5	6.2	83.2	373.6	3,624.2	857.8	2,502.9	263.5		4,774,839	31843	7.6	49.8	62.4	115	14.1	16.4%	17.2%	15.6%	38.6	37	40.1	26.1%	84.2%	84.9
SOUTH DAKOTA	316.5	2.4	18.8	236.2	1,914.7	399.1	1,404.6	111.0		844,877	40864	3.8	20.1	45.2	104	14.5	16.6%	16.9%	16.3%	36.7	35.6	37.9	26.6%	47.3%	83.3
TENNESSEE	590.6	5.0	112.5	436.9	3,180.9	785.1	2,213.7	182.1		6,495,978	35690	7.7	54.2	57.6	102	14.9	15.5%	16.0%	15.1%	38.5	37.1	39.8	24.8%	77.1%	84.9
TEXAS	408.3	4.3	120.2	246.9	3,258.2	721.8	2,287.8	248.6		26,448,193	39023	6.3	28.9	64.8	135	13.8	16.5%	17.0%	15.9%	34	33.1	35	27.5%	88.6%	83.5
UTAH	224.0	1.7	42.8	130.4	2,950.4	459.6	2,233.4	257.3		2,900,872	32206	4.6	12.2	48.6	125	8.6	19.4%	19.4%	19.3%	30.1	29.6	30.6	31.3%	89.3%	82.9
VERMONT	121.1	1.6	11.6	87.1	2,214.2	528.7	1,632.2	53.3		626,630	41062	4.4	42.7	42.9	58	9.6	16.7%	17.4%	16.1%	42.4	41.1	43.5	35.7%	34.3%	86.4
VIRGINIA	196.2	3.8	55.3	109.7	2,065.9	322.5	1,640.1	103.3		8,260,405	42480	5.7	44.3	55.1	100	11.4	15.5%	16.1%	15.0%	37.7	36.4	39	36.1%	87.2%	84.3
WASHINGTON	289.1	2.3	83.5	166.4	3,710.3	837.0	2,465.9	407.4		6,971,406	43003	7	38.4	48.3	58	11	15.0%	15.5%	14.6%	37.6	36.5	38.6	32.7%	89.8%	82.5
WEST VIRGINIA	300.3	3.3	35.1	226.7	2,103.9	521.7	1,478.9	103.3		1,854,304	31328	6.8	45.2	51.8	60	18	15.0%	15.4%	14.6%	41.8	40.5	43	18.9%	61.4%	88.2
WISCONSIN	277.9	2.8	84.2	161.6	2,188.7	424.0	1,636.0	128.6		5,742,713	37876	6.7	32.6	43.1	89	10.7	15.7%	15.9%	15.5%	38.9	37.8	40.1	27.7%	73.8%	85.6
WYOMING	205.1	2.9	12.9	157.2	2,198.4	335.5	1,763.6	99.2		582,658	46876	4.7	12.9	42	114	10.9	16.0%	16.4%	15.7%	36.8	36.1	37.6	26.6%	30.4%	81.6