Lab11 Crim320

pdf

School

Simon Fraser University *

*We aren’t endorsed by this school

Course

320

Subject

Economics

Date

Jan 9, 2024

Type

pdf

Pages

Uploaded by cecileshen71

Question 1: Research question: Does unemployment rate predict the residential burglary rate? Question 2: Null hypothesis: The unemployment rate does not predict the residential burglary rate. Alternative hypothesis: The unemployment rate predict the residential burglary rate. Question 3: Unemployment rate is the independent variable and residential burglary rate is the dependent variable. This is because it is more likely that unemployment rate will be influencing the residential burglary rate and we might believe that the increase of unemployed people who needs money will put them in the situation to obtain money in an illegal manner. Additionally, social disorganization theory suggest that areas with high unemployment have greater

di ﬃ culty establishing social organization and the collective behaviour to repel crime. So it is reasonable for us to assume unemployment rate will increase the residential burglary rate. Question 4: Considering the coe ﬃ cient of determination, is this a good model? Use the e ff ect size ratios we used for correlation analyses in the previous lab (2 marks) The value of R-squared is 0.000. It measures how good the regression is fitted to the data. Since the R-square value is 0, it shows a weak linear relationship, and the model does not fits to the data. With a such low R-square, we are not able explaining a lot of variation in Y (residential burglary rate) with variation X (unemployment rate) . Hence, it is not a good model. The coe ﬃ cient of correlation is 0.01. The e ff ect size of this study is 0.01, since it is between 0.00-0.35, it is characterized as low magnitude e ff ect. Question 5: The regression of coe ﬃ cient: Bunemprate = -0.044, so the relationship between the unemployment rate and the residential burglary rate is negative and low magnitude. With a p- value of 0.92, which is bigger than 0.05, it is not statistically significant and we failed to reject the null hypothesis. The independent variable is not a significant predictor of the dependent variable. In the coe ﬃ cient table, the constant is 29.852, it is the y intercept which is the value of y when x is zero The slop(b)=-0.044, it is negative, indicating the direction of the correlation that is when the unemployment rate increase, the residential burglary rate decrease. Question 6: y=28.852-0.044x Question 7: Bunemprat = -0.044 The b coe ﬃ cient is negative and low magnitude, so the relationship between the unemployment rate and the residential burglary rate is negative and low magnitud. So as X in creases, Y decrease. The b coe ﬃ cient has a p-value of 0.92, shows that the independent variable is not statistically significant. Thus, we failed to reject our null hypothesis. As a result, the unemployment rate does not predict the residential burglary rate. -There is only one independent variable, so the p-value in ANOVA analysis is the same as the regression coe ﬃ cient Question 8: Mischief rate and government assistance: Q1&Q2: Research question: Does government assistance predict mischief rate? Null hypothesis: Government assistance does not predict mischief rate.

Alternative hypothesis: The government assistance predict mischief rate. Q3: The dependent variable is mischief rate and the independent variable is government assistance. We want to study whether government assistance is influencing the mischief rate. According to social disorganization theory, areas with high unemployment have greater di ﬃ culty establishing social organization. People who receive government assistance on housing are usually people who does not have any income. With the area that di ﬃ culty establishing social organization, we assume that we can expect a higher mischief rate with the government assistance. Q4: The value of R-squared is 0.007. It measures how good the regression is fitted to the data. Since the R-square value is 0.007, it shows a positive weak linear relationship, and the model does not fits to the data. Hence, it is not a good model. The coe ﬃ cient of correlation is 0.083. The e ff ect size of this study is 0.083, since it is between 0.00-0.35, it is characterized as low magnitude e ff ect. Q5: The regression coe ﬃ cient is not statistically significant with a p-value of 0.399. With the p-value = 0.399 which is bigger than 0.05, it is not statistically significant so we failed to reject the null hypothesis that government assistance does not predict mischief rate. The independent variable is not a significant predictor of the dependent variable. In the coe ﬃ cient table, the constant is 46.033, it is the y intercept which is the value of y when x is zero. The slop(b)=0.71, it is positive, indicating the direction of the correlation that is when the government assistance increase, the mischief rate decrease. Q6: y=46.033+0.71x Q7: Bgovtass = 0.71 The b coe ﬃ cient is positive low magnitude, so the relationship between the unemployment rate and the residential burglary rate is positive and low magnitude. So as X in creases, Y increases. The b coe ﬃ cient has a p-value of 0.399, shows that the independent variable is not statistically significant. So we failed to reject our null hypothesis. As a result, government assistance does not predict mischief rate.

Your preview ends here

Eager to read complete document? Join bartleby learn and gain access to the full version

Access to all documents
Unlimited textbook solutions
24/7 expert homework help

Related Documents

Advmacro hw (5).pdf

Advmacro hw (7).pdf

Advmacro hw (8).pdf

Assignment-II Data (1).xlsx

ECN129 ass 2.docx

Practice Exam1 Solution Bank Management.docx

Problem Set 6 (1).pdf

SAKA F22 BEPP 1000 Midterm.docx

ECN104 QUIZ 1 PART 5.docx

ECN104 QUIZ 1 PART 7.docx

Saka Bepp 1000 F22 Final Exam.docx

ECN104 QUIZ 1 PART 4.docx

Recommended textbooks for you

Managerial Economics: Applications, Strategies an...

Economics

ISBN:9781305506381

Author:James R. McGuigan, R. Charles Moyer, Frederick H.deB. Harris

Publisher:Cengage Learning

Exploring Economics

Economics

ISBN:9781544336329

Author:Robert L. Sexton

Publisher:SAGE Publications, Inc

Micro Economics For Today

Economics

ISBN:9781337613064

Author:Tucker, Irvin B.

Publisher:Cengage,

MACROECONOMICS FOR TODAY

Economics

ISBN:9781337613057

Author:Tucker

Publisher:CENGAGE L

Economics For Today

Economics

ISBN:9781337613040

Author:Tucker

Publisher:Cengage Learning

Survey Of Economics

Economics

ISBN:9781337111522

Author:Tucker, Irvin B.

Publisher:Cengage,

SEE MORE TEXTBOOKS

Recommended textbooks for you

Managerial Economics: Applications, Strategies an...
Economics
ISBN:9781305506381
Author:James R. McGuigan, R. Charles Moyer, Frederick H.deB. Harris
Publisher:Cengage Learning
Exploring Economics
Economics
ISBN:9781544336329
Author:Robert L. Sexton
Publisher:SAGE Publications, Inc
Micro Economics For Today
Economics
ISBN:9781337613064
Author:Tucker, Irvin B.
Publisher:Cengage,
MACROECONOMICS FOR TODAY
Economics
ISBN:9781337613057
Author:Tucker
Publisher:CENGAGE L
Economics For Today
Economics
ISBN:9781337613040
Author:Tucker
Publisher:Cengage Learning
Survey Of Economics
Economics
ISBN:9781337111522
Author:Tucker, Irvin B.
Publisher:Cengage,