Only question 6 please, this is the model referred to in
Question 6 from 5.c


6.
Sol:

Unemployment rate = 0.3364*wage + 0.8455
A.
For a minimum wage of $10.
Predicted unemployment rate = 0.3364*10 + 0.8455
Predicted unemployment rate = 4.21%
Actual unemployment rate in Colorado = 3%
So, predicted unemployment rate in Colorado is overstated by 1.21%
B.
For a minimum wage of $15.
Predicted unemployment rate = 0.3364*15 + 0.8455 = 5.89%
Change in predicted unemployment rate = 5.89%-4.21% = 1.68%
C.
| State | 2018 Minimum wage ($) | Unemployment rate % | Predicted unemployment rate (%) | Prediction error |
| Alabama | 7 | 4 | 3.20 | 0.80 |
| Arizona | 11 | 5 | 4.55 | 0.45 |
| Arkansas | 8 | 3 | 3.54 | -0.54 |
| California | 11 | 4 | 4.55 | -0.55 |
| DC | 13 | 6 | 5.22 | 0.78 |
D.
R Square = 45.26%
The minimum wage can only explain 45.26% of the change in unemployment rate.
C) Estimate the linear model for a state's unemployment rate shown below (i.e. estimate Bo and β1...
5. Use the unemployment and minimum wage data above to answer this question. a) What is the predicted unemployment rate for a state with a minimum wage of 10 dollars? How does this compare to the actual unemployment rate in Colorado? If a state increases its minimum wage from 10 dollars to 15 dollars, what is the predicted change in the unemployment rate? b) c) Compute the predicted unemployment rate and the prediction error for each state. d) Compute the...
ys 70 eoni idence interval for μο 4. The following table contains the minimum wage and unemployment rates for a sample of eight states. State 2018 Unemployment | Minimum | Rate (96) Wage Alabama Arizona Arkansas S7 $11 $8 California $11 4 Colorado $10 Connecticut $10 $8 $13 Delaware DC 4 Graph unemployment and minimum wage, with unemployment on the vertical axis. Draw a line that best fits the points and label the intercept and slope. You can use your...
The homeownership rate in the US. was 61.4% in 2009, In order to determine if homeownership is linked with income, 2009 state-level data on the homeownership rate (Ownership in %) and median household income (income in $) were collected. A portion of the data is shown in the accompanying table State Alabama Alaska ownership Income 36,000 57,624 66.7 62.5 Wyoming 68.2 48,490 XC a-1. Estimate the model Ownership = β0 + β1Income + ε. (Negative values should be indicated by...
Suppose you estimate the following model by OLS: wage = β0 + β1educ + β2exper + u wage : hourly age in dollars educ : years of education exper : years of experience You obtain the following fitted model using STATA, where standard errors are given in parenthesis wage [ = 3.5 + 0.9educ + 1.5exper (2.0) (0.7) (0.5) Number obs. : 523 R 2 = 0.45 For the following questions, make use to the relevant statistical tables. If you...
QUESTION 1 Consider the following OLS regression line (or sample regression function): wage =-2.10+ 0.50 educ (1), where wage is hourly wage, measured in dollars, and educ years of formal education. According to (1), a person with no education has a predicted hourly wage of [wagehat] dollars. (NOTE: Write your answer in number format, with 2 decimal places of precision level; do not write your answer as a fraction. Add a leading minus sign symbol, a leading zero and trailing...
12. Which
coefficient(s) is (are) significant at the .05
level?
Select one:
a. Income, Unemployment and Pupil/Tea
b. Divorce
c. Income and Pupil/Tea
d. Income
PART B-- Refer to Figure 2
Is the regression equation significant at the .05
level?
Select one:
a. YES
b. NO
c. Need more information to answer this question
PART C-- Refer to Figure 2
Which of the following is a correct interpretation for
the variable Unem?
Select one:
a. Unemployed people steal 40.63 more...
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Consider a multiple linear regression model Y; = Bo + B1Xi1 + B22:2 + 33213 + Blog(x14) + Ej. We have the following statistics for the regression Call: 1m formula = y “ x1 + x2 + x3 + log(x4) Coefficients: Estimate Std. Error t value Pr(>1t|) (Intercept) 154.1928 194.9062 0.791 0.432938 x1 -4.2280 2.0301 -2.083 0.042873 * x2 -6.1353 2.1936 -2.797 0.007508 ** x3 0.4719 0.1285 3.672 0.000626 *** x4 26.7552 9.3374 2.865 0.006259 ** Signif. codes: O '***'...
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