Here is the information that is needed for this work:
regression summary , coefficient table and F-test
SUMMARY OUTPUT | |||||
Regression Statistics | |||||
Multiple R | 0.585123024 | ||||
R Square | 0.342368953 | ||||
Adjusted R Square | 0.299479971 | ||||
Standard Error | 5.412552887 | ||||
Observations | 50 | ||||
ANOVA | |||||
df | SS | MS | F | Significance F | |
Regression | 3 | 701.5751592 | 233.8583864 | 7.982678579 | 0.00021757 |
Residual | 46 | 1347.603523 | 29.29572876 | ||
Total | 49 | 2049.178682 | |||
Coefficients | Standard Error | t Stat | P-value | Lower 95% | |
Intercept | 7.872986591 | 4.087193427 | 1.926257401 | 0.06026464 | -0.354107071 |
EDUC | 1.437060506 | 0.338639743 | 4.243626263 | 0.000105402 | 0.755414058 |
EXPER | 0.448282229 | 0.141867317 | 3.159869648 | 0.002789784 | 0.162718131 |
AGE | -0.011386246 | 0.083422321 | -0.1364892 | 0.89203017 | -0.179306669 |
wage^ = 7.873 + 1.4371 Educ + 0.4483 Exper -0.0114 Age
yes, all signs are as expected
wage is positively correlated with Education and experience
and is negatively correlated with age
coefficient of Education is 1.4371
as education increases by 1 unit, on average wage will increase by
1.45 units
coefficient of detemrination = 0.3427
hence 34.27% of variation in wage is explained by this model
Here is the information that is needed for this work: A researcher interviews 50 employees of a large manufacturer and colects data on each worker's hourly wage (Wage), years of higher education...
A researcher interviews 50 employees of a large manufacturer and collects data on each worker’s hourly wage (Wage), years of higher education (EDUC), experience (EXPER), and age (AGE). Wage EDUC EXPER AGE Male 37.85 11 2 40 1 21.72 4 1 39 0 ⋮ ⋮ ⋮ ⋮ ⋮ 24.18 8 11 64 0 A researcher interviews 50 employees of a large manufacturer and collects data on each worker's hourly wage (Wage), years of higher education (EDUC), experience (EXPER), and age...
Using data from 50 workers, a researcher estimates Wage = β0 + β1Education + β2Experience + β3Age + ε, where Wage is the hourly wage rate and Education, Experience, and Age are the years of higher education, the years of experience, and the age of the worker, respectively. The regression results are shown in the following table. Coefficients Standard Error t Stat p-Value Intercept 7.17 4.26 1.68 0.0991 Education 1.81 0.35 5.17 0.0000 Experience 0.45 0.10 4.50 0.0000 Age −0.01...
Using data from 50 workers, a researcher estimates Wage = β0 + β1Education + β2Experience + β3Age + ε, where Wage is the hourly wage rate and Education, Experience, and Age are the years of higher education, the years of experience, and the age of the worker, respectively. The regression results are shown in the following table. Coefficients Standard Error t Stat p-Value Intercept 7.73 3.94 1.96 0.0558 Education 1.15 0.39 2.95 0.0050 Experience 0.45 0.11 4.09 0.0002 Age −0.03...
Using data from 50 workers, a researcher estimates Wage = β0 + β1Education + β2Experience + β3Age + ε, where Wage is the hourly wage rate and Education, Experience, and Age are the years of higher education, the years of experience, and the age of the worker, respectively. The regression results are shown in the following table. Coefficients Standard Error t Stat p-Value Intercept 8.23 4.40 1.87 0.0678 Education 1.23 0.38 3.24 0.0022 Experience 0.53 0.18 2.94 0.0051 Age −0.08...
2 Using data from 50 workers, a researcher estimates Wage BoIEducation + 2Experience B3Age E, where Wage is the hourly wage rate and Education, Experience, and Age are the years of higher education, the years of experience, and the age of the worker respectively. The regression results are shown in the following table. 10 points Standard Coefficients t Stat P-Value 0.1310 0.0003 0.0022 Error 4.24 Intercept Education Experience Age 6.52 1.32 1.54 0.34 0.12 3.88 3.25 -0.20 0.39 0.01 0.05...
Using data from 50 workers, a researcher estimates Wage = ?0 + ?1Education + ?2Experience + ?3Age + ?, where Wage is the hourly wage rate and Education, Experience, and Age are the years of higher education, the years of experience, and the age of the worker, respectively. The regression results are shown in the following table. Coefficients Standard Error t Stat p-Value Intercept 7.58 4.42 1.71 0.0931 Education 1.68 0.37 4.54 0.0000 Experience 0.35 0.18 1.94 0.0580 Age ?0.06...
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2. The following data were collected last semester on ten students. Complete a multiple regression analysis in which you use AGE (A), MATH PROFICIENCY (MP) (on a 1 –10 scale), and GENDER (G) (0 = male, 1 = female) as predictors of FINAL EXAM (FE) performance. Do this analysis in SPSS and then answer the following questions. Subject # A MP G FE 1 35 8 1 90 2 31 6 0 88 3 26 5 1 84 4 33...
1. Use Minitab to get the same type of output (including graphs) with the following Income/Education data as the little data set discussed in class. Do the regression of Income on Education. Interpret the meaning of the coefficient of Education. What do you predict income to be for a person with 17 years of education? Why do you think Income for 21 years of Education is lower than Income for 19 years of Education in the data set? Education in...
20. A social scientist would like to analyze the relationship between educational attainment (in years of higher education) and annual salary (in $1,000s). He collects data on 20 individuals. A portion of the data is as follows: Salary Education 35 1 67 6 ⋮ ⋮ 32 0 a. Find the sample regression equation for the model: Salary = β0 + β1Education + ε. (Round answers to 2 decimal places.) Salaryˆ=Salary^= + Education b. Interpret the coefficient for Education. As Education...