The Equal Opportunity Commission is investigating questions around unequal pay rates and discriminatory remuneration in various industries. The Pay Equity tab in the excel workbook contains information on 100 employees from a particular industry. Information includes: Salary ($) Gender Years of Education Years of Experience Division of employment Age Run a multiple regression analysis looking at the relationship between salary and years of education and years of experience. What proportion of variation in salary can be explained by these two variables? Conduct a test of the overall significance of the model. Test both the Education and Experience variables separately. Do both contribute to explaining the variation in salaries? Write out the estimated equation and interpret all coefficients. Salary (in $) Education (years) Experience (years) 20860 11 4 30200 16 1 31240 12 1 36860 12 8 44760 14 4 46690 19 3 47400 15 1 47880 14 3 50620 13 6 50690 14 4 56590 15 4 57040 15 1 57100 14 5 57960 16 2 61260 16 6 62260 12 12 63330 12 8 64540 16 6 66380 15 10 67670 14 22 67920 16 10 68620 17 12 68670 14 9 68690 15 13 69640 15 5 70030 18 12 70160 18 15 74810 13 21 75490 15 10 76820 15 19 77030 12 10 77290 12 10 79100 16 10 79290 14 10 80090 15 7 80320 15 19 80520 16 8 81640 15 12 81760 15 32 82100 16 9 82430 17 12 83160 15 11 83430 14 16 84340 13 20 85470 15 6 86010 14 16 87320 16 12 87920 15 16 88350 17 2 89050 16 6 89150 13 9 90070 16 11 90990 15 18 91450 13 20 91560 16 10 91850 11 22 92630 15 12 92720 15 10 92840 14 19 93090 14 18 93390 17 10 93830 17 9 94470 14 20 97440 14 12 97480 17 14 97790 16 20 98050 16 13 98940 15 10 99090 16 25 99680 15 18 99710 14 18 100250 16 16 100930 16 8 101400 12 22 101750 17 6 105750 14 22 106830 20 18 107570 17 29 109360 17 28 109680 18 16 110660 15 21 113920 15 17 114970 17 10 115500 14 20 116930 17 16 119470 17 18 120580 15 31 122940 16 30 125360 15 21 126750 16 30 129790 17 21 131560 17 32 132010 17 20 134540 16 23 139260 15 21 142760 18 10 145110 13 19 158960 17 35 165400 16 30 175760 15 24

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Correlation

Correlation defines a relationship between two independent variables. It tells the degree to which variables move in relation to each other. When two sets of data are related to each other, there is a correlation between them.

Linear Correlation

A correlation is used to determine the relationships between numerical and categorical variables. In other words, it is an indicator of how things are connected to one another. The correlation analysis is the study of how variables are related. 

Regression Analysis

Regression analysis is a statistical method in which it estimates the relationship between a dependent variable and one or more independent variable. In simple terms dependent variable is called as outcome variable and independent variable is called as predictors. Regression analysis is one of the methods to find the trends in data. The independent variable used in Regression analysis is named Predictor variable. It offers data of an associated dependent variable regarding a particular outcome.