The first thing done when using the mini-tab program is to put all your data into the columns. Once you have done that you can start to look at your regression analysis. Because I inserted the data by hand, the columns are as follows: C1: Annual salary in dollars, C2: Experience in years, C3: Gender, C4: Age (years), C5: Training level (A=1, B=2, C=3). From the regression analysis the first thing we get is B1. The B1 value is located on the computer print out under the coefficient column and the horizontal col

umn C2. The B1 value tells us that for every unit change in X, it will contribute to the Y by 1833.9 units. Since we have a positive slope, a one-unit increase in X will lead to an increase in Y by 1833.9 units. After we find out what B1 is we then can find out what B0 is which is also located under the coefficient column horizontally in the constant column. This value is 34,620 which to us means that when X is zero then Y(hat)=34620. The fundamental equation for our regression analysis is Y(hat)= 34620+ 1833.9X. From this equation we can now predict what our annual salary will be using a value of X or the years of experience.

So in conclusion, through running this test on mini-tab and printing out the graphs, we are able to see that there is in fact a positive linear relationship between annual salary and years experience with the company. In this report I put parts 2 and 3 together to make it flow better. I found that for using a Simple Linear Regression model it is quite easy to see this relationship and it is something that you would expect to see in a work environment. In the real world usually it takes years of experience and training to get a high salary. The more schooling and experience you have the higher the salary you would typically find for that specific position which is being held. However, in this project there are different areas that significantly affect one’s salary.

Two other important values that are found on the print out are the R-squared and R-value. The R-squared value is located under R-Sq on the print out and this equals 30.3%. This represents the measure of the proportion of variation in Y that is explained by the independent var

Some topics in this essay:
Slope B1, Regression Analysis, R-squared=SSR/SST Square, Sum Squared, B1 B0, Linear Regression, Squared Regression, R-value R-squared, Squared Error, C2 B1, linear relationship, sum squared, regression analysis, annual salary, value located, regression model, simple linear regression, coefficient column, independent variable, b1 value, experience company, sum squared regression, sum squared error, sum squared total, column vertically horizontally,