16285 ratio shows the weight of the variable X to Y. That is, the value of the analyzed parameter is influenced by other factors, which has not been described in the model. 1428 ratio shows how will be Y, if all the variables in the model will be equal to 0. 5 (such an analysis can hardly be considered reasonable). The higher the coefficient of determination, the better is the model. 5% explains the addiction between the parameters whixh are studied. This means that the model parameters estimated at 75. R-square – is the coefficient of the determination.

After you click OK, the program will display the calculations on a new page (you can choose the interval to be displayed on the current page or assign to the output to a new book).įirstly of all pay attention to the R-squared and the ratios.In the fields for the specify range of the input data, which describes the options (Y) and influence the factor (X). Open the menu for selecting the input values and output parameters (which display the result).Select the «Analysis ToolPak» and click OK.Īfter activating the superstructure will be available on the «DATA» tab. Below the drop-down list in the «Manage:» field is the inscription «Excel Add-Ins» (if it is not, click on the checkbox to the right and select).

Push the button «FILE»-«Options»-«Add-Ins».

But faster it will make the add-on «analysis package». In Excel, there are built-in features with which you can calculate the parameters of the linear regression model. In our example as Y serves the indicator of employees who retired. Where a – are the regression coefficients, x – the influencing variables, k – the number of factors. The linear regression model is as follows: It is necessary to determine the dependence of the number of employees who retired from the average salary. On 6 enterprises was analyzed the average monthly salary and the number of employees who retired. And based on the main factors you may to predict, to plan the development of priorities areas, to make to the management decisions.Ĭonsider the example to the construction of a regression model in Excel and the interpretation of the results. The analysis result allows you to prioritize. Or: how to influence foreign investment, energy prices, etc. For example, it depends on the number of economically active population from the number of enterprises, the value of wages and other parameters. It shows the influence of some values (independent, substantive ones) on the dependent variable.

Excel linear regression significance