Welcome everyone in this video. I'm going to show you how you can run multiple regression model in Minitab software and then how you can get variance inflation factor or we are a factor for each variable where your factors are used for detecting multicollinearity in your explanatory variable so the data. I'm going to use have 16 variables and these 16 variables is the woman hours worked and then there are 15 other explanatory or independent variable so. I'm going to regress women. Hours worked on 15 explanatory variables and these variables include a number of socio-economic variables associated to women these include the education of women parents like mother education or further education or the woman has been education or how many kids they have in house or or their household income so the model. I have is shown here in this variable in this model I have women. Hours worked is being regressed. On age of the woman woman education we are in here then women experience in here and family income then further education then husband age spend education then husband hours worked and husband wage then kids less than six years and kids between 6 and 18 years and wage of the woman herself and then the mother education then the marginal tax rate of the country in which the woman is working and finally the unemployment rate in that particular country and in the area so. I'm going to estimate this model in mini tapes software and I'm going to discuss the output. We did we get it so first of all we have to take the data to Minitab and then we have to run the multiple regression model and the steps. I am going to follow will automatically ensure that we get VI here for various inflation factors with that as well so let me switch to Minitab software and let me take the data there. Oh this is the excel sheet. I have all the data here. I'm going to select all the data for all the variables I am going to copy it and then I am going to take it to the Minitab software so I selected all the data on all the variable then.

I switch to a minute here I am going to paste it in cell one and it will automatically get pasted to all the cells as it was in. Excel then. I'm going to take the names of these variables because the heater by Dave old. Give them the name. C 1 C 2 C 3 s per the column title so. I'm going to adjust my screen so that I can see the names and then I tell type in directly from the excel so the first variable was ours. Oh it was type is ours then hey. H 10 education then experience then family income then it's further education. Then husband age husband education then husband hours worked per year then her husband wage then the kids less than 6 year of older than kids between 6 and 18 years in the family then the wage of the woman mother education then the marginal tax rate and finally the unemployment so all the 15 variables and the dependent variable has been headed to mini dip. So let me expand my hair windows now. I'm going to do the multiple regression model from that so for this after adjusting my windows so I go to the tab States then I select regression then again the regression and then in that I select the regression analysis and then in the dependent variable or the response variable. Is the first box here I will click in the box and then I will double click the powers and I will take the variable similarly I will click in the continuous box and I then can double click on the names of each variable. It can take it to that box of the continuous variables or the continuous productors after having that I can select some of the outputs I want. I can select whichever outputs I want or I can uncheck for example. I don't want the rewards any statuses for one check it when I click. OK then ok again we get all the results now. This session window shows me the entire results of the hour of the regression model which I click take marked in the result window. You can see here. The model fit with your are adjusted r-square values and then the and then the coefficients their p-values and then their vif values and you can see that we are.

Your values are with with different different values and from the theory. We from the theory. We know that as shown here in the in the table as well as the way of value increases it shows the existence of stronger and stronger correlation among the explanatory variable so the value here are given. I arrange these vif values in the descending order so that I can identify the edge weight. Variables are strongly correlated. Thank you for your patience if you liked this video please subscribe our Channel and if you have any questions feel free to email or comment. We will try to respond thank you.