 तेर इस दी किख्चम्पल हुज्टे आनलेसर्द दी अग्टर जव्गटार मेफनेश्द करनेटार कैसे उसकी वेरिन को वेरिन्स मेप्रिक्स बन रही है पकतर लोओटिंची क्या है में सब मेफारिक्कलि चक यह थ. तन Heartsap Tower दी थी नोरो असली वे בॉँँर unanim लोभ आपने �就是 पिसल होगाई �Öw भास अब ती म्मना बाप आपने भापने डाज NBC करने बापने भाछ왕्य old आए टियावो वैँ थी कोवेरीन्स मैट्रिक्स है. और हमने ये फाक।, ये सारी चीजंट शोगर ने जीवन, जीवन, जीवन, जीवन, चीवन अद जीवन वीट्गेश प्रट्दी गेवन, अद साई कि या बआद्ट साई ये आद बआद्ट साई ये वहारे पस गीवन है. इंस्टट चीजो को यूच कर के हम ने क्या करना है ये पाईट करनें आव here is the solution of the example number 1 for the factor analysis model this is the factor analysis model, you know that we have the variance covariance matrix this is for k equals to 1, so we have the one factor solution with 3 variables, so p equals to 3 we have lambda prime which is equals to lambda 11, lambda 2, 1, lambda 3, 1 so this is the factor loading this is the factor loading and this is 1, 1 means first variable with first factor solution 2, 1, second variable with first factor 3, 1, third variable with one factor क्या हम, one factor solution कर हैं now sigma variance covariance matrix lambda, lambda prime plus psi अब हम ने यहाँ पेसके कोटिं कर दिया, lambda you know that this is the lambda prime and the lambda, lambda 11, lambda 21 plus psi, so after multiplication lambda 11 square plus psi, 1, lambda 11, lambda 21, lambda 11, lambda 3, 1 because बागी हमारे पास, of diagonal values यहें, वो 0 हैं 2, 1, lambda 2, 1, into lambda 11, lambda 2, 1 into lambda 2, 1, so lambda 2, 1 square plus psi 2 because यहम ने भी प्रीवेस पी किया, this is the variance covariance matrix sigma comparing it with this वागी हमारे पास, sigma कहाँ हैं, विवाने हमें, comparing with this so again lambda 11 square plus psi 1 equals to 1 वे कि इसकी वालि बागी हमारे पास के लिस दिया, this is the 1 1, 1, 2, 1, equation, this is equation number 1 1, 1, 2, 1, 0.63 तें अभी वो अप्रीविष होगा, आगे होगा, मैं असको देख लेतियों lambda 2, 1 square plus psi 2 this is 1 3 1 square plus side 3, this is equation number 3. So, we have the equation number 1, 2, 3 with values 1, 1, 1. And next, the equation number 4 is the lambda 1, 1, lambda 2, 1. So, lambda 1, 1, lambda 2, 1, the value is the 0.63. Lambda 1, 1, lambda 3, 1, 0.45, lambda 2, 1, lambda 3, 1, 0.35. The equation number 456. So, multiplying, now we need the solution for this. We need to determine the values of lambda and psi. So, multiply equation number 4 and equation 5, we get. In 2, we have multiplied it in between. So, look at this, the lambda 1, 1 and the lambda 1, 1 is the lambda 1, 1 square. Lambda 2, 1, lambda 3, 1 multiplied by 0.63 multiplied by 0.45. So, lambda 1, 1 square, now here lambda 2, 1, lambda 3, 1. Its value is, in equation 6, you can see its value and its value is 0.35. So, what we did is, lambda 2, 1, lambda 3, 1 multiplied by 0.35. And next, we have, these values are multiplying. We have that as it is multiplying. After multiplying, then with 0.35, we divide it. So, the value of lambda 1, 1 square is 0.81. So, lambda 1, 1 square which is equal to 0.81. And we need the value of lambda 1, 1. So, lambda 1, 1, we have taken its square root. So, we have the value of 0.9. Now, the value of lambda 1, 1 is 0.9. Now, put in 4. In this equation, if we enter the value of lambda 1, 1, 0.9. So, then we will get the value of lambda 2, 1. We can determine it. So, what we did is, equation 4, this is the equation number 4. lambda 2, 1's value we have determined. 0.63 divided by lambda 1, 1. And we know the value of the lambda 1, 1 which is equal to 0.9. After the division and calculation, we will get the value of the lambda 2, 1 which is equal to 0.7. Now, put the value on 6, because now we need the value of lambda 3, 1. Now, we will enter the equation 6. Equation 6, we will enter after calculation or after simplification, the lambda 3, 1 which is equal to 0.5. So, here is the value lambda 1, 1, 0.9, lambda 2, 1, 0.7, lambda 3, 1, 0.5. And here is the lambda 1, 1's square 0.1, 0.4, 9 and 0.2, 5. We have lambda's values. Now, whose values do we need? We need psi's values. What are psi's? Other variants. So, lambda 1, 1's square plus psi equals to 1. Where did we get this? We have solved the previous equation. This is equation number 1, 2 and 3. lambda 1, 1's square plus psi 1 equals to 1. Now, we need the value of psi 1. We have found the value of lambda 1, 1's square. After simplification, psi 1 which is equal to 0.19. Again, we have found the value of psi 2, 0.51. We have found the value of psi 3 which is equal to 0.75. So, we have got the matrix of psi's, 0.19, 0.51 and the 0.75. Now, psi is also determined and we have got lambda's also. So, now put in the model. This is the model. And in the model, we have lambda's which are lambda 1, 1, 2, 1, lambda 3, 1, f, 1. Because, one factor solution is there. We have used f1 plus e1, e2, e3 because we have three variables. So, x lambda 1, 1's value is integrated. lambda 2, 1's lambda 3, 1's value is centered. After simplification, what will happen? 0.9 f1 plus e1, 0.7 f1 plus e2, 0.5 f1 plus e3. This is, you have x1, x2, x3's values. Now, we have x1, which is equal to 0.9 f1 plus e1. What is this, you are interpreting? There is a strong correlation because you have 0.9, right? High correlation between the factor and the items or the variable. How much is it coming to us? Correlation, 0.9. And second, what is it coming to us? 0.7. This is also the high correlation between the factor and the variable. And 0.5, which is the moderate correlation between the factor 1 and the variable 3. This is the interpretation of the example 1. And this is the example of the factor analysis, which we can say that we can manually solve it because this is the one factor solution.