 Jo interprating the output of the two way analysis of variants that we got through SPSS. But before I interpret each table and explain it to you again, remember... More general assumptions have to be made. Assumptions means that we run tests on certain assumptions. In other words, ANOVA is a very robust test. अप्रड़दी डानबार नादारी आपको आपको जेन्ल अप्रड़़न समझनके इसारी आपको रश्वार करनी चाही आप पताभी हूना जाए अन भी आपको लगाटी रान बार के हैं. इसारी आपको रन भी करनी चाहीं और पता भी होना चाहीं न वी हो आल्ड़ी डान्बार। तो हमारे पस चब रन की आप तो पहली इजम्छन हमारी होमोजनाइती अप वेर्यन्स आप ने उसको ख्लिक किया था न पहला तेबल वो तेपा है वो लिविन्स टेस्ट पो वो मोजनाइती अप वेर्यन्स का देता है. तो भी वोग़ा वो तो वो लोग़ा वाली नाईता है. अगर हमारी नाँईँगद किए देगे, हमने का देखने है, हमने ये sleep साइजी के कालिम में लेएयो देखनी है, Anside b1 आगर वोगेंट किभाँ करी. आप आप की सहुलत के लिए मैंने सारे टेबवस लिखके उन्की दिस्क्रिष्चन देदी है के what we would be looking in each table, so this is for your record and for preparation and for understanding, कुछ भी बूले हर चीस को explain मैंने कर दिया इसकेंदर के, कुँँँँँँँँँँँँँँँँँँँँँँँँँँँँँँँँँँँँ� तो हमारी टेस्ट्सेतंटीक सिंदर सम् Stewart Strict to show many different more than 0.05. So, our homogeneity of variance assumptions meet. So, this is the main table for two way analysis of variance, where we have both independent variable treatment and for gender, it gives you any effects and also its interaction effects. It is a treatment into gender. This is the interaction of both the variables. So, what you will see for treatment is the main effect of the first independent variable on the depression. Our sum of square is 64000. Degrees of freedom is 1. As we have two treatments, 2 minus 1 will be 1. So, mean square is 64000. Mean square is always sum of square divided by degrees of freedom is 64000. Similarly, for gender, your sum of square is 0, 0 is given, 1 is given, we will divide it by 0. And when we have to take out its mean square, to take out f value, we will divide the mean square divided by error mean square. So, for treatment, the mean square is 64000 or 64.000 whereas our error mean square is 8.5. So, we will divide 64 divided by 8.5. So, we will get f value, which is 7.52. So, f value is equal to mean square for the independent variable 1 divided by the mean square of error. That is, we call this error within variance and for treatment, the mean square of independent variable is called between group variance. Similarly, we will take out f value, which is 0. And for interaction, we will multiply it by 1 and divide it by f value. Similarly, we will take out 64 divided by 8.5 will be f value for the interaction. So, what you have to see in this table is your s-i-g, i.e., p value. If this value is less than 0.5, it means that your significant predictor or significant difference is there. So, for the treatment, s-i-g or p value is 0.018, which is less than 0.05. It means that there is significant difference of treatment on the depression score. i.e., the main effect for the treatment is your significant to make the depression more successful. Similarly, if you have gender, then your significant value is 1 i.e., the significant effect is not there. i.e., there is no effect of gender but if you look at the treatment and gender, then amazingly, this is significant, 0.018. Again, it is less than 0.05, which means that there is a significant effect of treatment and gender interaction. What does this mean? It means that both your variables are combined and are affecting the main and variable. So, we know more about interaction from plots or charts. But now we know that we have the main effect for treatment is significant. We have the main effect for interaction is significant. But we do not have the main effect for the gender. After that, our estimated marginal means is given for gender and for interaction. So, now we will see how we interpret plots especially finding the unique effect of a combined effect of the two variables.