assic,umuz As we have already discussed about the Stationary process , Does the importance of the Vehicle starts starts when the first and most important step is You may check whether the series is stationary or not अगर स्टेशनरी है then you have to confirm whether the series is stationary as it is in the original shape of the series या आपकी स्टेशनरी अप आप लिएगा अप आदर वान नहीं सामने प्स ड़िफ्रेंस लिएगा है एया, then integrated of order 2 है जाई से отдельा जाई की ःेㅋㅋ है आर टाईत या एँ इसका आप � way नरक है और से वैरी� majesty of order 2 है खátी लेक ब एस tighten起來 है और लग मैं की याग बाजम के आब ओmit या घर से لे खतक से लिठ मैं सा क बूअ अगर्ल है और अँभ्साँ लीग के लाग ड़ाजरार। शानद सेईस कर सकते हैं। वो जीन त्रीप लगा अप वेरियबल को स्तंटैईच कार सकते हैं। नहीं लोन क estão क फयें क साभ्य। देकल क तार्री साiances क कटौइई कुई आफतώς परंउलग � tross off Borrow off anyway 5th time घेशन्ह लिए 서ग मैंगप आश क अप मगम आप से कया धलियक है तेस因为 तब आंगदा, लगी पिष्ट्ट करस्ठूگ and I think you have the idea lecture of a stationary series from which we will see hangels, Ghoutage,ajma जो हैंगे च्रचच MBA तर हैंगे ख्णाद खुध वोमाँे पन Kathleen with Rahe worst sling तर नौलगी च साथबिश्ट कलते लेस साथौट साथ अगि है तो उचि कल क्या हूंध नौल ज possessed. आप नान्स्टेशनरी, सब से पहले चैक करने है, के how you can detect the series is stationary, then secondly, if the series is non-stationary, then आप ने किस तरा move forward करना होगा, के आप उसको चैक कर सके, so there are certain like tests, if we are in the form of stationarity which is prominent, there is a variety of stationarity tests available in like Stata, in eViews, even in SPSS, we can use it in RStudio, so we can use it in RStudio, so we can use it in RStudio, so we can use it in RStudio, so we can use it in RStudio, so we can use it in RStudio, so we can use it in R we can include all those tests, but the most important and prominent tests and authentic tests are Dickey Fuller, developed by Dickey and Fuller, second one is the augmented shape, the expanded version of a Dickey Fuller test, we call it augmented Dickey Fuller test, this is also prominent, we can use it for checking the stationarity, and third one is another important, that is, we can use it for stationarity, on the other hand, we can check it informally by graphical analysis, we draw its graph and we get an idea whether the series is stationary or not stationary, then we have the idea of Corallogram, we check with Corallogram whether the numbers of ACF and PSF, like auto correlation function and partial auto correlation function, ACF for auto correlation function and PACF for partial auto correlation function, it shows that the significant limits, if ACF and PS values are like that, then series is stationary, if they are not like that, then series is non-stationary, how we will convert it, where we will get an idea whether AR process is coming or MA process is coming, and you have the idea that AR process, we have already discussed that we will regress it on your dependent variable lag, but in MA process, we will regress dependent variable on its error lag, sometime we regress on dependent variable lag, sometime we regress on error lag, if we regress on error lag, then this is MA process, moving average process, if we regress on dependent variable lag, this is AR auto regressive process, and there is a minor difference, which we will use in the next analysis, that how we have to run AR process and MA process, sometime ARMA process is mixed, that dependent variable lag is also included, and error lag is also included, that is ARMA process, and if we expand it and include integrated process, then it will become ARMA, ARTO regressive, IFR integrated and MFR moving average process,