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What is Time-Series forecasting? What is the difference between in Time series and regression?

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        Time series forecasting is a technique for the prediction of events through a sequence of time. The technique is used across many fields of study, from the geology to behavior to economics. The techniques predict future events by analyzing the trends of the past, on the assumption that future trends will hold similar to historical trends.     Difference between in Time series and regression Time-series:  1. Whenever data is recorded at regular intervals of time.  2. Time-series forecast is Extrapolation . 3. Time-series refers to an ordered series of data.    Regression:    1. Whereas in regression, whether data is recorded at regular or irregular intervals of time, we can apply.  2. Regression is Interpolation.  3. Regression refer both ordered and unordered series of data
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What is the statistical test for data validation with an example, Chi-square, ANOVA test, Z statics, T statics, F statics, Hypothesis Testing?

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  Before discussing the different statistical test, we need to get a clear understanding of what a null hypothesis is. A null hypothesis proposes that has no significant difference exists in the set of a given observation.   Null: Two samples mean are equal. Alternate: Two samples mean are not equal. For rejecting the null hypothesis, a test is calculated. Then the test statistic is compared with a critical value, and if found to be greater than the critical value, the hypothesis will be rejected.   Critical Value:- Critical values are the point beyond which we reject the null hypothesis. Critical value tells us, what is the probability of N number of samples, belonging to the same distribution. Higher, the critical value which means lower the probability of N number of samples belonging to the same distribution.   Critical values can be used to do hypothesis testing in the following way.   1. Calculate test statistic 2. Calculate critical values based on the
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