Economic Order QuantityEconomic order quantity (EOQ) is the order quantity that minimizes total inventory holding costs and ordering costs. It is one of the oldest classical production scheduling models. The framework used to determine this order quantity is also known as Wilson EOQ Model or Wilson Formula.Co-relation and regressionThe goal of a correlation analysis is to see whether two measurement variables co vary, and to quantify the strength of the relationship between the variables, whereas regression expresses the relationship in the form of an equation.*Scatter DiagramA graph in which the values of two variables are plotted along two axes, the pattern of the resulting points revealing any correlation present.

Time Series AnalysisTime series analysis comprises methods for analyzing time series data in order to extract meaningful statistics and other characteristics of the data. Time series forecasting is the use of a model to predict future values based on previously observed values.

Statistical HypothesisA statistical hypothesis is a scientific hypothesis that is testable on the basis of observing a process that is modeled via a set of random variables. A statistical hypothesis test is a method of statistical inference used for testing a statistical hypothesis.*Statistical Hypothesis null (H0) and Statistical Hypothesis alternative (Ha or H1)In statistical hypothesis testing, the alternative hypothesis (or maintained hypothesis or research hypothesis) and the null hypothesis are the two rival hypotheses which are compared by a statistical hypothesis test.In inferential statistics on observational data, the null hypothesis refers to a general statement or default position that there is no relationship between two measured phenomena.

Chi Square TestChi-squared test (2) is a statistical test applied to sets of categorical data to evaluate how likely it is that any observed difference between the sets arose by chance. It is suitable for unpaired data from large samples. It is the most widely used of many chi-squared tests (Yates, likelihood ratio, portmanteau test in time series, etc.). In contexts where it is important to improve a distinction between the test statistic and its distribution.It tests a null hypothesis stating that the frequency distribution of certain events observed in a sample is consistent with a particular theoretical distribution. The events considered must be mutually exclusive and have total probability 1. A common case for this is where the events each cover an outcome of a categorical variable.

Diagrams

Scatter diagrams are commonly produced from surveys or experiments, and are composed of points relating two variables, e.g. height and age of people under 18. Logically, for such a diagram, the horizontal axis would be used for age and the vertical axis for height. Each point on this diagram would represent the characteristics of an individual person.Obviously for such a graph, you might expect some correlation between the results, i.e. you might expect that as age increases, so does height. The points would tend to show a relationship does exist but the points definitely do not fall on a nice straight line - this 'straight line' relationship is implied.When there appears to be a relationship between the two quantities displayed on a scatter diagram, then we say they appear to be correlated. Look at the diagrams above.The first and last diagrams indicate correlation, the first is described as positive correlation, the last one as negative correlation. It is not exact, but all other things being equal, we are inclined to believe that there is a relationship between the quantities represented on the diagrams.We ought to point out that correlation itself is not proof of any causal relationship between the two variables. In the past, correlation has been shown to exist between the parrot population in an area of Central America and an economic factor in the United States. So although there was good correlation in this case, this was just chance - there was no actual causal connection between the two quantities at all.At the other end of the scale, even for quantities where a causal connection could exist, correlation is not, by itself, proof of an actual causal connection - it is just a piece of evidence that you would need to back up with other arguments, if you wanted suggest that there was an actual causal connection.It should also be stressed that here we having been talking about linear correlation (a 'straight line' connection). Features that are not linearly correlated could still possibly be correlated by a more complicated mathematical expressionNeural networksA neural network usually involves a large number of processors operating in parallel, each with its own small sphere of knowledge and access to data in its local memory. Typically, a neural network is initially "trained" or fed large amounts of data and rules about data relationships (for example, "A grandfather is older than a person's father"). A program can then tell the network how to behave in response to an external stimulus (for example, to input from a computer user who is interacting with the network) or can initiate activity on its own (within the limits of its access to the external world).In making determinations, neural networks use several principles, including gradient-based training, fuzzy logic, genetic algorithms, and Bayesian methods. Neural networks are sometimes described in terms of knowledge layers, with, in general, more complex networks having deeper layers. In feed forward systems, learned relationships about data can "feed forward" to higher layers of knowledge. Neural networks can also learn temporal concepts and have been widely used in signal processing and time series analysis.