Faukar Data Analysis and Consultancy

03/02/2020

Multivariate Analysis
The basic definition of multivariate analysis is a statistical method that measures relationships between two or more response variables. Multivariate techniques attempt to model reality where each situation, product or decision involves more than a single factor. For example, the decision to purchase a car may take into consideration price, safety features, color and functionality. Modern society has collected masses of data in every field, but the ability to use that data to obtain a clear picture of what is going on and make intelligent decisions is still a challenge.
Limitations of Multivariate Analysis
Multivariate techniques are complex and involve high level mathematics that require a statistical program to analyze the data. These statistical programs can be expensive for an individual to obtain. One of the biggest limitations of multivariate analysis is that statistical modeling outputs are not always easy for students to interpret. For multivariate techniques to give meaningful results, they need a large sample of data; otherwise, the results are meaningless due to high standard errors. Standard errors determine how confident you can be in the results, and you can be more confident in the results from a large sample than a small one. Running statistical programs is fairly straightforward but does require statistical training to make sense of the data.

19/12/2019

In this paper, we introduced a four-parameter probability model called Weibull-Inverse Lomax distribution with decreasing, increasing and bathtub hazard rate function. The WIL distribution density function is J-shaped, positively skewed, and J-shaped in reverse. Some of the mentioned distribution’...

01/12/2019

The Durbin-Watson test is the standard test for correlated errors.
No outliers: We assume that there is no outlier in the series. Outliers may affect conclusions strongly and can be misleading.
Random shocks (a random error component): If shocks are present, they are assumed to be randomly distributed with a mean of 0 and a constant variance.

01/12/2019

The following equation shows the non-linear behavior:
The dependent variable, where the case is the sequential case number.
Curve fitting can be performed by selecting “regression” from the analysis menu and then selecting “curve estimation” from the regression option. Then select “wanted curve linear,” “power,” “quadratic,” “cubic,” “inverse,” “logistic,” “exponential,” or “other.”
ARIMA:
ARIMA stands for autoregressive integrated moving average. This method is also known as the Box-Jenkins method.
Identification of ARIMA parameters:
Autoregressive component: AR stands for autoregressive. Autoregressive parameters is denoted by p. When p =0, it means that there is no auto-correlation in the series. When p=1, it means that the series auto-correlation is till one lag.
Integrated: In ARIMA time series analysis, integrated is denoted by d. Integration is the inverse of differencing. When d=0, it means the series is stationary and we do not need to take the difference of it. When d=1, it means that the series is not stationary and to make it stationary, we need to take the first difference. When d=2, it means that the series has been differenced twice. Usually, more than two-time difference is not reliable.
Moving average component: MA stands for moving the average, which is denoted by q. In ARIMA, moving average q=1 means that it is an error term and it is auto-correlation with one lag.
In order to test whether or not the series and their error term are auto correlated, we usually use W-D test, ACF, and PACF.
Decomposition: Refers to separating a time series into trend, seasonal effects, and remaining variabilityAssumptions:
Stationarity: The first assumption is that the series are stationary. Essentially, this means that the series are normally distributed and the mean and variance are constant over a long time period.
Uncorrelated random error: We assume that the error term is randomly distributed and the mean and variance are constant over a time period. The Durbin-

01/12/2019

***Time Series Analysis***
Time series analysis is a statistical technique that deals with time series data, or trend analysis. Time series data means that data is in a series of particular time periods or intervals. The data is considered in three types:
Time series data: A set of observations on the values that a variable takes at different times.
Cross-sectional data: Data of one or more variables, collected at the same point in time.
Pooled data: A combination of time series data and cross-sectional data.
Terms and concepts:
Dependence: Dependence refers to the association of two observations with the same variable, at prior time points.
Stationarity: Shows the mean value of the series that remains constant over a time period; if past effects accumulate and the values increase toward infinity, then stationarity is not met.
Differencing: Used to make the series stationary, to De-trend, and to control the auto-correlations; however, some time series analyses do not require differencing and over-differenced series can produce inaccurate estimates.
Specification: May involves the testing of the linear or non-linear relationships of dependent variables by using models such as ARIMA, ARCH, GARCH, VAR, Co-integration, etc.
Exponential smoothing in time series analysis: This method predicts the one next period value based on the past and current value. It involves averaging of data such that the nonsystematic components of each individual case or observation cancel out each other. The exponential smoothing method is used to predict the short term predication. Alpha, Gamma, Phi, and Delta are the parameters that estimate the effect of the time series data. Alpha is used when seasonality is not present in data. Gamma is used when a series has a trend in data. Delta is used when seasonality cycles are present in data. A model is applied according to the pattern of the data. Curve fitting in time series analysis: Curve fitting regression is used when data is in a non-linear

24/11/2019

https://chat.whatsapp.com/KG378buDX1rAi3TZzUyBQd

WhatsApp Messenger: More than 1 billion people in over 180 countries use WhatsApp to stay in touch with friends and family, anytime and anywhere. WhatsApp is free and offers simple, secure, reliable messaging and calling, available on phones all over the world.

15/11/2019

26/10/2019

CONCEPT OF ONE WAY ANOVA

One - way ANOVA ( Analysis of variance) is used to determine if the difference between means of two or more unrelated groups is statistically significant . People don ’ t usually perform one- way ANOVA when there are just two groups because student t test is already available for such condition. When there are more than two groups, one - way ANOVA tells whether at least two group differ in their means but doesn ’ t specify which are those groups . If we really want to know in specific which are those groups that differ, we have to run a post - hoc test. If there is a third intervening variable, we have to statistically control that variable using “ANCOVA ” which is known as Analysis of Co - variance

22/10/2019

04/09/2019

How to Start a Survey Business

Market Your Services

To find clients for your business, identify prospects in your area. Contact restaurants, service centers and retailers to offer customer satisfaction surveys. Marketing agencies may have a need for industry analysis or market research surveys. List your services on your website with examples of surveys you have carried out. To demonstrate your capability, carry out independent surveys of market sectors, place summaries on your website and email prospects offering sample reports. Contact magazine publishers with details of your independent surveys and offer executive summaries for publication.