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#statistics #stata #data_analysis

[statisticalaid]

#statistics #Stata #Multiple_regression #regression_analysis

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#statistics #Stata #data_cleaning

[statisticalaid]

#statistics #Stata

[statisticalaid]

#stata #data_cleaning

[statisticalaid]

Learn Statistics and Data Analysis Intuitively

Welcome to Statistical Aid!

Statistical Aid is a site that provides statistical content, data analysis content, and also discusses the various fields of statistics. You can learn statistics and data analysis intuitively by Statistical Aid. All the contents on this site are written to provide help to the students who are very weak in statistics and data analysis. From basic to advanced, you can get all the topics of statistics presented on this site very simply. You can get help from the following topics:

Basic Statistics

Definition and scope of statistics

Statistical Data

Population vs Sample

Random Variable

Central tendency

Arithmetic, Geometric and harmonic mean

Measures of Dispersion

Variance and Standard Deviation

Skewness and Kurtosis

Correlation analysis

Intra vs Inter class correlation

Regression Analysis

Data levels (Nominal, ordinal, Interval and Ratio)

Hypothesis Testing

Probability Distributions in Statistics

Bernoulli Distribution

Binomial Distribution

Negative Binomial distribution

Poission Distribution

Exponential Distribution

Normal distribution

Gamma Distribution

Geometric Distribution

Hypergeometric Distribution

Uniform Distribution

Power Series Distribution

Logarithmic Series Distribution

Sampling Distributions in Statistics

Probability Sampling

Simple Random Sampling

Stratified Sampling

Systematic Sampling

Multi-Stage Sampling

Cluster Sampling      

Quadrat Sampling

Non Probability Sampling

Purposive sampling

Snowball sampling

Convenience sampling

Data analysis Content and Tutorials

SPSS

R Tutoriala

SPSS Tutorials

There are also some other topics as following:

Non Parametric Tests

Time Series Analysis

Statistical Inference

Experimental Design

Data Science

Data Analytics tools free Download

SPSS 25 free Download

Stata 14.2 free Download

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Image Source: Statistical Aid: A School of Statistics

Literally, skewness means the 'lack of symmetry'. We study skewness to have an idea about the shape of the curve which we can draw with the help of the given data. A distribution is said to be skewed if-

Mean, median, mode fall at different points, i.e, Mean ≠ Median ≠ Mode.

Quartiles are not equidistant from median.

The curve drawn with the help of the given data is not symmetrical but stretched more to one side than the other.

The lack of symmetry in a distribution is always determined with reference to a normal distribution, which is always symmetrical. Any departure of a distribution from symmetry leads to an asymmetric distribution and in such cases, we call this distribution as skewed. The skewness may be either positive or negative. Absence of skewness makes the distribution symmetrical.

It is important to emphasize that skewness of a distribution cannot be determined simply by inspection. If we understand the differences between the mean, median and the mode, we should be able to suggest a direction of skew.We can define the skewness of a frequency distribution in three different shapes as following-

(1). Symmetrical distributions

This type of  distribution is known as normal distribution. We can obtain this distribution with height, weight, iq score and many other random variable from real life data. An important characteristics of such distribution is that the mean, median and mode have same value.

(2). Positively skewed distributions

In this distribution, the right tail is long which indicates the presence of extreme values at the positive end of the distribution. This pulls the mean to the right tail. this types of distribution is known as positively skewed distribution. This distributions occur with some real life variable such as family size, wages of the worker etc.

(3). Negatively skewed distributions

In this types of distribution, the mean is pulled in the negative direction. This occurs in some real life variable such as daily maximum temperature for a month in winter.

Measures of skewnessWe can simply measure the skewness using pearson's coefficient of skewness as below-

Skewness(p)= (Mean-Mode) / Standard Deviation

We can make following decissions from the pearson's coefficient of skewness as following-

If mean > mode, the distribution is positively skewed.

If mean < mode, the distribution is negatively skewed.

If mean = mode, the distribution is not skewed or symmetrical.

In some case, mode cannot be uniquely defined, so we cannot apply the above formula. For alternative we use the following formula pearson's coefficient of skewness-

Skewness(p)= 3(Mean-Mode) / Standard Deviation

We can also mesure the skewness using moments. The formula of skewness using moments is following-

And Karl pearson suggest skewness as,

(kurtosis)

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Business Forecasting Definition, Steps, Modeling with Importance

Image Source: Statistical Aid: A School of Statistics

Forecasting is the process of making prediction of the future based on past and present data.

In many cases a reliable forecast can be worth a lot of money, such as consistently and correctly guessing the behavior of the stock market for enough in advance to act upon such a guess.

Objectives of forecasting

In narrow sense, the objectives of forecasting is to produce better forecast. But in the broader sense, the objective is to improve organizational performance, more revenue, more profit, increased customer satisfaction etc. Better forecast by themselves are no inherent value of those forecast are ignored by management or otherwise not used to improve organizational performance.

Steps in forecasting

There are six steps in business forecasting. They are given below-

Identify the problem: This is the most difficult step of forecasting. Defining the problem carefully requires an understanding of the way the forecasts will be used.

Collect information: In this steps we collect information not data, because data may not be available if for example the forecast is aimed at a new product. The information comes essentially in two ways: the knowledge gathered by expert and from actual data.

Performing a preliminary analysis: An early analysis of data may tell us right away if the data usable or not. It also helps in choosing the model that best fit it.

Choose a forecasting model: Once all the information is collected and treated then we may choose the model that will give the best prediction possible. If we may not even have historical data then we have to use qualitative forecasting otherwise quantitative forecasting.

Data analysis: This step is very simple. After choosing the suitable model, run the data through it.

Verify model performance: Finally, we have to compare forecast to actual data.

Methods of  business forecastingThere are various important forecasting methods in time series analysis. They are-

Historical analogy method

Field survey and opinion poll

Business barometers

Extrapolation

Regression analysis

Time series analysis

Exponential smoothing

Econometric model

Lead-lag analysis

Input-output analysis

Importance of forecasting

Formation of new business: Forecasting is utmost important in setting up a new business. with the help of forecasting the promoter can find out whether he can succeed in new business, whether he can face the existing competition.

Estimation of financial requirements: Financial estimates can be calculated in the light of probable sales and cost there of. How much capital is needed for expansion, development etc will depend upon accurate forecasting.

Correctness of management decision: The correctness of management decisions to a great extent depends upon accurate forecasting. The forecasting is considered as the indispensable components of business, because it helps management to take correct decisions.

Plan formation: The importance of correct forecasting apparent from the key role it plays in planning. Infact, planning under all circumstance and in all occassions involve a good deal of forecasting.

Success in business: The accurate forecasting of sales helps to produce necessary raw materials on the basis of which many business activities are undertaken. It is difficult to decide as to how much production should be done. Thus the success of a business unit depends on the accurate forecasting.

Complete control: Forecasting provides the information which helps in the achievement of effective control. The managers become aware of their weakness during forecasting and through implementing better effective control they can overcome these weakness.

Source..

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Canonical correlation analysis using R

How to perform canonical correlation in R?

Follow this step to find canonical correlation in R-

   open R program

   open a new script

  import your data set

   define the independent and dependent variable

   combing the data into matrix

   convert the data into standard normal

   block the correlation matrix

   find the eigen value

   calculate canonical correlation

R code