Data utilization has become a day-to-day essential in today's digital world. In this session, we discuss about how to analyse data using Stata
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Data utilization has become a day-to-day essential in today's digital world. In this session, we discuss about how to analyse data using Stata
#statistics #stata #data_analysis
Multiple Regression Analysis using stata is a an easy way to predict a single outcome variable from a set of independent factors.
#statistics #Stata #Multiple_regression #regression_analysis
Data cleaning and editing is a vital step to analyze our data. Data cleaning is a process that checks to see if variables' values are valid
#statistics #Stata #data_cleaning
Variable manipulation and reliability check is an essential step of data analysis. In this article we will also learn how to re-code....
#statistics #Stata
Statistical tests are used to test hypotheses relating to either the difference between two or more samples/groups or the relationship...
Data cleaning and editing is a vital step to analyze our data. Data cleaning is a process that checks to see if variables' values are valid
#stata #data_cleaning
Variable manipulation and reliability check is an essential step of data analysis. In this article we will also learn how to re-code....
ICA means Independent Component Analysis. ICA is a most powerful and widely used statistical technique which is used to separate independent
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
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Quadrat Sampling
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Purposive sampling
Snowball sampling
Convenience sampling
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There are also some other topics as following:
Non Parametric Tests
Time Series Analysis
Statistical Inference
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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)
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..
Logistic regression is a statistical technique to find the association between the categorical dependent (response) variable and one or.....
skewness and kurtosis in statistics, measure of skewness, measure of kurtosis, positively and negatively skewed distribution.
Statistical aid is a site which provides statistical content, data analysis content and also discuss the various fields of statistics.
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
statistical hypothesis testing, hypothesis testing, null and alternative hypothesis, simple and composite hypothesis, example of hypothesis.
random variable, discrete random variable, continuous random variable, properties of random variable,definition of random variable.