- ARIMA-ARIMA methodology is popular under SAS because of ARIMA models. These models are popularly used by data analysts to capture a suite of different standard temporal structures in time series data.
- ANOVA and MANOVA
- Linear Regression
- Time series smoothers
- Choice modelling
- Sample selection
- Finite mixture model
- Bayesian Analysis
Stata is cross-platform compatible and can run on Windows, Mac and Linux. SAS is a versatile software application with well-built functions and commands. With its friendly environment, you can carry out any task testing on Full Time –binary with fewer complications It offers the following data management capabilities to its users;
- Stata allows an easy and straight forward creation of variables and sub-setting data.
- The users can learn coding quickly because syntax is provided
- Users can share complex coding syntax
Using Stata can sometimes prove to be difficult because of the challenging complex programming involved. Stata’s documentation is sparse and does not provide full details. According to our internal survey, we have realized that heteroskedasticity estimation, melin convolution and data manipulation are the most sought after topics in our platform. We have deduced that these areas are exhaustive and time-consuming for any typical student. Students are always left frustrated especially when they are dealing with large data sets. They are forced to look for experts to help them use Stata for their data analysis.
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Stata Homework Help
Stata is a statistical software that allows the user to manipulate all types of data. The user can change the data, control the variable and integrate data sets. It provides tools for users to perform estimations using the following phenomena;
- Truncation- Stata has a Truncreg tool used to perform truncated linear regression
- Censoring- The Tobit and Intreg commands are available to users for censoring linear regression
Stata is very useful in data management and statistics. It is comprehensively designed to generate powerful intuitive syntax. Many fields of studies like Commerce employ Stata in their various statistical analyses to help their managers forecast and make pragmatic decisions. For instance, Stata has become highly applicable in the medical field under clinical trial evaluation. Stata is used to analyze the subgroups of health groups using a test of interaction.
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Anova Homework Help in Stata
Analysis of Variance is a test used to conduct analysis on various types of factors that affect a data set. It can be used to determine if a relationship exist between two or more groups. It is mostly used when a statistical software is not available and computing needs to be done by hand.
The following four assumptions are made when using ANOVA;
- The errors are expected to generate zero values
- The errors have equal variances
- The errors are independent
- There is normal distribution of errors
There are two main types of Analysis of variance;
- One-way ANOVA- It is also known as unidirectional. It only allows for one independent variable. It is used to determine the similarity of the samples and the impact of a factor on the independent variable.
- Two-way ANOVA- It is an extension of the One-way ANOVA. It has two independent variables. It is used by researchers to observe the interaction between the two factors and their effects at the same time.
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Chi Square Homework Help in Stata
Chi square is a statistical tool that is used to measure how expectations compare to actual results. It is used to determine the discrepancies that exist between expected results and actual results. A chi square performs two different kinds of non-parametric test; test of independence- Which tests for relationships, and the goodness of fit test. If you are keen in your chi-square lectures, you must have heard your professor mentioning bivariate correlation frequently. In this area, you are supposed to comprehend how to use chi-square to summarize the difference of frequencies against the frequencies you expect to see if there was no relationship between the variables.
Chi square requires a researcher to perform a statistical significance test. This test is used to determine if the results of the experiment occur randomly or by chance. For a Chi square test of goodness of fit to be valid, the following conditions must be met:
- The observations on the sample should be independent
- The constraints on the cell frequencies should be linear
- No theoretical frequency should be less than 5
- The total frequency should be reasonably large, say greater than 50
A Chi square distribution has the following important applications;
- Chi square test of goodness of fit
- Chi square test for independent attributes
- Chi square test as a test of homogeneity.
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Hypothesis Testing Homework Help in Stata
Hypothesis testing is a statistical tool that computes tests on two data sets and compares it to a critical value. The null hypothesis assumes that no relationship exist between the two variables. The alternative hypothesis assumes that there is a relationship between the two variables. To conduct a hypothesis testing, the researcher should;
- Figure out what their null hypothesis is
- State the null hypothesis
- Decide on which kind of test they want to perform
- Either accept or reject the null hypothesis
The P value in hypothesis testing represents the probability of a given event occurring. A smaller P value would mean that a stronger evidence exist in the favour of a hypothesis testing. It is calculated using tables or a statistical software. A type error occurs when a null hypothesis is rejected although it is true. It is also referred to as the false positive and it accepts the alternative hypothesis which is despite it being attributed to chance. Having that in mind, one key aspect you should expect to be tested under hypothesis testing is on concurrent validity coefficient. For you to crack any question, you are supposed to be adept on how to compare concurrently two datasets at the same line against critical value. A z-test is a type of hypothesis test which follows normal distribution.
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Statistics Homework Help in Stata
Statistics is an advanced form of mathematics that involves data collection, analysis, interpretation of results and presentation. A lot of formulas and software are always put in use to help with calculations. The right formula or software should always be used to ensure that the results are accurate. Multiple concepts and topics in statistics are always interrelated and students are always left confused. Topics such estimated regression equation, discrete random variable representation are just a mention of troubling questions under statistics. However, don’t allow the assignment confusions affect your scores. At statistics assignment helper we can clear all those confusions with no hesitation
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Inferential Statistics Assignment Help on Stata
Inferential statistics is a tool used to draw conclusions that extend beyond the immediate data . It is used to infer judgements on a sample of data to find out if the differences observed between groups are dependable ones or happened by chance. In most cases‘ conduct appropriate statistical tests‘ is a common question under inferential statistics. Here you are required to use measurements of sample objects to make generalizations on larger objects using statistical analyses Descriptive statistics on the other hand describes to the researcher what is going on in the data. The examples of inferential statistics include;
- Analysis Of Variance
- Analysis Of Covariance
- Factor Analysis
- Regression Analysis
- Cluster Analysis
- Discriminant functions
- Multidimensional Scaling
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Sampling Assignment Help on Stata
Sampling is a statistical tool used in collecting information from representatives of a target population and using that information to judge the entire population. The following are the methods of sampling that are commonly used;
- Purposive sampling- This is a type of sampling where sample units are selected with definite purpose view. This method is greatly criticised to suffer from favouritism and nepotism. It does not give a clear representation of the entire population.
- Random sampling- This method selects the sample units randomly. Every unit within the population has an equal chance of making the sample. It overcomes the drawback of favouritism suffered in purposive sampling. It is very difficult to achieve because of the time, effort and resources required.
- Stratified sampling- in this method, the entire target population is divided into a number of homogenous groups called strata. Units are then randomly sampled from each stratum to form an aggregate.
- Simple sampling-This method is a form of random sampling. It is mostly used on small populations. Cluster sampling. In this method, the researcher encounters multiple clusters with homogenous characteristics, and every group has an equal chance to be part of the sample.
The method used to conduct sampling depends on the size of the population and the types of characteristics the researcher want to study. It is also very important that the samples are taken from the population of study. This will help the researcher derive effective conclusions and apply the results to the entire population.
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