College Statistics Help
As a college student, you have probably experienced challenges with your statistics assignments many times. Though statistical techniques and models are meant to help student understand how to analyze data effectively and solve real life problems, the subject can be quite a challenge especially for those who have a dislike for numbers. So how do students get rid of the headache and stress resulting from statistics projects? They seek professional college statistics help. We, at Statistics Assignment Experts are adept at the various topics and concepts covered in this subject and willing to offer the needed academic assistance. We offer aid on different educational documents including essays, theses, business plans, term papers, and more. Contact us now for help with college statistics.
Division Of Topics In Statistics
The most commonly known divisions of statistics is descriptive and inferential statistics. But there are many different ways that the discipline of statistics can be separated out. One of these ways is classifying statistical methods into parametric or nonparametric methods. Most students can hardly tell the difference between the two and that’s why we have seen the need to look into this division in depth.
In parametric division, statistical methods are classified based on what is known about the population being studied. This is typically the first division of statistical methods you study when you enroll in a statistics course. Parametric methods are used when the parameters in a population are approximately normal. There are two major parameters that can be used in a normal distribution – mean and standard deviation. Overall, a statistical method is classified as parametric based on the assumptions made about the population being studied. Below are a few examples of parametric methods:
- Confidence interval for population mean in which the standard deviation is known
- Confidence interval for population mean in which the standard deviation is unknown
- Confidence interval for population variance
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Nonparametric methods are statistical methods in which no assumptions need to be made regarding the parameters of the population of interest. The parameters in nonparametric methods are not fixed, do not depend on the population being studied, and the distribution is not fixed. For this reason, nonparametric methods have also been referred by many as distribution free methods.
Nonparametric methods are widely used today because they are not as constraining as parametric methods. Also, no assumptions need to be made about the population, and most importantly, most nonparametric methods are easy to understand and apply in data analysis. Below are a few examples of nonparametric methods:
- Sign test
- Bootstrapping techniques
- U test
- Spearman correlation test
- Chi square
- Wilcoxon ran sum test
- Kruskal Wallis test
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Type I And Type II Errors
Type I and type II errors are used in descriptive statistics during hypothesis testing. A type I error occurs when you reject the null hypotheses incorrectly, when null hypothesis is true. A type II error, on the other hand occurs when you don’t reject the null hypothesis. Statistical tests contain both null and alternative hypothesis. Null hypothesis is a claim about a population that is considered to have no particular effect on the population. Alternative hypothesis is the claim you need to provide enough evidence for during a hypothesis test. Statistical tests can have four possible results:
- You reject null hypothesis yet the null hypothesis is true (type I error)
- You reject null hypothesis and the disagreeing hypothesis (alternative hypothesis) is true. This shows you have made the right decision.
- You accept null hypothesis and this null hypothesis is true
- You accept the null hypothesis, yet the disagreeing hypothesis (alternative hypothesis) is true. (type II error)
Of course, the preferred hypothesis test would be the one where the right decision has been made, without any errors, but quite often errors are made during hypothesis tests. Still, knowing how to carry out your test properly can help minimize the number of errors. Learn more about type I and type II errors from our college statistics helpers.
Statistical Data Analysis Methods
Now that you know what parametric and nonparametric methods as well as hypothesis test errors involve, let’s briefly look at some of the most common statistical data analysis methods you will cover in college. But first things first – what is statistical data analysis?
Statistical data analysis is the process by which raw numerical data is cleaned, transformed, and modeled to obtain useful information. This process is performed using data analysis tools like:
- Python, and more
These tools make it easier for analysis to process data, manipulate it, and analyze the correlations and relationships between different datasets. They also help them identify trends and patterns in data, which is useful in forecasting.
There are many different types of methods and techniques used in statistical data analysis. The most common ones include:
- Text analysis: Also known as data mining, text analysis is the technique of discovering patterns in large sets of data using data mining tools and databases. This technique provides analysts with a way to extract and study data, derive patterns, and interpret the data into meaningful information.
- Statistical analysis: This method involves collecting, analyzing, interpreting, presenting, and modeling data. Statistical analysis can be categorized into descriptive analysis and inferential analysis.
- Diagnostic analysis: This technique is used to find and rectify problems in data. It answers the question, “Why did it happen?” It also helps come up with solutions for potential problems in data sets.
- Predictive analysis: This technique shows analysts what is likely to happen. It uses previous data to make predictions about the future. For instance, consider a company that bought a new car last year based on the profit it had made. If it happens that this year the profit registered by the company has doubled, then the assumption might be the company will buy two or three vehicles. However, it may not happen this way because there are other circumstances like the chances of the prices of cars increasing this year or maybe possibility of the company buying something else instead. So, generally, predictive analysis is not a technique that analysts can really count on for accurate results. Its accuracy depends on how detailed the available information is, and how consistent the data being studied has been in the past.
- Prescriptive analysis: This technique puts together the insights from all the previous analyses to determine the best action to take in a current decision or problem.
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