ST2101 Introduction to Probability and Statistics Assignment Sample NUI Galway Ireland
ST2101 Introduction to Probability and Statistics is a course offered by the National University in Ireland, Galway. The course covers probability, statistics, and their applications.
The objectives of the course are to introduce students to the basic concepts and methods of probability and statistics, as well as their applications in engineering and science. The course will also equip students with skills in data analysis, statistical modelling, and associated computational methods.
The course consists of lecture sessions, tutorial sessions, and laboratory sessions. Tutorial sessions will focus on problem-solving and interpretation of results. Laboratory sessions will allow students to apply the concepts and methods learned in the lectures and tutorials to real data sets.
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In this section, we are describing some assigned briefs. These are:
Assignment brief 1: Understand the role of Probability as a discipline in its own right and in solving Statistical Inference problems.
Probability plays a vital role in statistical inference, which is the process of using data to conclude population parameters. To accurately solve statistical inference problems, one must have a strong understanding of probability.
Probability theory provides the mathematical foundation for inferring population parameters from sample data. The foundation is based on the notion of repeatable random experimentation and emphasizes the use of probability models for assessment and decisions. Without this mathematical underpinning, it would be impossible to make the inferences that are essential for critical decision-making in fields such as medicine, engineering, and finance.
Statistical inference traditionally involves two steps: estimation and hypothesis testing. In estimation, we use sample data to estimate population parameters, such as the mean or variance. In hypothesis testing, we use sample data to test hypotheses about population parameters, such as whether the mean is equal to a certain value.
Probability theory is also important in deriving measures of uncertainty, such as confidence intervals and p-values. These measures quantify the degree of uncertainty associated with estimates and tests of hypotheses.
Assignment brief 2: Carry out basic probability calculations, especially about normal variables, including implementation of the Central Limit Theorem.
When it comes to probability calculations, normal variables are some of the easiest to work with. This is because of the Central Limit Theorem, which essentially states that any distribution can be approximated by a normal distribution if you have enough data points. Therefore, if you understand how to calculate probabilities for a normal distribution, you can use that knowledge to approximate probabilities for any distribution.
One of the most important things to know when working with normal variables is the standard deviation. This is a measure of how spread out the data is around the mean. The larger the standard deviation, the more spread out the data will be. Standard deviations are usually denoted by σ (sigma) in mathematical equations.
Another important concept is the standard normal distribution, which is a special case of the normal distribution with a mean of 0 and a standard deviation of 1. The standard normal distribution is also sometimes called the z-distribution.
To calculate probabilities for a normal distribution, you need to know the mean and standard deviation. Once you have that information, you can use a table of standard normal probabilities, or you can use a graphing calculator or computer software to calculate the probabilities.
Assignment Brief 3: Understand basic ideas about confidence intervals; be familiar with Type I and Type II errors in hypothesis tests, including producer and consumer risk in quality control, and be able to calculate the p-value and power of various statistical tests.
Confidence intervals are tools statisticians use to quantify uncertainty. Essentially, a confidence interval is an interval estimate of a population parameter. It gives us a range of values within which the true value is likely to lie.
There are two types of errors that can occur in hypothesis tests: Type I and Type II. Type I error occurs when we reject the null hypothesis when it is true. This represents ‘producer risk’. Type II error occurs when we don’t reject the null hypothesis when it is false. This represents ‘consumer risk’.
Ideally, we would like to have zero producer risk and zero consumer risk, but this is often not possible. As statisticians, we must choose a level of risk that we are comfortable with.
P-values and power are two measures statisticians use to assess the performance of statistical tests. P-value is the probability of observing a test statistic at least as extreme as the one we observed, given that the null hypothesis is true. Power is the probability of rejecting the null hypothesis when it is false.
Assignment Brief 4: Get confidence intervals and perform hypothesis tests about population means, population proportions and the difference between two population means, with emphasis on problems that arise in Environmental Science and Health and Safety.
There are a few different types of confidence intervals you can use to get an estimate of the population mean. You can use a z-interval, t-interval, or bootstrap interval. A z-interval is based on the assumption that the population standard deviation is known and is equal to the sample standard deviation. A t-interval is based on the assumption that the population standard deviation is unknown but is estimated by the sample standard deviation. A bootstrap interval does not rely on any assumptions about the population distribution.
To perform a hypothesis test about a population mean, you first need to calculate a test statistic. The test statistic is used to determine which type of hypothesis test you should use (z-test or t-test). Once you have determined the type of test to use, you can calculate the p-value. The p-value is the probability of observing a test statistic at least as extreme as the one we observed, given that the null hypothesis is true.
If you want to test the difference between two population means, you can use a two-sample t-test. This test is based on the assumption that the two populations have equal variances. If this assumption is not met, you can use a Welch’s t-test instead.
Assignment Brief 5: Analyse qualitative data and test the independence of categorical variables.
There are a few different ways that you can go about analyzing qualitative data and testing the independence of categorical variables is one of them. To start with, you’ll need to understand what qualitative data is and what categorical variables are.
Qualitative data is non-numeric data that can be classified. This data can be described using words or labels, and it is often used to collect information on opinions, attitudes, and behaviours. Categorical variables are also known as ‘nominal’ or ‘labelled’ variables, and they too can be classified. These types of variables are usually stored as text values (e.g. “red”, “blue”, “green”) rather than numeric values (e.g. 1, 2, 3).
One way to test the independence of two categorical variables is to use a chi-squared test. This type of test is used to compare the observed frequencies of data in two or more different groups to the expected frequencies of data if the two variables were independent.
Assignment brief 6: Calculate and interpret the linear correlation coefficient for relating two variables.
The linear correlation coefficient, r, is a measure of the strength and direction of the linear relationship between two variables. The coefficient can range from -1 to +1, with a value of +1 indicating a perfect positive correlation and a value of -1 indicating a perfect negative correlation.
A value of r near 0 indicates that there is no linear relationship between the two variables. A positive value of r indicates that as one variable increases, the other variable also increases. A negative value of r indicates that as one variable increases, the other variable decreases.
To calculate the linear correlation coefficient, you will need to know the mean, standard deviation, and covariance of the two variables. The covariance is a measure of how much the two variables vary together. It is calculated by taking the product of the deviation of each variable from its mean and then averaging these products.
The linear correlation coefficient can be interpreted in terms of the strength and direction of the linear relationship between two variables. The strength of the relationship is indicated by the magnitude of r, with a larger absolute value indicating a stronger relationship. The direction of the relationship is indicated by the sign of r, with a positive sign indicating a positive relation and a negative sign indicating a negative relation.
Assignment brief 7: Find the line of best fit to data pairs, make statistical inferences about the slope of the underlying population equation, and perform basis prediction.
A line of best fit is a straight line that describes how two variables are related. The line of best fit is found by using the least-squares method, which minimizes the sum of the squared residuals (the difference between the actual value and the predicted value).
Once the line of best fit has been determined, you can use it to make predictions about future values. For example, if you know the value of one variable, you can use the line of best fit to predict the value of the other variable.
You can also use the line of best fit to make inferences about the underlying population equation. For example, you can use the slope of the line to estimate the population slope (the average change in the dependent variable for a unit change in the independent variable).
Assignment Brief 8: Understand the ideas behind some survey designs.
Survey designs can be roughly divided into two categories: probability-based and non-probability-based surveys.
Probability-based surveys are created using a random selection process that ensures every unit in the population has an equal chance of being selected. This type of survey is used when the researcher wants to make generalizations about the entire population.
Non-probability-based surveys are not created using a random selection process, meaning that not every unit in the population has an equal chance of being selected. This type of survey is often used when the researcher wants to target specific individuals or groups within the population. Because non-probability-based surveys do not include everyone in the population, they cannot be used to make generalizations about the entire population.
Assignment Brief 9: Learn the basic principles of experimental design and understand when and in what ways a randomized block experimental design is often superior to a completely randomized design.
A randomized block experimental design is often superior to a complete block experimental design because the use of blocks enables the researcher to control for confounding variables.
A confounding variable is a factor that affects the results of an experiment but that is not being studied. For example, in an experiment to test the effects of a new drug, age might be a confounding variable because older people are likely to respond differently to the drug than younger people.
Blocks are groups of experimental units that are similar concerning one or more confounding variables. When blocks are used in an experiment, the researcher can control for the effects of those confounding variables by matching units within each block concerning those variables. This helps ensure that any differences between treatment groups are due to the treatment and not to other factors.
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