Statistics II - Syllabus

The distribution of a statistic obtained from multiple samples of a population. It is a fundamental concept in inferential statistics.

The distribution of the sample mean and proportion, which are used to make inferences about the population mean and proportion.

A fundamental theorem in statistics that states that the sampling distribution of the mean will be approximately normal, even if the population distribution is not normal.

The branch of statistics that deals with making inferences about a population based on a sample of data.

The process of making an educated guess about a population parameter based on a sample of data.

Different techniques used to estimate population parameters, such as maximum likelihood estimation and method of moments.

The characteristics of a good estimator, including unbiasedness, consistency, and efficiency.

The process of determining the required sample size to achieve a desired level of precision in estimation.

The relationship between the sample size and the desired level of error in estimation, including the concept of margin of error.

This topic covers the different types of statistical hypotheses, including null and alternative hypotheses, and their roles in hypothesis testing.

This topic explains the concept of power of the test, p-value, and its use in decision making during hypothesis testing.

This topic outlines the steps involved in testing a hypothesis, from formulating the hypothesis to making a decision based on the test results.

This topic covers one sample tests for the mean of a normal population, including tests for known and unknown variance.

This topic explains how to conduct a test for a single proportion, including the test statistic and p-value calculation.

This topic covers the test for the difference between two means, including the test statistic and p-value calculation.

This topic explains how to conduct a test for the difference between two proportions, including the test statistic and p-value calculation.

This topic covers the paired sample t-test, including its application and interpretation.

This topic explains the relationship between confidence intervals and hypothesis testing, including how to use confidence intervals to make inferences about a population.

The Chi-Square test is a statistical test used to determine whether there is a significant association between two categorical variables. It is used to test the independence of two variables or to test whether the observed frequencies of a categorical variable match the expected frequencies.

Order statistics is a branch of statistics that deals with the arrangement of data in order of magnitude. It is used to describe the distribution of data and to make inferences about the population.

The Run test is a non-parametric test used to determine whether a sequence of data is random or not. It is used to test for randomness in a sequence of binary data.

The Sign test is a non-parametric test used to compare the median of two related samples. It is used to test whether the median of one sample is significantly different from the median of another sample.

The Wilcoxon Matched Pairs Signed Ranks test is a non-parametric test used to compare the median of two related samples. It is used to test whether the median of one sample is significantly different from the median of another sample.

The Mann-Whitney U test is a non-parametric test used to compare the median of two independent samples. It is used to test whether the median of one sample is significantly different from the median of another sample.

The Median test is a non-parametric test used to compare the median of two or more samples. It is used to test whether the median of one sample is significantly different from the median of another sample.

The Kolmogorov Smirnov test is a non-parametric test used to compare the distribution of a sample to a known distribution. It is used to test whether the distribution of a sample is significantly different from a known distribution.

Introduction to multiple correlation, its concept, and application in statistics. Exploring the relationship between multiple variables.

Understanding partial correlation, its concept, and application in statistics. Analyzing the relationship between two variables while controlling for other variables.

Basic concepts and principles of multiple linear regression, including model formulation and estimation. Understanding the relationship between multiple independent variables and a dependent variable.

Testing hypotheses in multiple regression, including significance testing and confidence intervals. Evaluating the overall fit and significance of the regression model.

Testing the overall significance of the regression model, including F-test and p-value interpretation. Determining whether the regression model is a good fit to the data.

Testing the significance of individual regression coefficients, including t-test and p-value interpretation. Evaluating the contribution of each independent variable to the regression model.

Evaluating the goodness of fit and adequacy of the multiple regression model, including residual analysis and diagnostic plots. Identifying potential issues and limitations of the model.

This topic covers the fundamentals of designing databases, including the relational database model, normalization, and transforming E-R diagrams into relations.

This topic focuses on designing forms and reports, including formatting and assessing usability to create effective user interfaces.

This topic explores the design of interfaces and dialogues, including interaction methods and devices, and designing interfaces and dialogues in graphical environments.

Addressing common challenges and considerations that arise during the implementation phase of software development.

Exploring the principles, benefits, and best practices of open-source software development.

Methods for estimating missing values in experimental designs, including CRD and RBD.

Discussion of the benefits and drawbacks of Completely Randomized Design (CRD) and Randomized Block Design (RBD).

A type of experimental design that accounts for two blocking factors to reduce variability.

Methods for analyzing data from a Latin Square Design (LSD) experiment.

Comparison of the efficiency of Latin Square Design (LSD) and Randomized Block Design (RBD).

Discussion of the benefits and drawbacks of Latin Square Design (LSD).

Understanding state management in stateless HTTP protocol and its implications on ASP.NET Core application development.

Exploring server-side strategies for state management in ASP.NET Core applications, including Session State, TempData, and Using HttpContext.

Using Session State to store and manage user data in ASP.NET Core applications.

Using TempData to store and manage temporary data in ASP.NET Core applications.

Using HttpContext to access and manage HTTP request and response data in ASP.NET Core applications.

Using Cache to store and manage frequently accessed data in ASP.NET Core applications.

Exploring client-side strategies for state management in ASP.NET Core applications, including Cookies, Query Strings, and Hidden Fields.