Statistical Aid: A School of Statistics
Learn Statistics and Data Analysis Intuitively!
Statistical tests are used to test hypotheses relating to either the difference between two or more samples/groups, or the relationship between two or more variables. What is a statistical test? Statistical tests assume a null hypothesis. Depending on what you are testing, the null hypothesis is that there is no difference between the sample or … Read more
In this session, we are going to learn how to manipulate, compute, and check the reliability of our variables. Variable manipulation and reliability check is an essential step of data analysis. In this article we will also learn how to re-code the response of our variables and compute to a new variable of our interest. … Read more
We’ll be learning about data cleaning and editing in this session. Data cleaning is a process that checks to see if your variables’ values are valid. We do data coding in order to make data (participants’ responses) more manageable, in a numeric format that statistical software like Stata, SPSS, or Excel can understand, allowing you … Read more
We’re learning how to perform multiple regression analysis using stata this session. Regression is a prominent statistical technique for predicting a single outcome variable (continuous variable) from a set of independent factors (continuous as well as binary variables). For example, you may use gender (binary), family income, age, parental education, and self-efficacy to predict pupils’ … Read more
Data utilization has become a day-to-day business essential in today’s digital world, whether in public or private enterprises. Whatever sector or field you work in, you will always come across data, either indirectly as someone who relies on data-driven decisions or directly as someone who interprets it. In this session, we discuss the introduction part … Read more
Logistic regression is a statistical technique to find the association between the categorical dependent (response) variable and one or more categorical or continuous independent (explanatory) variables. We can define the regression model as, G(probability of event)=β0+β1×1+β2×2+…+βkxk We determine G using the link function as following, Y={1 ; β0+β1×1+ϵ>0 {0; else There are three types of … Read more
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