The field of statistics offers techniques for analyzing datasets in order to fully explain a dataset (descriptive statistics) or to make generalizations about a wider population (logical statistics).
What are Statistics?
Researchers utilize a set of techniques called statistics to gather, examine, and draw conclusions from data. There are many different approaches to data analysis, which are often divided into descriptive and inference statistics.
You may learn about the characteristics of the full data collection, such as mean and dispersion, and how data points relate to one another using descriptive statistics. You may draw conclusions about a bigger population from a smaller sample by using the numbers presented.
Many uses in financial analysis and investment may be made using statistical approaches since they are effective at assessing, estimating, and summarizing enormous volumes of data. You may assess the performance of individual stocks using statistical measurements like standard deviation, R-square, and Sharpe Ratio.
Why do investors need statistics?
An investor can use statistics to conduct research and analysis of the stock market and determine how to improve the performance of the investment portfolio. For example, you wanted to calculate the average return of an investment portfolio with a combination of assets. Using weighted average statistics, you can account for how much you invest in each type of asset in the portfolio.
The formula for calculating the weighted average yield is as follows:
Weighted average = (R1 * W1) + (R2* W2) + … + (Rn* Wn)
R represents the return for a certain class of assets;
W represents the percentage (or weight) of that particular asset in the investment portfolio.
Types of statistics
Inferential statistics and descriptive statistics are the two primary categories of statistics. There are extra categories for each broad category of statistic.
1. Summary statistics known as descriptive statistics are those that quantify or list qualities from a collection of data.
Among its measures are the following:
- Normative deviation
2. Using inferential statistics,you may examine a sizable group using the data presented without having to take into account each individual topic or group member.
For instance, a firm that sells doughnuts would be interested in learning about Americans’ preferred flavors so that it can improve the taste of its own doughnuts. However, polling more than all Americans is difficult to implement, and economically impractical. Instead, the company can use statistical sampling methods to create a more manageable sample of 700 people who are representative of the entire US population and then apply a number of statistical studies. For example, you can try to test the null hypothesis, which is that there is no significant relationship between two variables (for example, age and preference for caramel doughnut filling.
Measures of inferential statistics are:
- Confidence interval
- Hypothesis theory
Thus, statistics and statistical analysis are important in all areas of life. Do not underestimate the role of statistics in the investment sector.