Question

1. Explain the difference between a variable and a statistic in the context of a regression equation

2. Define the terms "causal effect" and "ideal experiment". Explain the difference between descriptive statistics, inferential statistics, and causal inference.

Answer 1

A regression analysis is used to measure how a dependent variable and other independent variables are related to each other. It is used in analysis of finance and investments to estimate the strength of the variables and these variables are termed as regression variables. Statistical processes are involved in a regression analysis. When we conduct a regression analysis inorder to find out whether the means between two populations are not same, then the value calculated from it is known as F statistic.

A casual effect is something had happened relating independent and dependent variables as a result of an action that had occured before.

If an experiment is conducted at true situation, then such an experiment is said to be an ideal experiment.

Statistics is the science of collecting, analyzing and interpreting data.

Descriptiive statistics :

• Here, datas are organized and summarized using number and graphs.
• We can summarize our data using a bar graph, histograms, pie charts etc.
• Measures of central tendency which includes mean, median and mode.
• Measures of variability such as range, variance and standard deviation.

Inferential statistics :

• Here sample data is used to make an inference or draw a conclusion of the population.
• It uses probability to determine how confident we can be that the conclusions we make are correct.

Causal inference :

• it is making conclusions on a causal connection by evaluating the conditions at which an effect had occured.
• It is highly important because using it we can analyze the reason behind an effect. Thus we can change our behavior to better outcomes thereafter.

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