1. P-value:
The P-value is a statistical measure that quantifies the probability of obtaining an observed result or one more extreme, assuming that the null hypothesis is true. It is a critical component in hypothesis testing, where a low P-value indicates strong evidence against the null hypothesis, suggesting that the observed effect is likely not due to random chance.
On the other hand, a high P-value indicates weak evidence against the null hypothesis, implying that the observed result could be attributed to random variation in the data.
2. Linear Regression:
Linear Regression is a statistical technique used to model the relationship between a dependent variable and one or more independent variables. It assumes a linear relationship between the variables, where the goal is to find the best-fitting straight line that minimizes the distance between the observed data points and the predicted values.
The model calculates coefficients for the intercept and slopes of the independent variables, enabling predictions and understanding the impact of the independent variables on the dependent variable.
3. T-test:
The T-test is a statistical hypothesis test used to compare the means of two groups and determine if there is a significant difference between them. It is applicable to situations where the sample size is relatively small, and the data follows a normal distribution.
The t-test produces a t-statistic, which measures the difference between the means in terms of the standard error, and it also provides a P-value to assess the significance of the observed difference.
4. Correlation Coefficient:
The Correlation Coefficient, often denoted as "r," is a statistical measure that quantifies the strength and direction of the linear relationship between two continuous variables. It ranges from -1 to 1, where -1 indicates a perfect negative correlation (as one variable increases, the other decreases), 1 indicates a perfect positive correlation (both variables increase together), and 0 indicates no linear correlation between the variables.
5. Types of Errors:
In the context of hypothesis testing, there are two types of errors:
- Type I Error (False Positive): It occurs when we reject a true null hypothesis. In other words, it is the probability of incorrectly concluding that there is a significant effect when, in fact, there is none. The probability of Type I error is denoted by the significance level (α) and is controlled by the researcher.
- Type II Error (False Negative): It occurs when we fail to reject a false null hypothesis. In other words, it is the probability of incorrectly concluding that there is no significant effect when, in reality, there is one. The probability of Type II error is denoted by beta (β) and is inversely related to the statistical power of the test. Increasing the sample size c
