Making predictions and reliability of predicted values

Understanding Predictions

Predictions are often based on historical data patterns. These patterns provide a model or trend that can be used to estimate future outcomes.
The process of making predictions involves extrapolation or interpolation. Extrapolation is predicting beyond the data range, while interpolation is predicting within the data range.

Regression lines or lines of best fit are popular tools used to predict outcomes. The line of best fit is the line that minimises the sum of the squares of the residuals (differences between observed and predicted values).
Mean and median values are also used to make predictions. The median is less affected by extreme values or outliers than the mean, so it is often a more reliable predictor.

Predictions are estimations and don’t guarantee an exact future outcome. They should be interpreted as likely results based on current trends.
The confidence level specifies the probability that the prediction is within a certain range. A higher confidence level means greater uncertainty, but also greater assurance that the actual result lies within the predicted range.

Reliability refers to the accuracy and consistency of predicted values. A highly reliable prediction will be on target and consistent when repeated.
Reliability can be measured in several ways, including correlation coefficients and root-mean-square error (RMSE). A high absolute value of the correlation coefficient (close to 1) indicates a strong relationship, while a low RMSE indicates high reliability.

The reliability of predictions can be influenced by various factors, including the size of the dataset, the variability of the data, and the assumptions made in the prediction model.
Outliers can significantly affect the reliability of predicted outcomes. They may cause overfitting of a model, skewing of mean values, or inflation of the predicted range.
Assumptions about data normality, homoscedasticity, or independence can impact the robustness of correlation coefficients, regression lines, and other prediction tools. If these assumptions are violated, they can decrease the reliability of predictions.

Improving the reliability of predictions often involves increasing the sample size, utilising more accurate measuring tools, or improving the estimation model.
The more data available, the more reliable the predictions. Larger datasets can smooth out random variations and reduce the likelihood of predictions skewed by outliers.
Cross-validation or bootstrap methods can also be used to assess and improve the reliability of predictions. These techniques involve resampling the data and recalculating the prediction model to test its stability and accuracy.