PART ONE
Excel comes with various statistical tools that are necessary for analysis and can enable in forecasting future values and trends accurately and quickly. Excel forecast function is one of the statistical tools that can be used in predicting future values using historical data. One of the ways through which excel forecast tool can be used is through a worksheet function. The worksheet function can be used as a formula, where it extrapolates the future values and trends of a data. Another way through which the excel forecast can be used is through the use of regression model. Using the data analysis tool, the set of historical data is regressed to give a linear equation which helps in predicting future values. Under regression, the intercept and X-coordinate are used as the bench for predicting the future values. The regression equation returns the predicted value of the depend variable under a specific value of the independent variable.
PART TWO
From the regression computed based on the data provided, there is a positive linear relationship between base and pool. From the regression summary output the slope of the two sets of data is 0.57. As per the R square, it is clear that there is strong positive relationship between the two variables. The message send by this data is that, an increase in the X variable will also have a similar increasing trend in the Y variable. The missing item in the data is a clear distinction between the X and Y variables. The standard unit of measure for the data might also be missing.
Line of best fit – the main function of the line of best fit is to express relationship between data points. Line of best fit is important because it shows how the data scatter plots are related and the trend they display.
R2 value – the function of this value is to measure the percentage of variance in the dependent variable as explained by the independent variable collectively. Its importance is to explain how the regression model fits the set of data.
Slope – the function of the slope is to measure the rate of change of the dependent variable when the independent variable changes. The slope is important because it tells the expected rate of change between the dependent and independent variable.