#PREDICTED VALUE REGRESSION EXCEL HOW TO#
You do not need to learn how to find the slope and intercept it manually. Don’t worry, excel has functions for them. Now you must be wondering where the stat will you get the slope and intercept. The predicted y (number of salesmen) also called Regression Equation, would be x* Slope+ Intercept (relax, I’ve got it covered) We need to predict the required number of salesmen for all 11 cases to get the 12th closest prediction. The “Simple” Math of Linear Regression Equation: If we want to know how many soft drinks will be sold if we appoint 200 salesmen, then the scenario will be vice-versa. In our case, soft drinks are sold x and the number of salesmen is y. The independent variable is mostly denoted as x and dependent variable as y. So here, the dependent variable is the number of salesmen and the independent variable is sold soft drinks. You want to know the number of salesmen required to achieve 2000 sales. Now let’s compare this with your scenario. And the independent variable is often pronounced as a Driving, Predictor or simply Independent variable. The dependent variable is often called by names such as Driven, Response, and Target variable. Where one variable depends on the other independent variable.
The study of the relationship between two variables is called Simple Linear Regression. This part will make you understand regression better than just telling excel regression procedure. REGRESSION ANALYSIS USING EXCEL FUNCTIONS (MANUAL REGRESSION FINDING) To do so, we need to know Regression first. The regression equation is a tool to make such close estimates. Now based on this data you want to predict the number of salesmen required to achieve 2000 sales of soft drinks. You have 11 records of salesmen and soft drinks sold.
Want to predict the future? No, we are not going to learn astrology. You can also create a scatter plot of these residuals.Regression is an Analysis Tool, which we use for analyzing large amounts of data and making forecasts and predictions in Microsoft Excel.
For example, the first data point equals 8500. The residuals show you how far away the actual data points are fom the predicted data points (using the equation). For example, if price equals $4 and Advertising equals $3000, you might be able to achieve a Quantity Sold of 8536.214 -835.722 * 4 + 0.592 * 3000 = 6970. You can also use these coefficients to do a forecast. For each unit increase in Advertising, Quantity Sold increases with 0.592 units. In other words, for each unit increase in price, Quantity Sold decreases with 835.722 units. The regression line is: y = Quantity Sold = 8536.214 -835.722 * Price + 0.592 * Advertising. Most or all P-values should be below below 0.05.
Delete a variable with a high P-value (greater than 0.05) and rerun the regression until Significance F drops below 0.05. If Significance F is greater than 0.05, it's probably better to stop using this set of independent variables. If this value is less than 0.05, you're OK. To check if your results are reliable (statistically significant), look at Significance F ( 0.001).
0 kommentar(er)
