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Use the x column for Series X Values and the y + CI column for Series Y Values. Next, enter a Series name for the Upper 95 Confidence Intervals.Then, click on the Add button as shown below.First, right-click on the Chart Area to go to Select Data.Now we will plot the y values for the 95% confidence intervals to interpret them graphically. So, we can be 95% confident that y values from any sample size will fall within this range. Here the Upper 95% and the Lower 95% Confidence Intervals are 9.16 and 8.25 respectively. Interpreting Confidence Intervals in Linear Regression Here the STEYX function returns the standard error of the predicted y-value for each x in a regression. Then enter the following formula in cell F8 to calculate the Standard Error.Now enter the values of Slope (m), Intercept (b), and Observations (n) in cells F5:F7 You can use the COUNT function to get the sample count or observations to be 9.You can also use the SLOPE and INTERCEPTfunctions respectively to calculate these results. Comparing it to the linear regression equation yields m = -0.0532 and b = 8.704. After that, y = -0.0532x + 8.704 will appear on the chart.Next, check the Display Equation on chart checkbox.Then go to More Options or double-click on the Trendline. Now click on the chart Element icon in the upper-right corner and enable linear Trendline.After that, the following chart will be created.Then go to Insert > Insert Scatter (X, Y) or Bubble Chart > Scatter as shown below. First, select the entire dataset ( B4:C13).But in this article, we will use Scatter Charts so that it is easier for you to understand. There are several ways to perform Linear Regression in Excel. Now you want to calculate the linear regression confidence interval for this dataset.
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Suppose you have the following example dataset where the y variable is dependent on x. How to Calculate Linear Regression Confidence Interval in Excel Performing a linear regression analysis with a 95% confidence level suggests that there is a 95% probability that the predicted outcomes may fall within the confidence interval. SS x = sum of squares of deviations of data points from sample mean.