Note the value of R-squared on the graph. Your graph should now look like Figure 6. Right-click on the trendline, choose the Display R-Sqaured value on the chart in the Format Trendlines dialogue box. Return to Top Using the R-squared Coefficient Calculation to Estimate Fit Note that if you highlight your new equation in C13, the reference to cell B12 has also incremented to cell B13. Highlight the little black cross by passing over the lower lefthand corner of cell C12 and drag downwards to cell C13.Note: If your equation on the graph differs for the one in this example, use your equationĭuplicate your equation for the other unknown. Click in the cell representing 'y' in your equation (cell B12 in Figure 5) to put this cell label in your equation.Type an equal sign and then a parentheses.Click in the equation area (formula bar, figure 5).Highlight a spreadsheet cell to hold 'x', the result of the final equation (cell C12 in Figure 5).The solution for x (Concentration) is then displayed in cell 'C12'. 'B12' in the equation represents y (the absorbance of the unknown). The equation associated with the spreadsheet cell will look like what is in the Formula Cell. Now we have to convert this final equation into an equation in a spreadsheet cell. Below are the algebraic equations working out this calculation: We have a value for y (Absorbance) and need to solve for x (Concentration). Using the linear equation (Equation on Chart in Figure 5), a spreadsheet cell can have an equation associated with it to do the calculation for us. We have been given the absorbance readings for two solutions of unknown concentration. The regression line can be considered an acceptable estimation of the true relationship between concentration and absorbance. The linear equation shown on the chart represents the relationship between Concentration (x) and Absorbance (y) for the compound in solution. Return to Top Using the Regression Equation to Calculate Concentrations The chart now displays the regression line (Figure 4) The linear trendline should automatically be selected - see below:Ĭhoose Display equation on chart option as well (Figure 2):Ĭlick OK to close the dialogue. You can add a regression line to the chart by right-clicking on a data point, and choose Add Trendline.Ī dialogue box appears (Figure 2). Return to Top Creating a Linear Regression Line (Trendline) This module will start with the same scatter plot created in the basic graphing module. Traditionally, this would be a scatter plot. Creating an Initial Scatter Plotīefore you can create a regression line, a graph must be produced from the data. This is done by fitting a linear regression line to the collected data. This fact can be used to calculate the concentration of unknown solutions, given their absorption readings. Beer's Law states that there is a linear relationship between concentration of a colored compound in solution and the light absorption of the solution. The data below was first introduced in the basic graphing module and is from a chemistry lab investigating light absorption by solutions. This too can be calculated and displayed in the graph. The closer R^2 is to 1.00, the better the fit. How well this equation describes the data (the 'fit'), is expressed as a correlation coefficient, R^2 (R-squared). This equation can either be seen in a dialogue box and/or shown on your graph. In addition to visually depicting the trend in the data with a regression line, you can also calculate the equation of the regression line. That is, the theory underlying your lab should indicate whether the relationship of the independent and dependent variables should be linear or non-linear. It is important that you are able to defend your use of either a straight or curved regression line. A curved line represents a trend described by a higher order equation (e.g., y = 2x^2 + 5x - 8). There are no squared or cubed variables in this equation). A straight line depicts a linear trend in the data (i.e., the equation describing the line is of first order. Regression lines can be used as a way of visually depicting the relationship between the independent (x) and dependent (y) variables in the graph. Using the R-squared coefficient calculation to estimate fit.Using the regression equation to calculate slope and intercept.Creating a linear regression line (trendline).Untitled Document Linear Regression in Excel-2007 Table of Contents
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