Conduct your answer in excel. A recent article in Bloomberg Businessweek listed the "Best Small Compa-nies." We are interested in the current results of the companies' sales and earnings.A random sample of 12 companies was selected and the sales and earnings, inmillions of dollars, are reported below.SalesEarnings($ millions) ($ millions) CompanyEarnings($ millions) ($ millions)SalesCompanyPapa John's InternationalApplied InnovationIntegracare$89.2$4.9Checkmate Electronics$17.5$ 2.618.64.4Royal Grip11.91.718.21.3M-Wave19.63.5Wall Data71.78.0Serving-N-SlideDaigCobra Golf51.28.2Davidson & Associates58.66.628.66.0Chico's FAS46.84.169.212.8a. Draw a scatter diagram.b. Compute the correlation coefficient.c. Determine the regression equation.d. For a small company with $50.0 million in sales, estimate the earnings.

Given Information : A recent article in Bloomberg Business week listed "Best small companies" . We are interested in the current results of the companies' sales and earnings. A random sample of 12 companies was selected and the sales and earnings , in million dollars . To draw scatter plot : Software procedure - 1. Select the range of data . 2. On the Insert tab, in the Charts group, click the Scatter symbol. 3. Click Scatter. Result: Points on scatter diagram are scattered and moderately shows a linear trend . To Compute Correlation Coefficient : Software procedure (Using Data Analysis Toolpak ) : click Data > Data Analysis, and in the Data Analysis dialog, select Correlation, then click OK. 1) Select the data range; 2) Check Columns or Rows option based on your data; 3) Check Labels in first row if you have labels in the data; 4) Check one option as you need in Output options section . 5) Click OK. And the analysis result has been displayed in the range you specified. Result : Sales Earnings Sales 1 Earnings 0.54045 1 Since , correlation is + 0.5 . Data shows a moderate positive relationship between sales and earnings . To find Regression equation : Software Procedure : 1. On the Data tab, in the Analysis group, click Data Analysis. Note: can't find the Data Analysis button? Click here to load the Analysis ToolPak add-in. 2. Select Regression and click OK. 3. Select the Y Range . This is the predictor variable (also called dependent variable). 4. Select the X Range. These are the explanatory variables (also called independent variables). These columns must be adjacent to each other. 5. Check Labels. 6. Click in the Output Range box and select any empty cell . 8. Click OK. Output : SUMMARY OUTPUT Regression Statistics Multiple R 0.540450041 R Square 0.292086247 Adjusted R Square 0.221294872 Standard Error 2.865736983 Observations 12 ANOVA df SS MS F Significance F Regression 1 33.88468 33.88468 4.126015 0.069658 Residual 10 82.12448 8.212448 Total 11 116.0092 Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0% Intercept 2.744442661 1.522911 1.802103 0.101703 -0.64882 6.137701 -0.64882 6.137701 Sales 0.06970854 0.034318 2.031259 0.069658 -0.00676 0.146174 -0.00676 0.146174 Regression Equation : Y = 2.714 + 0.0697 sales For a small company, x = $ 50 million sales . Substituting value of x in the above regression equation : Y = 2.714 + 0.0697 (50) Y = 6.199 Estimated earnings = $ 6.199 million